servqual for PGPSE participants

SERVQUAL FOR PGPSE PARTICIPANTS



LIST OF ABBREVIATIONS



AIAG -Automotive Industry Action Group

ANSI -American National Standards Institute

AQL -Acceptable quality level

AS9100 - Quality system requirements for suppliers to the aerospace industry (previously known as

AS9000).

ASQ - American Society for Quality

ASTM - American Society for Testing and Materials

BS -British Standard

BSI - British Standards Institution

CAI - Computer aided inspection

CASCO - ISO Committee on Conformity Assessments

CC -Critical characteristic

CE Mark -European Union product safety certification symbol:

CEN -European Committee for Standardization

CENELEC -European Committee for Electro-technical Standardization

CIM - Computer Integrated Manufacturing

CQA -Certified Quality Auditor

CQE - Certified Quality Engineer

CQMgr -Certified quality manager

CRE- Certified Reliability Engineer

CSE – certificate in social / spiritual entrepreneurship – similar to PGPSE – read details

DFA- Design for assembly

DFM - Design for manufacturing

DFMEA -Design Failure Mode and Effects Analysis

DIN - Germany Standards Institute

DOE- Design of Experiments

EC - European Community

EFTA - European Free Trade Association

EN - European Standard

EQS - European Committee for Quality System Assessment and Certification

Establish - Define, document (in writing or electronically), and implement. [4]

ETSI - European Telecommunications Standards Institute

FTA - Fault Tree Analysis

GD&T -Geometric Dimensioning and Tolerance

GMP - Good Manufacturing Practice

GR&R -Gage Repeatability and Reproducibility

IEC -International Electro-technical Commission

IEEE- Institute of Electrical and Electronic Engineers

ISO- International Organization for Standards



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ISO14000-International environmental management system standard administered by ISO

ISO 9000 -International Standard for Quality Systems

LCL- Lower control limit

LSL- Lower specification limit

MBNQA- Malcolm Baldrige National Quality Award

MRB- Material review board

MSA - Measurement System Analysis

MTBF- Mean time between failures

NACCB - National Accreditation Council for Certification Bodies (UK)

NDT- Nondestructive testing

NIST - National Institute of Science and Technology

PGPSE – Post graduate programme on social / spiritual entrepreneurship- started by centre for social

entrepreneurship, afterschool career guidance trust bikaner – www.afterschool.tk – based on self

certification by the participants- they have to study about 30 books, prepare 4 projects, give 2

presentations, write 2 books / papers and prepare at least 4 business plans and undertake mentoring of 5

persons and get guidance to earn this. Similar to CSE

PFMEA- Process Failure Mode and Effects Analysis

QFD- Quality Function Deployment (see QFD FAQ)

QMS- Quality Management System (see Quality system)

QS-9000- Quality system requirements for suppliers to Daimler Chrysler, Ford and General Motors

QSR- Quality System Requirements

RAB - Registrar Accreditation Board

SAE - Society of Automotive Engineers

SCC- Standards Council of Canada

SMWT- Self-managed work teams

SPC - Statistical Process Control

SQC - Statistical Quality Control

TGA - Germany Association for Accreditation

TL 9000- Quality system requirements for suppliers to the telecommunications industry

TPM- Total productive maintenance

TQM -Total quality management

UCL - Upper control limit

USL - Upper specification limit



INTRODUCTION



Servqual refers to service quality management. It is a part of the concept of TQM and quality

initiatives, with special reference to service industries. Social entrepreneurship also refers to starting

business enterprises for the benefits of society, therefore entrepreneurs must be familiar with the

concept of quality management and TQM.









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CHAPTER I



BASIC CONCEPTS OF QUALITY







In the manufacturing industry it is commonly stated that ―Quality drives productivity.‖ Improved

productivity is a source of greater revenues, employment opportunities and technological advances.

However, this has not been the case historically, and in the early 19th century it was recognized that some

markets, such as those in Asia, preferred cheaper products to those of quality. Most discussions of quality

refer to a finished part, wherever it is in the process. Inspection, which is what, quality insurance usually

means, is historical, since the work is done. The best way to think about quality is in process control. If

the process is under control, inspection is not necessary.



However, there is one characteristic of modern quality that is universal. In the past, when we tried

to improve quality, typically defined as producing fewer defective parts, we did so at the expense of

increased cost, increased task time, longer cycle time, etc. We could not get fewer defective parts and

lower cost and shorter cycle times, and so on. However, when modern quality techniques are applied

correctly to business, engineering, manufacturing or assembly processes, all aspects of quality - customer

satisfaction and fewer defects/errors and cycle time and task time/productivity and total cost, etc. - must

all improve or, if one of these aspects does not improve, it must at least stay stable and not decline. So,

modern quality has the characteristics that it creates and-based benefits, not OR-based benefits.



The most progressive view of quality is that it is defined entirely by the customer or end user and

is based upon that person's evaluation of his or her entire customer experience. The customer experience

is the aggregate of all the touch points that customers have with the company's product and services, and

is by definition a combination of these. For example, any time one buys a product one forms an

impression based on how it was sold, how it was delivered, how it performed, how well it was supported

etc.



CONCEPT OF QUALITY - HISTORICAL BACKGROUND



The concept of quality as we think of it now first emerged out of the Industrial Revolution.

Previously goods had been made from start to finish by the same person or team of people, with

handcrafting and tweaking the product to meet 'quality criteria'. Mass production brought huge teams of

people together to work on specific stages of production where one person would not necessarily

complete a product from start to finish. In the late 1800s pioneers such as Frederick Winslow Taylor and

Henry Ford recognized the limitations of the methods being used in mass production at the time and the

subsequent varying quality of output. Taylor established Quality Departments to oversee the quality of

production and rectifying of errors, and Ford emphasized standardization of design and component

standards to ensure a standard product was produced. Management of quality was the responsibility of

the Quality department and was implemented by Inspection of product output to 'catch' defects.

Application of statistical control came later as a result of World War production methods. Quality

management systems are the outgrowth of work done by W. Edwards Deming, a statistician, after whom

the Deming Prize for quality is named.





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Quality, as a profession and the managerial process associated with the quality function, was

introduced during the second-half of the 20th century, and has evolved since then. Over this period, few

other disciplines have seen as many changes as the quality profession. The quality profession grew from

simple control, to engineering, to systems engineering. Quality control activities were predominant in the

1940s, 1950s, and 1960s. The 1970s were an era of quality engineering and the 1990s saw quality systems

as an emerging field. Like medicine, accounting, and engineering, quality has achieved status as a

recognized profession. Quality management system is mandatory for shipping companies and on board

sea going vessels, known as ISM CODE -International Safety Management Code. It is a mandatory

requirement under SOLAS convention (Safety of Life At Sea). It is implemented through the flag state of

the vessel as mandatory document for the vessel to sail. ISM in shipping has two ways implementation.



1. Quality management system for the company for the Safety and environment protection

implemented through DOC(Document of compliance) ; and

2. ISM implementation for Systems and procedures on board vessel through SMC (Safety

Management Code).



THE EVOLUTION OF QUALITY

Before the concepts and ideas of TQM were formalized, much work had taken place over the

centuries to reach this stage. This section charts the evolution, from inspection through to the present day

concepts of total quality.



FROM INSPECTION TO TOTAL QUALITY



During the early days of manufacturing, an operative‘s work was inspected and a decision made

whether to accept or reject it. As businesses became larger, so too did this role and full time inspection

jobs were created. Accompanying the creation of inspection functions, other problems arose:

• More technical problems occurred, requiring specialized skills, often not possessed by

production workers

• The inspectors lacked training

• Inspectors were ordered to accept defective goods, to increase output

• Skilled workers were promoted into other roles, leaving less skilled workers to perform the

operational jobs, such as manufacturing.



These changes led to the birth of the separate inspection department with a ―chief inspector‖,

reporting to either the person in charge of manufacturing or the works manager. With the creation of this

new department, there came new services and issues, e.g., standards, training, recording of data and the

accuracy of measuring equipment. It became clear that the responsibilities of the ―chief inspector‖ were

more than just product acceptance, and a need to address defect prevention emerged.

Hence the quality control department evolved, in charge of which was a ―quality control manager‖, with

responsibility for the inspection services and quality control engineering.



In the 1920‘s statistical theory began to be applied effectively to quality control, and in 1924

Shewhart made the first sketch of a modern control chart. His work was later developed by Deming and

the early work of Shewhart, Deming, Dodge and Romig constitutes much of what today comprises the

theory of statistical process control (SPC). However, there was little use of these techniques in





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manufacturing companies until the late 1940‘s. At that time, Japan‘s industrial system was virtually

destroyed, and it had a reputation for cheap imitation products and an illiterate workforce. The Japanese

recognized these problems and set about solving them with the help of some notable quality gurus –

Juran, Deming and Feigenbaum. In the early 1950‘s, quality management practices developed rapidly in

Japanese plants, and become a major theme in Japanese management philosophy, such that, by 1960,

quality control and management had become a national preoccupation. By the late 1960‘s/early 1970‘s

Japan‘s imports into the USA and Europe increased significantly, due to its cheaper, higher quality

products, compared to the Western counterparts.









In 1969 the first international conference on quality control, sponsored by Japan, America and

Europe, was held in Tokyo. In a paper given by Feigenbaum, the term ―total quality‖ was used for the first

time, and referred to wider issues such as planning, organisation and management responsibility. Ishikawa

gave a paper explaining how ―total quality control‖ in Japan was different, it meaning ―company wide

quality control‖, and describing how all employees, from top management to the workers, must study and

participate in quality control. Company wide quality management was common in Japanese companies by

the late 1970‘s. The quality revolution in the West was slow to follow, and did not begin until the early

1980‘s, when companies introduced their own quality programmes and initiatives to counter the Japanese

success. Total quality management (TQM) became the centre of these drives in most cases.



In a Department of Trade & Industry publication in 1982 it was stated that Britain‘s world trade

share was declining and this was having a dramatic effect on the standard of living in the country. There

was intense global competition and any country‘s economic performance and reputation for quality was

made up of the reputations and performances of its individual companies and products/services. The

British Standard (BS) 5750 for quality systems had been published in 1979, and in 1983 the National

Quality Campaign was launched, using BS5750 as its main theme. The aim was to bring to the attention of

industry the importance of quality for competitiveness and survival in the world market place. Since then



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the International Standardization Organisation (ISO) 9000 has become the internationally recognized

standard for quality management systems. It comprises a number of standards that specify the

requirements for the documentation, implementation and maintenance of a quality system. TQM is now

part of a much wider concept that addresses overall organizational performance and recognizes the

importance of processes. There is also extensive research evidence that demonstrates the benefits from

the approach. As we move into the 21st century, TQM has developed in many countries into holistic

frameworks, aimed at helping organizations achieve excellent performance, particularly in customer and

business results. In Europe, a widely adopted framework is the so-called ―Business Excellence‖ or

―Excellence‖ Model, promoted by the European Foundation for Quality Management (EFQM), and in

the UK by the British Quality Foundation (BQF).



MEANING OF QUALITY

In the vernacular, quality can mean a high degree of excellence (―a quality product‖), a degree of

excellence or the lack of it (―work of average quality‖), or a property of something (―the addictive quality

of alcohol‖). Distinct from the vernacular, the subject of this article is the business interpretation of

quality.



IMPROVEMENT OF QUALITY

Many techniques and concepts, often overlapping, have evolved to improve product or service

quality, including:



 statistical process control (SPC)

 Zero Defects

 Six Sigma

 Malcolm Baldrige National Quality Award

 quality circles

 requirements analysis

 total quality management (TQM)

 theory of constraints (TOC)

 quality management systems

 business process management (BPM)

 capability maturity models

 verification and validation

 business process reengineering

 life cycle management

 standardization (ISO 9000 and others)

 Continuous improvement.



KEY TO QUALITY

The key to improving quality is to improve processes that define, produce and support our products.

All people work in processes.

People

 Get processes "in control"

 Work with other employees and managers to identify process problems and eliminate them



Managers and/or Supervisors Work on Processes





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 Provide training and tool resources

 Measure and review process performance (metrics)

 Improve process performance with the help of those who use the process



THE QUALITY GURUS



A guru, by definition, is a good person, a wise person and a teacher. A quality guru should be all of

these, plus have a concept and approach to quality within business that has made a major and lasting

impact. The gurus mentioned in this section have done, and continue to do, that, in some cases, even after

their death.



There have been three groups of gurus since the 1940‘s:



Early 1950‘s



Americans who took the messages of quality to Japan



Late 1950‘s



Japanese who developed new concepts in response to the Americans



1970‘s-1980‘s Western gurus who followed the Japanese industrial success. It is beyond the scope of this

site to go into great detail on each of the gurus, their philosophies, teachings and tools; however, a brief

overview of their contribution to the quality journey is given, supported by several references.



THE AMERICANS WHO WENT TO JAPAN

W Edwards Deming placed great importance and responsibility on management, at the individual

and company level, believing management to be responsible for 94% of quality problems. His fourteen

point plan is a complete philosophy of management that can be applied to small or large organizations in

the public, private or service sectors:



 Create constancy of purpose towards improvement of product and service Adopt the new

philosophy. We can no longer live with commonly accepted levels of delay, mistakes and

defective workmanship

 Cease dependence on mass inspection. Instead, require statistical evidence that quality is built

in

 End the practice of awarding business on the basis of price

 Find problems. It is management‘s job to work continually on the system

 Institute modern methods of training on the job

 Institute modern methods of supervision of production workers, the responsibility of

foremen must

 be changed from numbers to quality

 Drive out fear, so that everyone may work effectively for the company

 Break down barriers between departments

 Eliminate numerical goals, posters and slogans for the workforce asking for new levels of

Productivity without providing methods



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 Eliminate work standards that prescribe numerical quotas

 Remove barriers that stand between the hourly worker and their right to pride of

workmanship

 Institute a vigorous programme of education and retraining

 Create a structure in top management that will push on the above points every day



He believed that adoption of, and action on, the fourteen points was a signal that management

intended to stay in business. Deming also encouraged a systematic approach to problem solving and

promoted the widely known Plan, Do, Check, Act (PDCA) cycle. The PDCA cycle is also known as the

Deming cycle, although it was developed by a colleague of Deming, Dr Shewhart.









It is a universal improvement methodology, the idea being to constantly improve, and thereby

reduce the difference between the requirements of the customers and the performance of the process.

The cycle is about learning and ongoing improvement, learning what works and what does not in a

Systematic way; and the cycle repeats; after one cycle is complete, another is started. Dr Joseph M Juran

developed the quality trilogy – quality planning, quality control and quality improvement. Good quality

management requires quality actions to be planned out, improved and controlled. The process achieves

control at one level of quality performance, and then plans are made to improve the performance on a

project by project basis, using tools and techniques such as Pareto analysis. This activity eventually

achieves breakthrough to an improved level, which is again controlled, to prevent any deterioration.









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Juran believed quality is associated with customer satisfaction and dissatisfaction with the product,

and emphasized the necessity for ongoing quality improvement through a succession of small

improvement projects carried out throughout the organization. His ten steps to quality improvement are:

 Build awareness of the need and opportunity for improvement

 Set goals for improvement

 Organize to reach the goals

 Provide training

 Carry out projects to solve problems

 Report progress

 Give recognition

 Communicate results

 Keep score of improvements achieved

 Maintain momentum



He concentrated not just on the end customer, but on other external and internal customers. Each

person along the chain, from product designer to final user, is a supplier and a customer. In addition, the

person will be a process, carrying out some transformation or activity.









Armand V Feigenbaum was the originator of ―total quality control‖, often referred to as total quality.





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He defined it as:

―An effective system for integrating quality development, quality maintenance and quality

improvement efforts of the various groups within an organisation, so as to enable production and service

at the most economical levels that allow full customer satisfaction‖. He saw it as a business method and

proposed three steps to quality:



 Quality leadership

 Modern quality technology

 Organizational commitment



The Japanese:



Dr Kaoru Ishikawa made many contributions to quality, the most noteworthy being his total

quality viewpoint, company wide quality control, his emphasis on the human side of quality, the Ishikawa

diagram and the assembly and use of the ―seven basic tools of quality‖:

 Pareto analysis- which are the big problems?

 Cause and effect diagrams- what causes the problems?

 Stratification- how is the data made up?

 Check sheets- how often it occurs or is done?

 Histograms- what do overall variations look like?

 Scatter charts- what are the relationships between factors?

 Process control charts- which variations to control and how?



He believed these seven tools should be known widely, if not by everyone, in an organization and

used to analyze problems and develop improvements. Used together they form a powerful kit. One of the

most widely known of these is the Ishikawa (or fishbone or cause and effect) diagram.









Like other tools, it assists groups in quality improvements. The diagram systematically represents

and analyses the real causes behind a problem or effect. It organizes the major and minor contributing

causes leading to one effect (or problem), defines the problem, identifies possible and probable causes by

narrowing down the possible ones. It also helps groups to be systematic in the generation of ideas and to

check that it has stated the direction of causation correctly. The diagrammatic format helps when

presenting results to others.







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Dr Genichi Taguchi believed it is preferable to design product that is robust or insensitive to

variation in the manufacturing process, rather than attempt to control all the many variations during actual

manufacture. To put this idea into practice, he took the already established knowledge on experimental

design and made it more usable and practical for quality professionals. His message was concerned with

the routine optimization of product and process prior to manufacture rather than quality through

inspection. Quality and reliability are pushed back to the design stage where they really belong, and he

broke down off-line quality into three stages:

 System design

 Parameter design

 Tolerance design









―Taguchi methodology‖ is fundamentally a prototyping method that enables the designer to

identify the optimal settings to produce a robust product that can survive manufacturing time after time,

piece after piece, and provide what the customer wants. Today, companies see a close link between

Taguchi methods, which can be viewed along a continuum, and quality function deployment (QFD).

Shigeo Shingo is strongly associated with Just-in-Time manufacturing, and was the inventor of the single

minute exchange of die (SMED) system, in which set up times are reduced from hours to minutes, and

the Poka-Yoke (mistake proofing) system. In Poka Yoke, defects are examined, the production system

stopped and immediate feedback given so that the root causes of the problem may be identified and

prevented from occurring again. The addition of a checklist recognizes that humans can forget or make

mistakes!



He distinguished between ―errors‖, which are inevitable, and ―defects‖, which result when an error

reaches a customer, and the aim of Poka-Yoke is to stop errors becoming defects. Defects arise because

errors are made and there is a cause and effect relationship between the two. Zero quality control is the

ideal production system and this requires both Poka-Yoke and source inspections. In the latter, errors are

looked at before they become defects, and the system is either stopped for correction or the error

condition automatically adjusted to prevent it from becoming a defect.



Western Gurus:

Philip B Crosby is known for the concepts of ―Quality is Free‖ and ―Zero Defects‖, and his

quality improvement process is based on his four absolutes of quality:

 Quality is conformance to requirements





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 The system of quality is prevention

 The performance standard is zero defect

 The measurement of quality is the price of non-conformance



His fourteen steps to quality improvement are:

 Management is committed to a formalized quality policy

 Form a management level quality improvement team (QIT) with responsibility for quality

improvement process planning and administration

 Determine where current and potential quality problems lie

 Evaluate the cost of quality and explain its use as a management tool to measure waste

 Raise quality awareness and personal concern for quality amongst all employees

 Take corrective actions, using established formal systems to remove the root causes of

problems

 Establish a zero defects committee and programme

 Train all employees in quality improvement

 Hold a Zero Defects Day to broadcast the change and as a management recommitment and

employee commitment

 Encourage individuals and groups to set improvement goals

 Encourage employees to communicate to management any obstacles they face in attaining

their

 improvement goals

 Give formal recognition to all participants

 Establish quality councils for quality management information sharing

 Do it all over again – form a new quality improvement team.



Tom Peter‘s identified leadership as being central to the quality improvement process, discarding

the word ―Management‖ for ―Leadership‖: The new role is of a facilitator, and the basis is ―Managing by

walking about‖ (MBWA), enabling the leader to keep in touch with customers, innovation and people, the

three main areas in the pursuit of excellence. He believes that, as the effective leader walks, at least 3 major

activities are happening:

 Listening- suggests caring

 Teaching- values are transmitted

 Facilitating-able to give on-the-spot help









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Having researched successful American organizations, he concluded that any intelligent approach

to organizing had to encompass, and treat as interdependent, seven variables, in what became known as

the McKinsey 7-S Framework, designed to force explicit thought about both the hardware and software of

an organization.









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There are many other management ―gurus‖ whose philosophies and ideas fill whole books on

their own, and several of these are important to quality management. The ones included in this section

are those whose reputation is primarily for their work in quality and excellence. When embarking on,

or continuing along, a quality journey within your organisation it is advisable to take note of the

messages from all of the prominent quality gurus, who have most influenced, the path of quality in the

last 50 – 60 years. However, be aware that there are contradictions between the gurus‘ approaches, as

well as many common features. It is imperative that the approach you take is purpose built and

tailored to suit your organization and its current and future needs. The total organizational excellence

model (TOE), discussed in the Implementation section, can help with these issues. However, be aware

that there are contradictions between the gurus‘ approaches, as well as many common features. It is

imperative that the approach you take is purpose built and tailored to suit your organization and its

current and future needs. The total organizational excellence model (TOE), discussed in the

Implementation section, can help with these issues.





QUALITY SYSTEM FOR MEDICAL DEVICES



Quality System requirements for medical have been internationally recognized as a way to assure

product safety and efficacy and customer satisfaction since at least 1983, and were instituted as

requirements in a final rule published on October 7, 1996. FDA had documented design defects in

medical devices that contributed to recalls from 1983 to 1989 that would have been prevented if Quality

Systems had been in place. The rule is promulgated at 21 CFR 820.





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According to current Good Manufacturing Practice (GMP), medical device manufacturers have

the responsibility to use good judgment when developing their quality system and apply those sections of

the Food and Drug Administration (FDA) Quality System (QS) Regulation that are applicable to their

specific products and operations, in Part 820 of the QS regulation. As with GMP, operating within this

flexibility, it is the responsibility of each manufacturer to establish requirements for each type or family of

devices that will result in devices that are safe and effective, and to establish methods and procedures to

design, produce, and distribute devices that meet the quality system requirements.



FDA has identified in the QS regulation the essential elements that a quality system shall embody

for design, production and distribution, without prescribing specific ways to establish these elements.

These elements include:









QUALITY MANAGEMENT SYSTEM

Quality System

The quality system comprises different components. In addition, there are several fundamental

requirements too. The following are the few examples of such kind.



 personnel training and qualification;

 controlling the product design;

 controlling documentation;



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 controlling purchasing;

 product identification and traceability at all stages of production;

 controlling and defining production and process;

 defining and controlling inspection, measuring and test equipment;

 validating processes;

 product acceptance;

 controlling nonconforming product;

 instituting corrective and preventive action when errors occur;

 labeling and packaging controls;

 handling, storage, distribution and installation;

 records;

 servicing and

 statistical techniques;



QS Regulation covers a broad spectrum of devices and production processes; it allows some

leeway in the details of quality system elements. It is left to manufacturers to determine the necessity for,

or extent of, some quality elements and to develop and implement procedures tailored to their particular

processes and devices. For example, if it is impossible to mix up labels at a manufacturer because there is

only one label to each product, then there is no necessity for the manufacturer to comply with all of the

GMP requirements under device labeling. Drug manufactures are regulated under a different section of

the CFR: 21 CFR 211. However, FDA has instituted new policies requiring QS for pharmaceuticals.



QUALITY MANAGEMENT ORGANIZATIONS AND AWARDS



The International Organization for Standardization's ISO 9000:2000 series describes standards for

a QMS addressing the principles and processes surrounding the design, development and delivery of a

general product or service. Organizations can participate in a continuing certification process to ISO

9001:2000 to demonstrate their compliance with the standard, which includes a requirement for continual

(i.e. planned) improvement of the QMS. (ISO 9000:2000 provides guidance on Quality principles and on

the common language used by quality professionals. ISO 9004:2000 provides guidance on improvement

methods. It can be seen that neither of these standards can be used for certification purposes as they

provide guidance, not requirements).



The Malcolm Baldrige National Quality Award is a competition to identify and recognize top-

quality U.S. companies. This model addresses a broadly based range of quality criteria, including

commercial success and corporate leadership. Once an organization has won the award it has to wait

several years before being eligible to apply again. The European Foundation for Quality Management's

EFQM Excellence Model supports an award scheme similar to the Malcolm Baldrige Award for

European companies.



In Canada, the National Quality Institute presents the 'Canada Awards for Excellence' on an

annual basis to organizations that have displayed outstanding performance in the areas of Quality and

Workplace Wellness, and have met the Institute's criteria with documented overall achievements and

results. The Alliance for Performance Excellence is a network of state, local, and international

organizations that use the Malcolm Baldrige National Quality Award criteria and model at the grassroots

level to improve the performance of local organizations and economies. NetworkforExcellence.org is the







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Alliance web site; browsers can find Alliance members in their state and get the latest news and events

from the Baldrige community.





IMPLEMENTATION OF QUALITY MANAGEMENT SYSTEM



Implementing a Quality Management System (QMS) within an organisation needs to be a decision

by top management. The objective of the quality system needs to be clearly defined so that the system can

be effective.









The design and implementation of a quality management system will vary depending on the type,

size and products of the organisation. Each company will have its own objective, however most

company‘s objective is to increase profitability. A Quality Management System will assist by:



 managing costs and risks

 increasing effectiveness and productivity

 identifying improvement opportunities

 increasing customer satisfaction



A well managed quality system will have an impact on:



 customer loyalty and repeat business

 market share

 operational efficiencies

 flexibility and ability to respond to market opportunities

 effective and efficient use of resources

 cost reductions



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 competitive advantages

 participation and motivation of human resources

 industry reputation

 control on all processes



The objectives of your Quality Management System should mirror the above in some form.



REQUIREMENTS



ISO 9001:2000 requires a quality system to be documented, tested, measured and

assessed. Management commitment is essential for the implementation and ongoing success of the

Quality Management System. QMS objectives must be measurable and reflect the overall company

objectives. The QMS must be able to be managed properly, adequate resources must be allocated. The

system must be reviewed regularly and measured for effectiveness, adjustments must be made to reflect

major changes to the organisation and business practices. The system must be practical and accessible to

all employees within the organisation.



ACCREDITATION



It is not essential to gain accreditation for a Quality Management System to work effectively. It

depends on the organisation if they wish to gain accreditation, however the benefits should be considered.

Your company will be recognized as an organisation that is committed to providing quality products,

improvement and customer satisfaction. You will gain respect through the industry as a fully accredited

quality company.



IMPLEMENTATION AND CERTIFICATION OF A QMS



Responsibility Process

 Set objectives and goals of the Quality Management System

 Appoint a Quality Team to develop and maintain the QMS

 Set timelines and project scope

Management  Allocate resources required for the development,

implementation and on-going management of the system

 Inform all staff and seek participation from all levels



 Decide if a Consultant is required for the project

 Prepare a project plan and allocate resources

 Assess an appropriate budget based on equipment, training,

Management/

time and personnel required

Quality Team

 Seek approval from management to procure required resources

and attend any training



 Assess method for documenting the QMS

 Design templates and documentation

Quality Team

 Set timelines for the various tasks

 Schedule individual departments and positions for







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development of policies and procedures



 Develop Quality Management System policies to reflect

company objectives

 Start to develop procedures and work instructions with each

department

Quality Team/  Report to management any risks and improvement

Management opportunities that have been found

 Document any Quality Corrective Action Requests that might

be identified (identified risk areas that require management

attention and improvements)



 Approve and issue the Quality Management System

Management  Operate the QMS for a minimum period of 3 months



 Carry out initial audits to ensure documentation matched

processes

 Ensure that "you do what you say you do" if any deficiencies

are found either change processes or change the QMS to

reflect what is actually done

Quality Team





 Assess the effectiveness of the QMS and implement any

changes that might be required



 Undertake management review of the QMS

 Adjust resource requirements

 Decide if accreditation assessment is required

Management

 Set accreditation assessment timelines

 Appoint Accreditation Body



 Undertake Audit

 Report findings to management including any changes required

Accreditation Body

to the QMS



 Make changes to the QMS according to the findings of the

Quality Team/ Accreditation Body

Management  Advise Accreditation Body to reassess the QMS



 Undertake follow-up audit

Accreditation Body  If all requirements are met, accreditation will be issued



 Continue to audit, review and assess the QMS at the agreed

Quality Team/

time intervals

Management

 Continue to assess risk areas and identify improvement

Accreditation Body

opportunities



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 Continue to review policies and procedures and make

amendments as required

 Continue to measure effectiveness of QMS to the overall

company performance









CONDUCTING QUALITY AUDITS



Internal Quality Audits is a powerful tool for any business to measure the effectiveness

of the Quality Management System. It is also a good management tool that can be used to review

processes and identify any weaknesses, risks and areas of improvement. On the Quality Management

System sense it is used to assess if a process is working, if things are being done the way they are supposed

to be done, however at the same time it is an excellent way of measuring the effectiveness of your

procedures. Audits should be planned on a regular basis so that each activity is audited at least once in the

audit cycle. High risk areas should be audited more often to ensure conformance. An audit can also be

carried out if a particular problem has arisen, to establish the source of the problem and document any

corrective actions. Audits are also used to check any previously identified non-conformances or business

changes. A good opportunity to assess how effective the changes have been.



QUALITY AUDITORS

You don't need to employ specific auditors, it is always best to choose a team of auditors from

within the company; your audit team should comprise people from all areas and levels of the company.

Auditor training is always recommended, and a formal auditing method will assist in keeping the audits

uniform. By choosing a broad spectrum of auditors from all levels of the organisation, you will ensure that

everyone has commitment to the Quality System and gain a better understanding of the overall

management process of the company. It is a good way for people from various areas to gain an

understanding on how their department fits into the organisation.



It is a pre-requisite that an auditor cannot audit a department or process that they are involved in,

for obvious reasons. It is also best not to use top management as auditors, this way an audit will not be

perceived as a personal evaluation or appraisal. Auditing should be seen as a positive process not as a

fault finding exercise.



THE QUALITY AUDIT



Audits need to be documented, it is important to remember that you are auditing against the

Quality Management System and therefore audits should be constructed against procedures from the

Quality Manual. If it is not in the manual, then it should not be audited. During an audit you need to see

evidence that the processes are being done in accordance to the procedures and policies. Evidence should

be recorded against each section being audited. Recording of evidence needs to have a description of the

documentation sighted, number, date and any other information that will assist in identifying that

document. Audit findings need to be documented and any non-conformances found should be reported

for further action. A date should be established for the correction, a follow up audit should be carried out

to ensure that the non-conformance has been fixed.





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QUALITY AUDIT DOCUMENTATION



Audit documentation does not need to be complicated, normally there are 3 documents: the audit

plan, audit notes and audit report.



The audit plan: The audit plan is sent to the department being audited a few days prior, it should include

the date of the audit, the planned time, duration, auditors names, location (if relevant) and the policies and

procedures that will be used during the audit. It should also mention any non-conformances that were

found during last audit.



Audit Notes: The notes are the Auditor's questions that will be asked during the audit. It should include

references to particular policies and procedures and what will be asked during the audit. The same

document should be used to record the findings and any comments during the audit.



Audit Report: The audit report is the official document used to report the findings of the audit. This

document should include details of the audit, date, auditors names, policies and procedures and findings

against them. It should include if it passed audit and any non-conformances or observations found. If

non-conformance are found a date should be established for completion of corrective actions. The audit

report is normally signed by the auditor and the department manager.









Quality Management System



Quality Manual is the apex document which documents the Quality Policy, the Quality Objectives

and the Quality Management System to be followed by all employees within MIND and to achieve

customer satisfaction through conformance to requirements. All our employees follow a documented

Quality Management System (QMS) to ensure that software and services provided by MIND conforms to

specified requirements. MIND QMS has been organized into procedures, guidelines, templates and

checklist defined according to CMM Level 5 specifications.









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QUALITY CONTROL



QUALITY - INITIAL PROBLEMS



Let‘s face it, quality is difficult to define. We want to be precise, to create a quality definition, yet

language is limited. Nor does it help that our domain has expanded from the relatively- constrained

factory floor into the open realms of a broader business context, and beyond that, to environmental and

social domains. The IQA dallies with all of the above definitions demonstrating the difficulty of naming

quality. In the end, it plumps for a customer focus of quality that ranges throughout the product/service

chain: this is still is not enough. The perception of ‗quality‘ as almost impossible to define is not confined

to our profession; in 'The Timeless Way of Building', architect Christopher Alexander calls it ‗the quality

without a name‘. In the same way that we know a good room when we use one, but cannot define

exactly what makes it good, we can name its attributes of quality, but cannot define quality itself. One

way to find a good definition of anything is to take a broader view. Alexander does this in his definition

of a ‗pattern language‘ for architecture, which reduces the whole of building and town design to 252

simple rule-sets.



What does this mean for quality?

Casting a keen quality eye over this revised definition may lead to certain queasiness. Optimising

means making compromises but we have technology: remember Mr Pareto and his law, and Juran‘s

‗vital few‘. We are not counting defects but units of value, in terms of value created and of the levels and

types of value required to keep each player in the game. A simple conceptual model is to imagine

everyone putting coins into a central pot and then taking them out again at a later time. As long as there



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is money in the pot, and there are people to play, the game continues. Staying in business means keeping

the game going. A consideration within this game is that some players can easily leave. When they are

critical value contributors (as customers often are), they can demand a higher level of value in return.

This can lead to low-value customers which many of us tolerate under the ‗customer is always right‘

banner. What we sometimes forget is that if someone is taking too much out of the pot, they can be

asked to leave.



If quality is making this game work, then quality professionals need to understand the game. It

does not mean abandoning our concern for customers and products: far from it. But it does mean

optimising the system so that the whole thing continues to operate. Blind quality is what killed TQM in

many companies. Why should I map my processes? - Because it is the right thing to do. Why do I need

to empower everyone? - Because it works. The revised view of quality proposed here pushes against such

mantras. Thus, one more defining statement is:



If we are to accept this definition, the most important result is that it changes what we must do as

quality professionals. We must act on the words: understanding, optimising, system, value and exchange.

It means understanding how things truly work, both individually and as systems. It means understanding

people, what they value and how they effectively trade with others. And it means working out how these

imperfect systems can be optimised so our businesses thrive. An ancient Chinese emperor once asked

his wise counsellor‘s advice for the greatest thing that could happen. The counsellor said: ‗Grandfather

dies, father dies, son dies.‘

The emperor was shocked at such a morbid suggestion until he realised that changing this sequence

would bring a far greater sadness. The same applies to our companies, which are often much like our

children. We can change and advise them in many ways, but the greatest thing we can do is to give them

the strength to outlive us.









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One Word Questions



1. ISM CODE

2. SOLAS

3. DOC

4. SMC

5. SPC

6. QII

7. MBWA

8. When did the Americans took the messages of quality to Japan ?

9. In (late) 1950‘s who developed new concept in response to the Americans ?



Answers

1. International Safety Management Code

2. Safety of Life at Sea

3. Document of Compliance

4. Safety Management Code

5. Statistical Process Control

6. Quality Improvement Team

7. Managing By Walking About

8. 1950‘s

9. Japanese



Short Answer Questions







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1. Explain the historical background of Quality

2. Describe the evolution of Quality with diagram

3. Discuss about the improvement of Quality

4. Explain keys to quality and the Quality Gurus

5. Explain about Americans going to Japan

6. Explain the 14 steps to Quality improvement

7. What are quality system for medical devices‘

8. What is Quality Management Systems

9. Explain Quality Audit Documentation









CHAPTER II

PRINCIPLES AND PHILOSOPHY OF QUALITY



INTRODUCTION



William Edwards Deming (October 14, 1900 – December 20, 1993) was an American

statistician, professor, author, lecturer, and consultant. Deming is widely credited with improving

production in the United States during World War II, although he is perhaps best known for his work in

Japan. There, from 1950 onward he taught top management how to improve design (and thus service),

product quality, testing and sales (the last through global markets) through various methods, including the

application of statistical methods.



Deming made a significant contribution to Japan's later renown for innovative high-quality

products and its economic power. He is regarded as having had more impact upon Japanese

manufacturing and business than any other individual not of Japanese heritage. Despite being considered

something of a hero in Japan, he was only beginning to win widespread recognition in the U.S. at the time

of his death.



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OVERVIEW



Ford Motor Company was simultaneously manufacturing a car model with transmissions made in

Japan and the United States. Soon after the car model was on the market, Ford customers were requesting

the model with Japanese transmission over the USA-made transmission, and they were willing to wait for

the Japanese model. As both transmissions were made to the same specifications, Ford engineers could

not understand the customer preference for the model with Japanese transmission. It delivered smoother

performance with a lower defect rate. Finally, Ford engineers decided to take apart the two different

transmissions. The American-made car parts were all within specified tolerance levels. On the other hand,

the Japanese car parts had much closer tolerances than the USA-made parts - i.e. if a part was supposed to

be one foot long, plus or minus 1/8 of an inch - then the Japanese parts were within 1/16 of an inch.

This made the Japanese cars run more smoothly and customers experienced fewer problems. This is an

example of Dr. Deming's teachings, having been adopted by the Japanese, delivering better quality

products.



Deming received a B.S. in electrical engineering from the University of Wyoming at Laramie

(1921), an M.S. from the University of Colorado (1925), and a Ph.D. from Yale University (1928). Both

graduate degrees were in mathematics and physics. Deming had an internship at Bell Telephone

Laboratories while studying at Yale. He subsequently worked at the U.S. Department of Agriculture and

the Census Department. While working under Gen. Douglas MacArthur as a census consultant to the

Japanese government, he famously taught statistical process control methods to Japanese business

leaders, returning to Japan for many years to consult and to witness economic growth that he had

predicted as a result of application of techniques learned from Walter Shewhart at Bell Laboratories.

Later, he became a professor at New York University while engaged as an independent consultant in

Washington, D.C.



Deming was the author of Out of the Crisis (1982–1986) and The New Economics for Industry,

Government, Education (1993), which includes his System of Profound Knowledge and the 14 Points for

Management (described below). Deming played flute & drums and composed music throughout his life,

including sacred choral compositions and an arrangement of The Star Spangled Banner. In 1993, Deming

founded the W. Edwards Deming Institute in Washington, D.C., where the Deming Collection at the

U.S. Library of Congress includes an extensive audiotape and videotape archive. The aim of the W.

Edwards Deming Institute is to foster understanding of The Deming System of Profound Knowledge to

advance commerce, prosperity and peace.





EARLY LIFE AND WORK



Born in Sioux City, Iowa, Deming was raised in Polk City, Iowa on his grandfather's chicken farm,

then later in Powell, Wyoming. His father's name was also William, so he was called Edwards (the maiden

name of his mother, Pluma Irene Edwards). In 1917, he enrolled in the University of Wyoming at

Laramie, graduating in 1921 with a B.S. in electrical engineering. In 1925, he received an M.S. from the

University of Colorado, and in 1928, a Ph.D. from Yale University. Both graduate degrees were in

mathematics and mathematical physics. Deming worked as a mathematical physicist at the United States

Department of Agriculture (1927–39), and was a statistical adviser for the United States Census Bureau



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(1939–45). He was a professor of statistics at New York University's graduate school of business

administration (1946–1993), and he taught at Columbia University's graduate School of business (1988–

1993). He also was a consultant for private business.



In 1927, Deming was introduced to Walter A. Shewhart of the Bell Telephone Laboratories by

Dr. C.H. Kunsman of the United States Department of Agriculture (USDA). Deming found great

inspiration in the work of Shewhart, the originator of the concepts of statistical control of processes and

the related technical tool of the control chart, as Deming began to move toward the application of

statistical methods to industrial production and management. Shewhart's idea of common and special

causes of variation led directly to Deming's theory of management. Deming saw that these ideas could be

applied not only to manufacturing processes but also to the processes by which enterprises are led and

managed. This key insight made possible his enormous influence on the economics of the industrialized

world after 1950. Deming edited a series of lectures delivered by Shewhart at USDA, Statistical Method

from the Viewpoint of Quality Control, into a book published in 1939. One reason he learned so much

from Shewhart, Deming remarked in a videotaped interview, was that, while brilliant, Shewhart had an

"uncanny ability to make things difficult." Deming thus spent a great deal of time both copying

Shewhart's ideas and devising ways to present them with his own twist.



Deming developed the sampling techniques that were used for the first time during the 1940 U.S.

Census. During World War II, Deming was a member of the five-man Emergency Technical Committee.

He worked with H.F. Dodge, A.G. Ashcroft, Leslie E. Simon, R.E. Wareham, and John Gaillard in the

compilation of the American War Standards (American Standards Association ZI.1-3 published in 1942)

and taught statistical process control (SPC) techniques to workers engaged in wartime production.

Statistical methods were widely applied during World War II, but faded into disuse a few years later in the

face of huge overseas demand for American mass-produced products.





WORK IN JAPAN



In 1947, Deming was involved in early planning for the 1951 Japanese Census. The Allied powers

were occupying Japan, and he was asked by the U.S. United States Department of the Army to assist with

the census. While Deming was there, his expertise in quality control techniques, combined with his

involvement in Japanese society, led to his receiving an invitation from the Japanese Union of Scientists

and Engineers (JUSE). JUSE members had studied Shewhart's techniques, and as part of Japan's

reconstruction efforts, they sought an expert to teach statistical control. During June–August 1950,

Deming trained hundreds of engineers, managers, and scholars in statistical process control (SPC) and

concepts of quality. He also conducted at least one session for top management. [10] Deming's message to

Japan's chief executives: improving quality will reduce expenses while increasing productivity and market

share. Perhaps the best known of these management lectures was delivered at the Mt. Hakone Conference

Center in August 1950. A number of Japanese manufacturers applied his techniques widely and

experienced theretofore unheard of levels of quality and productivity. The improved quality combined

with the lowered cost created new international demand for Japanese products. Deming declined to

receive royalties from the transcripts of his 1950 lectures, so JUSE's board of directors established the

Deming Prize (December 1950) to repay him for his friendship and kindness. The Deming Prize—

especially the Deming Application Prize, which is given to companies—has exerted an immeasurable

influence directly or indirectly on the development of quality control and quality management in Japan.







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HONOURS



In 1960, the Prime Minister of Japan (Nobusuke Kishi), acting on behalf of Emperor Hirohito,

awarded Dr. Deming Japan‘s Order of the Sacred Treasure, Second Class. The citation on the medal

recognizes Deming's contributions to Japan‘s industrial rebirth and its worldwide success. The first

section of the meritorious service record describes his work in Japan:



 1947, Rice Statistics Mission member

 1950, assistant to the Supreme Commander of the Allied Powers

 instructor in sample survey methods in government statistics



The second half of the record lists his service to private enterprise through the introduction of epochal

ideas, such as quality control and market survey techniques.



LATER WORK IN THE U.S.



David Salsburg wrote:

"He was known for his kindness to and consideration for those he worked with, for his robust, if

very subtle, humor, and for his interest in music. He sang in a choir, played drums and flute, and

published several original pieces of sacred music."

Later, from his home in Washington, D.C., Dr. Deming continued running his own consultancy

business in the United States, largely unknown and unrecognized in his country of origin and work. In

1980, he was featured prominently in an NBC documentary titled If Japan can... Why can't we? about the

increasing industrial competition the United States was facing from Japan. As a result of the broadcast,

demand for his services increased dramatically and Deming continued consulting for industry throughout

the world until his death at the age of 93.



Ford Motor Company was one of the first American corporations to seek help from Deming. In

1981, Ford's sales were falling. Between 1979 and 1982, Ford had incurred $3 billion in losses. Ford's

newly appointed Division Quality Manager John A. Manoogian was charged with recruiting Dr. Deming

to help jump-start a quality movement at Ford. Deming questioned the company's culture and the way its

managers operated. To Ford's surprise, Deming talked not about quality but about management. He told

Ford that management actions were responsible for 85% of all problems in developing better cars. In

1986 Ford came out with a profitable line of cars, the Taurus-Sable line. In a letter to Auto week

Magazine, Donald Petersen, then Ford Chairman, said, "We are moving toward building a quality culture

at Ford and the many changes that have been taking place here have their roots directly in Dr. Deming's

teachings." By 1986, Ford had become the most profitable American auto company. For the first time

since the 1920s, its earnings had exceeded those of arch rival General Motors (GM). Ford had come to

lead the American automobile industry in improvements. Ford's following years' earnings confirmed that

its success was not a fluke, for its earnings continued to exceed GM and Chrysler's.



In 1982, Dr. Deming, as author, had his book published by the MIT Center for Advanced

Engineering as Quality, Productivity, and Competitive Position, which was renamed Out of the Crisis in

1986. Deming offers a theory of management based on his famous 14 Points for Management.

Management's failure to plan for the future brings about loss of market, which brings about loss of jobs.

Management must be judged not only by the quarterly dividend, but by innovative plans to stay in





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business, protect investment, ensure future dividends, and provide more jobs through improved products

and services. "Long-term commitment to new learning and new philosophy is required of any

management that seeks transformation. The timid and the fainthearted, and the people that expect quick

results, are doomed to disappointment."



Over the course of his career, Deming received dozens of academic awards, including another,

honorary, Ph.D. from Oregon State University. In 1987 he was awarded the National Medal of

Technology: "For his forceful promotion of statistical methodology, for his contributions to sampling

theory, and for his advocacy to corporations and nations of a general management philosophy that has

resulted in improved product quality." In 1988, he received the Distinguished Career in Science award

from the National Academy of Sciences. In 1993, Dr. Deming published his final book, The New

Economics for Industry, Government, and Education, which included the System of Profound

Knowledge and the 14 Points for Management. It also contained educational concepts involving group-

based teaching without grades, as well as management without individual merit or performance reviews.

In December 1993, W. Edwards Deming died in his sleep at his Washington home at about 3 a.m. due to

"natural causes." His family was by his side when he died.



DEMING PHILOSOPHY SYNOPSIS





The philosophy of W. Edwards Deming has been summarized as follows:

"Dr. W. Edwards Deming taught that by adopting appropriate principles of management,

organizations can increase quality and simultaneously reduce costs (by reducing waste, rework,

staff attrition and litigation while increasing customer loyalty). The key is to practice continual

improvement and think of manufacturing as a system, not as bits and pieces."

In the 1970s, Dr. Deming's philosophy was summarized by some of his Japanese proponents with the

following 'a'-versus-'b' comparison:

(a) When people and organizations focus primarily on quality, defined by the following ratio,









Quality tends to increase and costs fall over time.



(b) However, when people and organizations focus primarily on costs (often dominant/typical

human behavior), costs (due to not minimizing waste, ignoring amount of rework occurring,

taking staff for granted, not rapidly resolving disputes, and failing to notice lack of product

improvement—plus, over time, loss of customer loyalty) tend to rise and quality declines over

time.



THE DEMING SYSTEM OF PROFOUND KNOWLEDGE



"The prevailing style of management must undergo transformation. A system cannot understand

itself. The transformation requires a view from outside. The aim of this chapter is to provide an outside

view—a lens—that I call a system of profound knowledge. It provides a map of theory by which to

understand the organizations that we work in.





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The first step is transformation of the individual. This transformation is discontinuous. It comes

from understanding of the system of profound knowledge. The individual, transformed, will perceive new

meaning to his life, to events, to numbers, to interactions between people. Once the individual

understands the system of profound knowledge, he will apply its principles in every kind of relationship

with other people. He will have a basis for judgment of his own decisions and for transformation of the

organizations that he belongs to. The individual, once transformed, will:



 Set an example;

 Be a good listener, but will not compromise;

 Continually teach other people; and

 Help people to pull away from their current practices and beliefs and move into the new

philosophy without a feeling of guilt about the past."



Deming advocated that all managers need to have what he called a System of Profound Knowledge,

consisting of four parts:



1. Appreciation of a system: understanding the overall processes involving suppliers,

producers, and customers (or recipients) of goods and services (explained below);

2. Knowledge of variation: the range and causes of variation in quality, and use of statistical

sampling in measurements;

3. Theory of knowledge: the concepts explaining knowledge and the limits of what can be

known (see also: epistemology);

4. Knowledge of psychology: concepts of human nature.



Deming explained, "One need not be eminent in any part nor in all four parts in order to

understand it and to apply it. The 14 points for management in industry, education, and government

follow naturally as application of this outside knowledge, for transformation from the present style of

Western management to one of optimization.



The various segments of the system of profound knowledge proposed here cannot be separated.

They interact with each other. Thus, knowledge of psychology is incomplete without knowledge of

variation. A manager of people needs to understand that all people are different. This is not ranking

people. He needs to understand that the performance of anyone is governed largely by the system that he

works in, the responsibility of management. A psychologist that possesses even a crude understanding of

variation as will be learned in the experiment with the Red Beads could no longer participate in

refinement of a plan for ranking people.



The Appreciation of a system involves understanding how interactions (i.e. feedback) between the

elements of a system can result in internal restrictions that force the system to behave as a single

organism that automatically seeks a steady state. It is this steady state that determines the output of the

system rather than the individual elements. Thus it is the structure of the organization rather than the

employees, alone, which holds the key to improving the quality of output. The Knowledge of variation

involves understanding that everything measured consists of both "normal" variation due to the flexibility

of the system and of "special causes" that create defects. Quality involves recognizing the difference in

order to eliminate "special causes" while controlling normal variation. Deming taught that making

changes in response to "normal" variation would only make the system perform worse. Understanding

variation includes the mathematical certainty that variation will normally occur within six standard

deviations of the mean.



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The System of Profound Knowledge is the basis for application of Deming's famous 14 Points for

Management, described below.





DEMING'S 14 POINTS



Deming offered fourteen key principles for management for transforming business effectiveness.

The points were first presented in his book Out of the Crisis.



1. Create constancy of purpose toward improvement of product and service, with the aim to

become competitive and stay in business, and to provide jobs.

2. Adopt the new philosophy. We are in a new economic age. Western management must awaken

to the challenge, must learn their responsibilities, and take on leadership for change.

3. Cease dependence on inspection to achieve quality. Eliminate the need for inspection on a

mass basis by building quality into the product in the first place.

4. End the practice of awarding business on the basis of price tag. Instead, minimize total cost.

Move towards a single supplier for any one item, on a long-term relationship of loyalty and

trust.

5. Improve constantly and forever the system of production and service, to improve quality and

productivity, and thus constantly decrease cost.

6. Institute training on the job.

7. Institute leadership. The aim of supervision should be to help people and machines and

gadgets to do a better job. Supervision of management is in need of overhaul, as well as

supervision of production workers.

8. Drive out fear, so that everyone may work effectively for the company. Break down barriers

between departments. People in research, design, sales, and production must work as a team,

to foresee problems of production and in use that may be encountered with the product or

service.

9. Eliminate slogans, exhortations, and targets for the work force asking for zero defects and new

levels of productivity. Such exhortations only create adversarial relationships, as the bulk of the

causes of low quality and low productivity belong to the system and thus lie beyond the power

of the work force.

10. a. Eliminate work standards (quotas) on the factory floor. Substitute leadership.

b. Eliminate management by objective. Eliminate management by numbers, numerical goals.

Substitute workmanship.

11. Remove barriers that rob the hourly worker of his right to pride of workmanship. The

responsibility of supervisors must be changed from sheer numbers to quality.

12. Remove barriers that rob people in management and in engineering of their right to pride of

workmanship. This means, inter alia, abolishment of the annual or merit rating and of

management by objective.

13. Institute a vigorous program of education and self-improvement.

14. Put everyone in the company to work to accomplish the transformation. The transformation is

everyone's work.



SEVEN DEADLY DISEASES







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The Seven Deadly Diseases (also known as the "Seven Wastes"):



1. Lack of constancy of purpose.

2. Emphasis on short-term profits.

3. Evaluation by performance, merit rating, or annual review of performance.

4. Mobility of management.

5. Running a company on visible figures alone.

6. Excessive medical costs.

7. Excessive costs of warranty, fueled by lawyers who work for contingency fees.



A Lesser Category of Obstacles:



1. Neglecting long-range planning.

2. Relying on technology to solve problems.

3. Seeking examples to follow rather than developing solutions.

4. Excuses, such as "Our problems are different."



JOSEPH M. JURAN – INTRODUCTION



Joseph M. Juran made many contributions to the field of quality management in his 70+ active

working years. His book, the Quality Control Handbook, is a classic reference for quality engineers. He

revolutionized the Japanese philosophy on quality management and in no small way worked to help shape

their economy into the industrial leader it is today. Dr. Juran was the first to incorporate the human aspect

of quality management which is referred to as Total Quality Management.



The process of developing ideas was a gradual one for Dr. Juran. Top management involvement,

the Pareto principle, the need for widespread training in quality, the definition of quality as fitness for use,

the project-by-project approach to quality improvement--these are the ideas for which Juran is best

known, and all emerged gradually.







A LIFETIME OF PROFESSIONAL AND WORLDWIDE QUALITY



Braila, Romania. December, 1904. The threadbare Jakob Juran family welcomes a newborn son,

Joseph Moses. Five years later Jakob leaves Romania for America. By 1912, he has earned enough to bring

the rest of the family to join him in Minnesota. Despite this hopeful emigration and American

opportunities, the family continues in poverty. Young Joseph Juran demonstrates his affinity for

knowledge; in school, his level of mathematical and scientific proficiency so exceeds the average that he

eventually skips the equivalent of four grade levels. In 1920, he enrolls at the University of Minnesota, the

first member of his family to pursue higher education. By 1925, he had received a B.S. in electrical

engineering and is working with Western Electric in the Inspection Department of the famous Hawthorne

Works in Chicago. The complexity of this enormous factory, manned by 40,000 workers, presents Juran

with his first challenge in management.

In 1926, a team of Quality Control pioneers from Bell Laboratories brought a new program to

Hawthorne Works. The program, designed to implement new tools and techniques, required a training

program. From a group of 20 trainees, Juran became one of two engineers for the Inspection Statistical

Department, one of the first of such divisions created in American industry.



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By 1937, Juran was the chief of Industrial Engineering at Western Electric's home office in New

York. His work involved visiting other companies and discussing methods of quality management. During

WWII, Juran's temporary leave of absence from Western Electric stretched through four years. During

that time, he served in Washington, D.C. as an assistant administrator for the Lend-Lease Administration.

He and his team improved the efficiency of the process, eliminating excessive paperwork and thus

hastening the arrival of supplies to the United States' overseas friends. Juran finally left Washington in

1945, but he didn't return to Western Electric. Rather, he chose to devote the remainder of his life to the

study of quality management.



As early as 1928, Juran had written a pamphlet entitled "Statistical Methods Applied to

Manufacturing Problems." By the end of the war, he was a well-known and highly-regarded statistician

and industrial engineering theorist. After he left Western Electric, Juran became Chairman of the

Department of Administrative Engineering at New York University, where he taught for many years. He

also created a thriving consulting practice, and wrote books and delivered lectures for American

Management Association. It was his time with NYU and the AMA which allowed for the development of

his management philosophies which are now embedded in the foundation of American and Japanese

management. His classic book, the Quality Control Handbook, first released in 1951, is still the standard

reference work for quality managers. The following table outlines the major points of Dr. Juran's quality

management ideas:



QUALITY TRILOGY



 Identify who are the customers.

 Determine the needs of those customers.

 Translate those needs into our language.

Quality Planning

 Develop a product that can respond to those needs.

 Optimize product features so as to meet our needs and customer needs.





 Develop a process which is able to produce the product.

Quality

 Optimize the process.

Improvement



 Prove that the process can produce the product under operating conditions

with minimal inspection.

Quality Control

 Transfer the process to Operations.







AN HONOURED THEORIST



The Union of Japanese Scientists and Engineers invited Dr. Juran to Japan, to teach them the

principles of quality management as they rebuilt their economy. Along with W. Edwards Deming, his

more colorful and perhaps better-known American colleague, Juran received Second Order of the Sacred

Treasure award from Emperor Hirohito of Japan. Dr. Juran published his lectures from Japan in his book

Managerial Breakthrough in 1964. In 1979, Juran founded The Juran Institute to better facilitate broader

exposure of his ideas. The Juran Institute is today one of the leading quality management consultancies in



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the world, and it produces books, workbooks, videos and other materials to support the wide use of Dr.

Juran's methods. The institute and the consulting practice continue to thrive today. Dr. Juran worked to

promote quality management into his 90's, and only recently retired from his semi-public life. One can

obtain the papers, lectures, and tapes of Dr. Juran from The Juran Institute or other quality management

educational providers. The Juran Foundation, which he founded, continues his work, exploring the social

and industrial implications of quality improvement while making his and others' valuable contributions

more accessible.



PHILIP CROSBY: THE FUN UNCLE OF THE QUALITY REVOLUTION

"Do It Right the First Time"

Dr. Deming and Dr. Juran were the great brains of the quality revolution. Where Phil Crosby

excelled was in finding a terminology for quality that mere mortals could understand. His books,

"Quality without Tears" and "Quality is Free" were easy to read, so people read them. He popularized

the idea of the "cost of poor quality", that is, figuring out how much it really costs to do things badly.



Like Frederick Taylor, Philip Crosby's ideas came from his experience on an assembly line. He

focused on zero defects, not unlike the focus of the modern Six Sigma Quality movement. Mr. Crosby

was quick to point out, however, that zero defects is not something that originates on the assembly

line. To create a manufacturing process that has zero defects management must set the tone and

atmosphere for employees to follow. If management does not create a system by which zero defects

are clearly the objective then employees are not to blame when things go astray and defects occur. The

benefit for companies of such a system is a dramatic decrease in wasted resources and time spent

producing goods that consumer's do not want.



Mr. Crosby defined quality as conformity to certain specifications set forth by management

and not some vague concept of "goodness." These specifications are not arbitrary either; they must be

set according to customer needs and wants.



FOUR ABSOLUTES OF QUALITY MANAGEMENT



1. Quality is defined as conformance to requirements, not as 'goodness' or 'elegance'.

2. The system for causing quality is prevention, not appraisal.

3. The performance standard must be Zero Defects, not "that's close enough".

4. The measurement of quality is the Price of Nonconformance, not indices.



Biography



Philip Crosby was born in West Virginia in 1926. After serving in WWII and the Korean

War he has worked for Crosley, Martin-Marietta and ITT where he was corporate vice president for 14

years. Philip Crosby Associates, Inc., founded in 1979, was his management consulting firm that served

served hundreds of companies. Since retiring in 1991 he has founded Career IV, Inc., Philip Crosby

Associates II, Inc. and the Quality College. Phil Crosby died in August, 2001, but his legacy will live on in

better quality in thousands of organizations. Here's an encomium from W. Noel Haskins-Hafer, a teacher

of quality improvement: 'He was one of the warmest and most focused people I ever had the pleasure to

meet and his common-sense approach will be missed by many.



ARMAND FEIGENBAUM



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Armand V. Feigenbaum is an American quality control expert who was born in 1922.

He received a bachelor's degree from Union College, and his master's degree and Ph.D. from MIT. He

was Director of Manufacturing Operations at General Electric (1958-1968), and is now President and

CEO of General Systems Company of Pittsfield, Massachusetts, an engineering firm that designs and

installs operational systems. He wrote several books and served as President of the American Society for

Quality (1961-1963). Feigenbaum's contributions to the quality body of knowledge include: In engineering

and manufacturing, quality control and quality engineering are involved in developing systems to ensure

products or services are designed and produced to meet or exceed customer requirements. ... Doctor of

Philosophy (Ph. ... Mapúa Institute of Technology (MIT) is a private, non-sectarian, Filipino tertiary

institute located in Intramuros, Manila. ... 1958 (MCMLVIII) was a common year starting on Wednesday

of the Gregorian calendar. ... 1968 (MCMLXVIII) was a leap year starting on Monday (the link is to a full

1968 calendar). ... American Society for Quality (ASQ), formerly known as American Society for Quality

Control (ASQC), is a non-profit professional society comprised of almost 100,000 members who work in

various aspects of the quality field (e. ... 1961 (MCMLXI) was a common year starting on Sunday (the link

is to a full 1961 calendar). ... 1963 (MCMLXIII) was a common year starting on Tuesday (the link is to a

full 1963 calendar). ...



 Total Quality Control (TQC) - "Total quality control is an effective system for integrating the

quality development, quality maintenance, and quality improvement efforts of the various groups

in an organisation so as to enable production and service at the most economical levels which

allow full customer satisfaction."



 The "hidden" plant - the idea that so much extra work is performed in correcting mistakes that

there is effectively a hidden plant within any factory.



 Because quality is everybody's job, it may become nobody's job - the idea that quality must be

actively managed and have visibility at the highest levels of management.



Our "Quality Guru of the Month" is Dr. Armand V. Feigenbaum. Feigenbaum is the originator of

Total Quality Control. While he was a doctoral student at MIT, Feigenbaum completed his first edition of

his book Total Quality Control. Feigenbaums work centralized around the notion for a systematic or total

approach to quality. He argued that total approach to quality requires the involvement of all functions of

the quality process, not only manufacturing. His idea was to build in quality in the early stage rather than

inspecting and controlling after the processes have been completed.



According to the Department of Trade and Industry, Feigenbaum served as the worldwide

Director of Manufacturing Operations and Quality Control at General Electric Company between 1958

and 1968. He later became President of General Systems Company, Inc. Feigenbaum was also the

founding chairman of the International Academy for Quality and also the past president of the American

Society of Quality Control. In 1988, Feigenbaum was appointed to the board of overseers of the United

States Malcolm Baldridge National Quality Award Program. Dr. Feigenbaums message was to move away

from the concerns of the technical aspect of quality control and make a focus of quality control as a

business method, including administrative and human relation functions. Another one of his emphases is

that quality does not mean "best" but "best for the customer." Feigenbaum saw Modern Quality Control

as the stimulating and building up of operator responsibilities and interests in quality. Feigenbaum also

argued that all levels of quality need to be emphasized. For quality control to achieve its specified results





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there is the need for complete support from management as well as the quality control program must

develop gradually from within the organization.



Dr. Feigenbaum is known for his thoughts on how quality programs are one of the most powerful

change agents for companies today. As a result of Dr. Feigenbaums work, company management has

assumed the responsibility to make leadership contributions that will increase their companies growth,

which in turn will positively affect the national economy.



THE GURUS - KAORU ISHIKAWA

Ishikawa is a forgotten guru to many in the world of quality. His contributions were very basic,

similar to the work of the statisticians. Ishikawa is remembered for his books on the tools of quality. He

contributed the term "Seven Tools of Quality." These tools are:

(1) Histograms

(2) Cause and effect diagrams

(3) Check sheets

(4) Pareto diagrams

(5) Graphs

(6) Control charts, and

(7) Scatter diagrams.



Although he did not develop any of these tools, he put them into wide use. He used these tools

because they were simple. He believed that 90% of all problems can be solved by the use of simple tools.

A simple tool that Ishikawa developed and put into wide use in Japan is the Quality Circle. This was

developed because he believed that "QC begins with the interaction of people." Quality circles eventually

led to the development of team concepts around the world. Ishikawa also believed in the concept of

Company Wide Quality Control (CWQC). He felt that CWQC would be used world wide and would

improve all countries quality and economy. The basic conditions for successful CWQC are as follows:



1. All employees should clearly understand the aim of the company in order to introduce and

promote CWQC.

2. The features of CWQC of the whole company, of departments, and of branches should be

clarified. People should have confidence in these features.

3. The effective PDCA (plan-do-check-act) cycle should rotate in the whole company, in branches, in

plants, and in workshops for at lease three to five years. Statistical quality and process analyses

should be adequately carried out, and upstream control should be developed and effectively used.

4. The company should have the capability of establishing a long term plan of CWQC and of

carrying it out systematically.

5. The walls between departments-or sectionalism-should be broken down, and cross functional

management should be effectively carried out.

6. Everyone should act with confidence, believing that his or her work will bear fruit.



In addition, the following indices should be used to signify successful CWQC:



1. Development of new product progresses on schedule.





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2. The percent defective, including rework, is lower than 5 percent one week after the start of mass

production.

3. The product sells very well without customer complaints.



Finally, four points that totally encompass the beliefs of Kaoru Ishikawa:



1. Revolution in the philosophy of management; that is, management in which humanity is respected.

2. Company Wide Quality Control.

3. Effective use of catch phrases those are adapted to the trend of the times.

4. Quality first concept; that is, customer satisfaction.



KAORU ISHIKAWA: ONE STEP FURTHER



Kaoru Ishikawa wanted to change the way people think about work. He urged managers to resist

becoming content with merely improving a product's quality, insisting that quality improvement can

always go one step further. His notion of company-wide quality control called for continued customer

service. This meant that a customer would continue receiving service even after receiving the product.

This service would extend across the company itself in all levels of management, and even beyond the

company to the everyday lives of those involved. According to Ishikawa, quality improvement is a

continuous process, and it can always be taken one step further. With his cause and effect diagram (also

called the "Ishikawa" or "fishbone" diagram) this management leader made significant and specific

advancements in quality improvement. With the use of this new diagram, the user can see all possible

causes of a result, and hopefully find the root of process imperfections. By pinpointing root problems, this

diagram provides quality improvement from the "bottom up." Dr. W. Edwards Deming --one of Isikawa's

colleagues -- adopted this diagram and used it to teach Total Quality Control in Japan as early as World

War II. Both Ishikawa and Deming use this diagram as one the first tools in the quality management

process.

Ishikawa also showed the importance of the seven quality tools: control chart, run chart,

histogram, scatter diagram, Pareto chart, and flowchart. Additionally, Ishikawa explored the concept of

quality circles-- a Japanese philosophy which he drew from obscurity into world wide acceptance.

.Ishikawa believed in the importance of support and leadership from top level management. He

continually urged top level executives to take quality control courses, knowing that without the support of

the management, these programs would ultimately fail. He stressed that it would take firm commitment

from the entire hierarchy of employees to reach the company's potential for success. Another area of

quality improvement that Ishikawa emphasized is quality throughout a product's life cycle -- not just

during production. Although he believed strongly in creating standards, he felt that standards were like

continuous quality improvement programs -- they too should be constantly evaluated and changed.

Standards are not the ultimate source of decision making; customer satisfaction is. He wanted managers to

consistently meet consumer needs; from these needs, all other decisions should stem. Besides his own

developments, Ishikawa drew and expounded on principles from other quality gurus, including those of

one man in particular: W. Edwards Deming, creator of the Plan-Do-Check-Act model. Ishikawa expanded

Deming's four steps into the following six:



 Determine goals and targets.

 Determine methods of reaching goals.

 Engage in education and training.

 Implement work.

 Check the effects of implementation.



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 Take appropriate action.









GENICHI TAGUCHI METHODS - PRACTICAL, RAPID QUALITY



After WWII Japanese manufacturers were struggling to survive with very limited resources. If it

were not for the advancements of Taguchi the country might not have stayed afloat let alone flourish as it

has. Taguchi revolutionized the manufacturing process in Japan through cost savings. He understood, like

many other engineers, that all manufacturing processes are affected by outside influences, noise.

However, Taguchi realized methods of identifying those noise sources which have the greatest effects on

product variability. His ideas have been adopted by successful manufacturers around the globe because of

their results in creating superior production processes at much lower costs.



Here are some of the major contributions that Taguchi has made to the quality improvement world:



 The Loss Function - Taguchi devised an equation to quantify the decline of a customer's

perceived value of a product as its quality declines. Essentially, it tells managers how much revenue

they are losing because of variability in their production process. It is a powerful tool for

projecting the benefits of a quality improvement program. Taguchi was the first person to equate

quality with cost.

 Orthogonal Arrays and Linear Graphs - When evaluating a production process analysis will

undoubtedly identify outside factors or noise which cause deviations from the mean. Isolating

these factors to determine their individual effects can be a very costly and time consuming process.

Taguchi devised a way to use orthogonal arrays to isolate these noise factors from all others in a

cost effective manner.

 Robustness - Some noise factors can be identified, isolated and even eliminated but others

cannot. For instance it is too difficult to predict and prepare for any possible weather condition.

Taguchi therefore referred to the ability of a process or product to work as intended regardless of

uncontrollable outside influences as robustness. He was pivotal in many companies' development

of products and processes which perform uniformly regardless of uncontrollable forces; an

obviously beneficial service.









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Biography



Born on the first day of 1924, Genichi Taguchi studied textile engineering at Kiryu Technical

College. After WWII he worked for the Japanese Ministry of Public Health and Welfare and conducted

the nation's first study on health and nutrition. He also applied his quality improvement knowledge at

Morinaga Pharmaceutical and even worked for a candy maker, Morinaga Sieka, to reduce the melting

properties of caramel at room temperature.



Genichi Taguchi



Genichi Taguchi ( Taguchi Genichi) (born January 1, 1924 in Tokamachi, Japan) is an engineer

and statistician. From the 1950s onwards, Taguchi developed a methodology for applying statistics to

improve the quality of manufactured goods. Taguchi methods have been controversial among some

conventional Western statisticians, but others have accepted many of the concepts introduced by him as

valid extensions to the body of knowledge. Taguchi was raised in the textile town of Tokamachi, where he

initially studied textile engineering with the intention of entering the family kimono business. However,

with the escalation of World War II, in 1942, he was drafted into the Astronomical Department of the

Navigation Institute of the Imperial Japanese Navy. After the war, in 1948, he joined the Ministry of

Public Health and Welfare, where he came under the influence of eminent statistician Matosaburo

Masuyama, who kindled his interest in the design of experiments. He also worked at the Institute of

Statistical Mathematics during this time, and supported experimental work on the production of penicillin

at Morinaga Pharmaceuticals, a Morinaga Seika company.



In 1950, he joined the Electrical Communications Laboratory (ECL) of the Nippon Telegraph

and Telephone Corporation just as statistical quality control was beginning to become popular in Japan,

under the influence of W. Edwards Deming and the Japanese Union of Scientists and Engineers. ECL

was engaged in a rivalry with Bell Labs to develop cross bar and telephone switching systems, and

Taguchi spent his twelve years there in developing methods for enhancing quality and reliability. Even at

this point, he was beginning to consult widely in Japanese industry, with Toyota being an early adopter of

his ideas. During the 1950s, he collaborated widely and in 1954-1955 was visiting professor at the Indian

Statistical Institute, where he worked with R. A. Fisher and Walter A. Shewhart.

On completing his doctorate at Kyushu University in 1962, he left ECL, though he maintained a

consulting relationship. In the same year he visited Princeton University under the sponsorship of John

Tukey, who arranged a spell at Bell Labs, his old ECL rivals. In 1964 he became professor of engineering

at Aoyama Gakuin University, Tokyo. In 1966 he began collaboration with Yuin Wu, who later emigrated

to the U.S. and, in 1980, invited Taguchi to lecture. During his visit there, Taguchi himself financed a

return to Bell Labs, where his initial teaching had made little enduring impact. This second visit began a

collaboration with Madhav Phadke and a growing enthusiasm for his methodology in Bell Labs and

elsewhere, including Ford Motor Company, Boeing, Xerox and ITT.



Since 1982, Genichi Taguchi has been an advisor to the Japanese Standards Institute and

executive director of the American Supplier Institute, an international consulting organization.



Contributions









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Taguchi methods are statistical methods developed by Genichi Taguchi to improve the quality

of manufactured goods, and more recently also applied to biotechnology, marketing and advertising.

Taguchi methods are considered controversial among some traditional Western statisticians, but others

accept many of his concepts as being useful additions to the body of knowledge.

Taguchi's principal contributions to statistics are:



1. Taguchi loss function;

2. The philosophy of off-line quality control; and

3. Innovations in the design of experiments.



Loss functions



Taguchi's reaction to the classical design of experiments methodology of R. A. Fisher was that it

was perfectly adapted for seeking to improve the mean outcome of a process. As Fisher's work had been

largely motivated by programmes to increase agricultural production, this was hardly surprising. However,

Taguchi realised that in much industrial production, there is a need to produce an outcome on target, for

example, to machine a hole to a specified diameter, or to manufacture a cell to produce a given voltage.

He also realized, as had Walter A. Shewhart and others before him, that excessive variation lays at the root

of poor manufactured quality and that reacting to individual items inside and outside specification was

counterproductive. He therefore argued that quality engineering should start with an understanding of

quality costs in various situations. In much conventional industrial engineering, the quality costs are

simply represented by the number of items outside specification multiplied by the cost of rework or scrap.

However, Taguchi insisted that manufacturers broaden their horizons to consider cost to society. Though

the short-term costs may simply be those of non-conformance, any item manufactured away from

nominal would result in some loss to the customer or the wider community through early wear-out;

difficulties in interfacing with other parts, themselves probably wide of nominal; or the need to build in

safety margins. These losses are externalities and are usually ignored by manufacturers. In the wider

economy the Coase Theorem predicts that they prevent markets from operating efficiently. Taguchi

argued that such losses would inevitably find their way back to the originating corporation (in an effect

similar to the tragedy of the commons), and that by working to minimize them, manufacturers would

enhance brand reputation, win markets and generate profits.



Such losses are, of course, very small when an item is near to nominal. Donald J. Wheeler

characterized the region within specification limits as where we deny that losses exist. As we diverge from

nominal, losses grow until the point where losses are too great to deny and the specification limit is

drawn. All these losses are, as W. Edwards Deming would describe them, unknown and unknowable, but

Taguchi wanted to find a useful way of representing them statistically. Taguchi specified three situations:



1. Larger the better (for example, agricultural yield);

2. Smaller the better (for example, carbon dioxide emissions); and

3. On-target, minimum-variation (for example, a mating part in an assembly).



The first two cases are represented by simple monotonic loss functions. In the third case, Taguchi

adopted a squared-error loss function on the following grounds:



 It is the first symmetric term in the Taylor series expansion of any reasonable, real-life loss

function, and so is a "first-order" approximation;





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 Total loss is measured by the variance. As variance is additive, it is an attractive model of cost; and

 There was an established body of statistical theory around the use of the least-squares principle.



The squared-error loss function had also been used by John von Neumann and Oskar

Morgenstern in the 1930s. Though much of this thinking is endorsed by statisticians and economists in

general, Taguchi extended the argument to insist that industrial experiments seek to maximize an

appropriate signal-to-noise ratio, representing the magnitude of the mean of a process compared to its

variation. Most statisticians believe Taguchi's signal-to-noise ratios to be effective over too narrow a range

of applications, and they are generally deprecated.





Off-line quality control



Taguchi realized that the best opportunity to eliminate variation is during the design of a product

and its manufacturing process (Taguchi's rule for manufacturing). Consequently, he developed a strategy

for quality engineering that can be used in both contexts. The process has three stages:



1. System design;

2. Parameter design; and

3. Tolerance design.



System design

This is design at the conceptual level, involving creativity and innovation.



Parameter design

Once the concept is established, the nominal values of the various dimensions and design

parameters need to be set, the detail design phase of conventional engineering. Taguchi's radical insight

was that the exact choice of values required is under-specified by the performance requirements of the

system. In many circumstances, this allows the parameters to be chosen so as to minimise the effects on

performance arising from variation in manufacture, environment and cumulative damage. This is

sometimes called robustification.



Tolerance design

With a successfully completed parameter design, and an understanding of the effect that the

various parameters have on performance, resources can be focused on reducing and controlling variation

in the critical few dimensions.



Design of experiments

Taguchi developed much of his thinking in isolation from the school of R. A. Fisher, only coming

into direct contact in 1954. His framework for design of experiments is idiosyncratic and often flawed,

but contains much that is of enormous value. He made a number of innovations.



Outer arrays

Unlike the design of experiments work of Fisher, Taguchi sought to understand the influence that

parameters had on variation, not just on the mean. He contended, as had W. Edwards Deming in his

discussion of analytic studies, that conventional sampling is inadequate here as there is no way of

obtaining a random sample of future conditions. In Fisher's work, variation between experimental



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replications is a nuisance that the experimenter would like to eliminate whereas, in Taguchi's thinking, it is

a central object of investigation. Taguchi's innovation was to replicate each experiment by means of an

outer array, possibly an orthogonal array that seeks deliberately to emulate the sources of variation that a

product would encounter in reality. This is an example of judgment sampling. Though statisticians

following in the Shewhart-Deming tradition have embraced outer arrays, many academics are still

skeptical. Later innovations in outer arrays resulted in "compounded noise". This involves combining a

few noise factors to create two levels in the outer array. First, noise factors that drive output lower, and

second, noise factors that drive output higher. This still emulates the extremes of noise variation but with

fewer test samples required.



Management of interactions

Many of the orthogonal arrays that Taguchi has advocated are saturated arrays, allowing no scope

for estimation of interactions. This is a continuing topic of controversy. However, this is only true for

"control factors" or factors in the "inner array". By combining an inner array of control factors with an

outer array of "noise factors", Taguchi's approach provides full information on control-by-noise

interactions. His concept is that those are the interactions of most interest in achieving a design that is

robust to noise factor variation. In this sense, the Taguchi approach provides more complete interaction

information than typical fractional factorial experiments.



 Followers of Taguchi argue that the designs offer rapid results and that interactions can be

eliminated by proper choice of quality characteristics and by transforming the data. That

notwithstanding, a confirmation experiment offers protection against any residual interactions. If

the quality characteristic represents the energy transformation of the system, then the likelihood of

control factor-by-control factor interactions is greatly reduced, since energy is additive.



 Western statisticians argue that interactions are part of the real world and that Taguchi's arrays

have complicated alias structures that leave interactions difficult to disentangle. George Box and

others have argued that a more effective and efficient approach is to use sequential assembly.



Analysis of experiments

Taguchi introduced many methods for analyzing experimental results including novel applications

of the analysis of variance and minute analysis. Little of this work has been validated by Western

statisticians.

Assessment

Genichi Taguchi has made seminal and valuable methodological innovations in statistics and

engineering, within the Shewhart-Deming tradition. His emphasis on loss to society, techniques for

investigating variation in experiments, and his overall strategy of system, parameter and tolerance design

have been massively influential in improving manufactured quality worldwide. Much of his work was

carried out in isolation from the mainstream of Western statistics and, while this may have facilitated his

creativity, much of the technical detail of Taguchi methods and their benefits to experimentation and

research is only now being studied in the West.





QUALITY FUNCTION DEPLOYMENT FOR COMPETITIVE ADVANTAGE



In today's business environment, companies cannot just assume they know what customers want

– they must know for sure. And once they know what customers want, businesses must then provide



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products and services to meet and exceed customers' desires. Business leaders have struggled for years to

meet this challenge. Having the ability to truly listen to the voice of the customer (VOC) and respond to

it appropriately is one good definition of a successful business, a business with a competitive advantage.

Companies which use Six Sigma employ the voice of the customer – internal and external customers – as

a key element in implementing their business strategies. So important is VOC data that no Six Sigma

project should proceed without first ensuring it is real, factual, and relevant and correlates with the goals

of the business. There is a useful and structured tool that helps to translate both spoken and unspoken

customer requirements into key business deliverables. This tool is quality function deployment (QFD).





FOCUSING ON 'POSITIVE QUALITY'



Many quality tools focus on "negative quality" – the things that disappoint the customer. One of

the key distinctions about QFD is it focuses on "positive quality" – things that delight a customer. It looks

at the items that please the customer and expands upon them. QFD is useful for cross-functional teams

which have to agree on what is important.



QFD is useful in a number of different scenarios. Some examples are when:



 A business knows the customers' requirements but does not have adequate internal measurements

relative to the requirements.

 The internal processes and practices of a business cannot meet the customers' requirements.

 A large investment is required for a new product or service.

 There is a lack of agreement within a business organization on how to proceed in delivering

customer requirements.

 There are competing alternatives for market segments.



QFD Around for Nearly 40 Years



QFD is not something new, but a tool that has been in existence for quite some time. Japanese

professors Yoji Akao and Shigeru Mizuno developed it in the late 1960s. Their goal was to develop a tool

that would design customer satisfaction into a product prior to being manufactured. Most other quality

control methods of the time focused on fixing manufacturing problems after the fact. QFD was first

introduced to America and Europe in 1983. American automotive manufacturers, Ford Motor Company

and General Motors Corporation soon adopted it. Later, other American companies such as General

Electric, IBM and AT&T started using this tool and reaping the benefits associated with it. QFD has been

successfully used in all types of industries and business functions with great success. For instance, it has

been used in sales organizations to improve their top line growth.



The most important step in doing a QFD is to properly select the team. The size of the team is

not as important as the quality of the team members. The team should be cross-functional and should

consist of all of the necessary stakeholders crucial to the team's success. In addition, it is important to

have the customer participate in the team. In doing so, the company will ensure that the customer's needs

and wants are clearly understood and addressed. The QFD process tends to be dynamic in nature. Hence

it is wise to consider changing the team members as the company cascades through the four different

houses of the QFD process.





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Figure 1: QFD Matrix (House of Quality)









Figure 2: The Four Houses of Quality









COMPLETING THE QFD PROCESS



The QFD process typically consists of four steps:



First House of Quality



House 1 is the customer house. In the customer house, the primary goal is to translate the voice

of the customer into unambiguous and clear language. A business must understand what measurements

the customer is using to determine if it has met their requirements. Next, the company must identify its

internal metrics which determine if it has met the customer requirements.

Key elements that are critical to completing the first house are:



1. Customers' needs.

2. Measurable characteristics of the customers' needs.

3. The relationship between items 1 and 2 measured in high, medium or low.

4. An understanding of how the company compares to competitors (from the customers'

perspective).

5. Competitive benchmarking.

6. Preliminary measurement targets that will meet the customers' requirements.









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Once the company has identified the key elements above, it can perform a correlation between the

measurable characteristics of the customers' needs and their relative strengths. Finally, the company

should analyze this first house to determine what improvements can be made.



Second House of Quality



House 2 is the company's house. This house is typically constructed during the Measure and

Analyze phases. The goal of completing the second house is to determine specific action items that the

company can take to meet the requirements of the customer.



Third House of Quality



House 3 is the process house and is typically constructed during the Analyze phase. The goal of

completing the third house is to determine which processes (that have data) can be used to meet the

customers' needs. It is possible that the process does not exist, so it may need to be developed.



Fourth House of Quality



House 4, the process control house, is typically constructed during the control phase. The purpose

of constructing this house is to identify the control variables that are being used to meet the customers'

needs. It is not necessary to construct all four houses every time that a QFD is performed. Judgment is

needed to determine which houses are needed.





Helping Satisfy the Customer



It should be used because it is aimed at satisfying the customer throughout the whole business

process from product/service development to delivery. It helps organizations reach agreement on

measurement systems and performance specifications that will meet customer requirements. It is

designed to improve a company's strategic competitiveness. It also prioritizes the steps that a business

must take in order to satisfy the spoken and unspoken requirements of the customer. In essence, utilizing

QFD helps businesses gain a competitive advantage.









CHAPTER III



INTRODUCTION AND IMPLEMENTATION OF TOTAL QUALITY MANAGEMENT



INTRODUCTION



Total Quality Management is a management approach that originated in the 1950's and has

steadily become more popular since the early 1980's. Total Quality is a description of the culture, attitude



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and organization of a company that strives to provide customers with products and services that satisfy

their needs. The culture requires quality in all aspects of the company's operations, with processes being

done right the first time and defects and waste eradicated from operations.

Total Quality Management, TQM, is a method by which management and employees can become

involved in the continuous improvement of the production of goods and services. It is a combination of

quality and management tools aimed at increasing business and reducing losses due to wasteful practices.

Some of the companies who have implemented TQM include Ford Motor Company, Phillips

Semiconductor, SGL Carbon, Motorola and Toyota Motor Company.1



DEFINITION



TQM is a management philosophy that seeks to integrate all organizational functions (marketing,

finance, design, engineering, and production, customer service, etc.) to focus on meeting customer needs

and organizational objectives.



TQM views an organization as a collection of processes. It maintains that organizations must

strive to continuously improve these processes by incorporating the knowledge and experiences of

workers. The simple objective of TQM is "Do the right things, right the first time, every time". TQM is

infinitely variable and adaptable. Although originally applied to manufacturing operations, and for a

number of years only used in that area, TQM is now becoming recognized as a generic management tool,

just as applicable in service and public sector organizations. There are a number of evolutionary strands,

with different sectors creating their own versions from the common ancestor. TQM is the foundation for

activities, which include:



 Commitment by senior management and all employees

 Meeting customer requirements

 Reducing development cycle times

 Just In Time/Demand Flow Manufacturing

 Improvement teams

 Reducing product and service costs

 Systems to facilitate improvement

 Line Management ownership

 Employee involvement and empowerment

 Recognition and celebration

 Challenging quantified goals and benchmarking

 Focus on processes / improvement plans

 Specific incorporation in strategic planning



This shows that TQM must be practiced in all activities, by all personnel, in Manufacturing, Marketing,

Engineering, R&D, Sales, Purchasing, HR, etc.









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PRINCIPLES OF TQM



The key principles of TQM are as following:



 Management Commitment

1. Plan (drive, direct)

2. Do (deploy, support, participate)

3. Check (review)

4. Act (recognize, communicate, revise)

 Employee Empowerment

1. Training

2. Suggestion scheme

3. Measurement and recognition

4. Excellence teams

 Fact Based Decision Making

1. SPC (statistical process control)

2. DOE, FMEA

3. The 7 statistical tools

4. TOPS (FORD 8D - Team Oriented Problem Solving)

 Continuous Improvement

1. Systematic measurement and focus on CONQ

2. Excellence teams

3. Cross-functional process management

4. Attain, maintain, improve standards

 Customer Focus

1. Supplier partnership

2. Service relationship with internal customers

3. Never compromise quality

4. Customer driven standards







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THE CONCEPT OF CONTINUOUS IMPROVEMENT BY TQM

TQM is mainly concerned with continuous improvement in all work, from high level strategic

planning and decision-making, to detailed execution of work elements on the shop floor. It stems from

the belief that mistakes can be avoided and defects can be prevented. It leads to continuously improving

results, in all aspects of work, as a result of continuously improving capabilities, people, processes, and

technology and machine capabilities.



Continuous improvement must deal not only with improving results, but more importantly with

improving capabilities to produce better results in the future. The five major areas of focus for capability

improvement are demand generation, supply generation, technology, operations and people capability. A

central principle of TQM is that mistakes may be made by people, but most of them are caused, or at

least permitted, by faulty systems and processes. This means that the root cause of such mistakes can be

identified and eliminated, and repetition can be prevented by changing the process.



There are three major mechanisms of prevention:



1. Preventing mistakes (defects) from occurring (Mistake - proofing or Poka-Yoke).

2. Where mistakes can't be absolutely prevented, detecting them early to prevent them being passed

down the value added chain (Inspection at source or by the next operation).

3. Where mistakes recur, stopping production until the process can be corrected, to prevent the

production of more defects. (Stop in time).



IMPLEMENTATION PRINCIPLES AND PROCESSES



A preliminary step in TQM implementation is to assess the organization's current reality. Relevant

preconditions have to do with the organization's history, its current needs, precipitating events leading to

TQM, and the existing employee quality of working life. If the current reality does not include important

preconditions, TQM implementation should be delayed until the organization is in a state in which TQM

is likely to succeed.



If an organization has a track record of effective responsiveness to the environment, and if it has

been able to successfully change the way it operates when needed, TQM will be easier to implement. If an

organization has been historically reactive and has no skill at improving its operating systems, there will

be both employee skepticism and a lack of skilled change agents. If this condition prevails, a

comprehensive program of management and leadership development may be instituted. A management

audit is a good assessment tool to identify current levels of organizational functioning and areas in need

of change. An organization should be basically healthy before beginning TQM. If it has significant

problems such as a very unstable funding base, weak administrative systems, lack of managerial skill, or

poor employee morale, TQM would not be appropriate.



However, a certain level of stress is probably desirable to initiate TQM. People need to feel a need

for a change. Kanter (1983) addresses this phenomenon be describing building blocks which are present

in effective organizational change. These forces include departures from tradition, a crisis or galvanizing

event, strategic decisions, individual "prime movers," and action vehicles. Departures from tradition are

activities, usually at lower levels of the organization, which occur when entrepreneurs move outside the



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normal ways of operating to solve a problem. A crisis, if it is not too disabling, can also help create a

sense of urgency which can mobilize people to act. In the case of TQM, this may be a funding cut or

threat, or demands from consumers or other stakeholders for improved quality of service. After a crisis, a

leader may intervene strategically by articulating a new vision of the future to help the organization deal

with it. A plan to implement TQM may be such a strategic decision. Such a leader may then become a

prime mover, who takes charge in championing the new idea and showing others how it will help them

get where they want to go. Finally, action vehicles are needed and mechanisms or structures to enable the

change to occur and become institutionalized.



STEPS IN MANAGING THE TRANSITION



Beckhard and Pritchard (1992) have outlined the basic steps in managing a transition to a new

system such as TQM: identifying tasks to be done, creating necessary management structures, developing

strategies for building commitment, designing mechanisms to communicate the change, and assigning

resources.



Task identification would include a study of present conditions (assessing current reality, as

described above); assessing readiness, such as through a force field analysis; creating a model of the

desired state, in this case, implementation of TQM; announcing the change goals to the organization; and

assigning responsibilities and resources. This final step would include securing outside consultation and

training and assigning someone within the organization to oversee the effort. This should be a

responsibility of top management. In fact, the next step, designing transition management structures, is

also a responsibility of top management. In fact, Cohen and Brand (1993) and Hyde (1992) assert that

management must be heavily involved as leaders rather than relying on a separate staff person or function

to shepherd the effort. An organization wide steering committee to oversee the effort may be

appropriate. Developing commitment strategies was discussed above in the sections on resistance and on

visionary leadership. To communicate the change, mechanisms beyond existing processes will need to be

developed. Special all-staff meetings attended by executives, sometimes designed as input or dialog

sessions, may be used to kick off the process, and TQM newsletters may be an effective ongoing

communication tool to keep employees aware of activities and accomplishments.



Management of resources for the change effort is important with TQM because outside

consultants will almost always be required. Choose consultants based on their prior relevant experience

and their commitment to adapting the process to fit unique organizational needs. While consultants will

be invaluable with initial training of staff and TQM system design, employees (management and others)

should be actively involved in TQM implementation, perhaps after receiving training in change

management which they can then pass on to other employees. A collaborative relationship with

consultants and clear role definitions and specification of activities must be established. In summary, first

assess preconditions and the current state of the organization to make sure the need for change is clear

and that TQM is an appropriate strategy. Leadership styles and organizational culture must be congruent

with TQM. If they are not, this should be worked on or TQM implementation should be avoided or

delayed until favorable conditions exist.



Remember that this will be a difficult, comprehensive, and long-term process. Leaders will need to

maintain their commitment, keep the process visible, provide necessary support, and hold people

accountable for results. Use input from stakeholder (clients, referring agencies, funding sources, etc.) as

possible; and, of course, maximize employee involvement in design of the system.



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Always keep in mind that TQM should be purpose driven. Be clear on the organization's vision

for the future and stay focused on it. TQM can be a powerful technique for unleashing employee

creativity and potential, reducing bureaucracy and costs, and improving service to clients and the

community. TQM encoureges participation amongst shop floor workers and managers. There is no single

theoretical formalization of total quality, but Deming, Juran and Ishikawa provide the core assumptions,

as a "...discipline and philosophy of management which institutionalizes planned and continuous...

improvement ... and assumes that quality is the outcome of all activities that take place within an

organization; that all functions and all employees have to participate in the improvement process; that

organizations need both quality systems and a quality culture.".



Total Quality Management (TQM), a buzzword phrase of the 1980's, has been killed and

resurrected on a number of occasions. The concept and principles, though simple seem to be creeping

back into existence by "bits and pieces" through the evolution of the ISO9001 Management Quality

System standard. Companies who have implemented TQM include Ford Motor Company, Phillips

Semiconductor, SGL Carbon, Motorola and Toyota Motor Company. The latest changes coming up for

the ISO 9001:2000 standard‘s "Process Model" seem to complete the embodiment. TQM is the concept

that quality can be managed and that it is a process. The following information is provided to give an

understanding of the key elements of this process.



TOTAL QUALITY MANAGEMENT (TQM)



Total = Quality involves everyone and all activities in the company.

Quality = Conformance to Requirements (Meeting Customer Requirements).

Management = Quality can and must be managed.

TQM = A process for managing quality; it must be a continuous way of life; a philosophy of perpetual

improvement in everything we do.



TQM COMPARED TO ISO 9001



ISO 9000 is a Quality System Management Standard. TQM is a philosophy of perpetual

improvement. The ISO Quality Standard sets in place a system to deploy policy and verifiable objectives.

An ISO implementation is a basis for a Total Quality Management implementation. Where there is an

ISO system, about 75 percent of the steps are in place for TQM. The requirements for TQM can be

considered ISO plus. Another aspect relating to the ISO Standard is that the proposed changes for the

next revision (1999) will contain customer satisfaction and measurement requirements. In short,

implementing TQM is being proactive concerning quality rather than reactive.



TQM AS A FOUNDATION



TQM is the foundation for activities which include;



 Meeting Customer Requirements

 Reducing Development Cycle Times

 Just In Time/Demand Flow Manufacturing





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 Improvement Teams

 Reducing Product and Service Costs

 Improving Administrative Systems Training



Total Quality Management



Total Quality Management (TQM) is a business management strategy aimed at embedding

awareness of quality in all organizational processes. TQM has been widely used in manufacturing,

education, call centers, government, and service industries, as well as NASA space and science programs.



Definition

TQM is composed of three paradigms:



Total: Involving the entire organization, supply chain, and/or product life cycle



Quality: With its usual definitions, with all its complexities [1]



Management: The system of managing with steps like Plan, Organize, Control, Lead, Staff,

provisioning and organizing[citation needed].



As defined by the International Organization for Standardization (ISO):



"TQM is a management approach for an organization, centered on quality, based on the

participation of all its members and aiming at long-term success through customer satisfaction, and

benefits to all members of the organization and to society."



One major aim is to reduce variation from every process so that greater consistency of effort is

obtained. (Royse, D., Thyer, B., Padgett D., & Logan T., 2006)



In Japan, TQM comprises four process steps, namely:



Kaizen – Focuses on "Continuous Process Improvement", to make processes visible, repeatable and

measurable.



Atarimae Hinshitsu – The idea that "things will work as they are supposed to" (for example, a pen will

write).



Kansei – Examining the way the user applies the product leads to improvement in the product itself.



Miryokuteki Hinshitsu – The idea that "things should have an aesthetic quality" (for example, a pen will

write in a way that is pleasing to the writer).[citation needed]



TQM requires that the company maintain this quality standard in all aspects of its business. This

requires ensuring that things are done right the first time and that defects and waste are eliminated from

operations.







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A comprehensive definition



Total Quality Management is the organization wide management of quality. Management consists

of planning, organizing, directing, control, and assurance. Total quality is called total because it consists of

two qualities: quality of return to satisfy the needs of the shareholders, and quality of products.



Origins



The origin of the expression Total Quality Management is unclear. "Total Quality Control" was

the key concept of Armand Feigenbaum's 1951 book, Quality Control: Principles, Practice, and

Administration. In a chapter titled "Total Quality Control" Feigenbaum grabs on to an idea that sparked

many scholars' interest in the following decades. The expression Total Quality Control existed together

with the Japanese expression "Company Wide Quality Control" (CWQC) and the differences between the

two expression was unclear. Major influencers for both expressions were W. Edwards Deming, Joseph

Juran, Philip B. Crosby, and Kaoru Ishikawa, known as the big four.



The expression Total Quality Management started to appear in the 1980s and there are two

theories of its origin:



One theory is that Total Quality Management was created as an misinterpretation from Japanese

to English since no difference exist between the words "control" and "management" in Japanese. [1].

According to William Golomski (American quality scholar and consultant, 1924-2002) was TQM first

mentioned by Koji Kobayashi at NEC (Nippon Electrical Company) in his speech when he received the

Deming Prize in 1974.



The American Society for Quality says that the term Total Quality Management was used by the

U.S. Naval Air Systems Command in 1984 to describe its Japanese-style management approach to quality

improvement since they did not like the word control in Total Quality Control. The word management

should then have been suggested by one of the employees, Nancy Warren. [3] [3] This is consistent with

the story that the United States Navy Personnel Research and Development Center began researching the

use of statistical process control (SPC), the work of Juran, Crosby, and Ishikawa, and the philosophy of

W. Edwards Deming to make performance improvements in 1984. This approach was first tested at the

North Island Naval Aviation Depot.



TEN STEPS TO TOTAL QUALITY MANAGEMENT (TQM)



The Ten Steps to TQM are as follows:



1. Pursue New Strategic Thinking

2. Know your Customers

3. Set True Customer Requirements

4. Concentrate on Prevention, Not Correction

5. Reduce Chronic Waste

6. Pursue a Continuous Improvement Strategy



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7. Use Structured Methodology for Process Improvement

8. Reduce Variation

9. Use a Balanced Approach

10. Apply to All Functions









PRINCIPLES OF TQM



The Principles of TQM are as follows:



1. Quality can and must be managed.

2. Everyone has a customer and is a supplier.

3. Processes, not people are the problem.

4. Every employee is responsible for quality.

5. Problems must be prevented, not just fixed.

6. Quality must be measured.

7. Quality improvements must be continuous.

8. The quality standard is defect free.

9. Goals are based on requirements, not negotiated.

10. Life cycle costs, not front end costs.

11. Management must be involved and lead.

12. Plan and organize for quality improvement.



THE EIGHT ELEMENTS OF TQM



Total Quality Management is a management approach that originated in the 1950's and has

steadily become more popular since the early 1980's. Total Quality is a description of the culture, attitude

and organization of a company that strives to provide customers with products and services that satisfy

their needs. The culture requires quality in all aspects of the company's operations, with processes being

done right the first time and defects and waste eradicated from operations.



To be successful implementing TQM, an organization must concentrate on the eight key elements:



1. Ethics

2. Integrity

3. Trust



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4. Training

5. Teamwork

6. Leadership

7. Recognition

8. Communication



This paper is meant to describe the eight elements comprising TQM.

KEY ELEMENTS



TQM has been coined to describe a philosophy that makes quality the driving force behind

leadership, design, planning, and improvement initiatives. For this, TQM requires the help of those eight

key elements. These elements can be divided into four groups according to their function. The groups

are:

I. Foundation - It includes: Ethics, Integrity and Trust.

II. Building Bricks - It includes: Training, Teamwork and Leadership.

III. Binding Mortar - It includes: Communication.

IV. Roof - It includes: Recognition.



I Foundation



TQM is built on a foundation of ethics, integrity and trust. It fosters openness, fairness and

sincerity and allows involvement by everyone. This is the key to unlocking the ultimate potential of TQM.

These three elements move together, however, each element offers something different to the TQM

concept.



1. Ethics - Ethics is the discipline concerned with good and bad in any situation. It is a two-

faceted subject represented by organizational and individual ethics. Organizational ethics establish a

business code of ethics that outlines guidelines that all employees are to adhere to in the performance of

their work. Individual ethics include personal rights or wrongs.



2. Integrity - Integrity implies honesty, morals, values, fairness, and adherence to the facts and

sincerity. The characteristic is what customers (internal or external) expect and deserve to receive. People

see the opposite of integrity as duplicity. TQM will not work in an atmosphere of duplicity.



3. Trust - Trust is a by-product of integrity and ethical conduct. Without trust, the framework of

TQM cannot be built. Trust fosters full participation of all members. It allows empowerment that

encourages pride ownership and it encourages commitment. It allows decision making at appropriate

levels in the organization, fosters individual risk-taking for continuous improvement and helps to ensure

that measurements focus on improvement of process and are not used to contend people. Trust is

essential to ensure customer satisfaction. So, trust builds the cooperative environment essential for TQM.



II. Bricks









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Basing on the strong foundation of trust, ethics and integrity, bricks are placed to reach the roof of

recognition. It includes:

1. Training - Training is very important for employees to be highly productive. Supervisors are

solely responsible for implementing TQM within their departments, and teaching their employees the

philosophies of TQM. Training that employees require are interpersonal skills, the ability to function

within teams, problem solving, decision making, job management performance analysis and

improvement, business economics and technical skills. During the creation and formation of TQM,

employees are trained so that they can become effective employees for the company.

2. Teamwork - To become successful in business, teamwork is also a key element of TQM. With

the use of teams, the business will receive quicker and better solutions to problems. Teams also provide

more permanent improvements in processes and operations. In teams, people feel more comfortable

bringing up problems that may occur, and can get help from other workers to find a solution and put into

place. There are mainly three types of teams that TQM organizations adopt:



A. Quality Improvement Teams or Excellence Teams (QITS) - These are temporary teams

with the purpose of dealing with specific problems that often re-occur. These teams are set up for

period of three to twelve months.



B. Problem Solving Teams (PSTs) - These are temporary teams to solve certain problems and

also to identify and overcome causes of problems. They generally last from one week to three

months.



C. Natural Work Teams (NWTs) - These teams consist of small groups of skilled workers who

share tasks and responsibilities. These teams use concepts such as employee involvement teams,

self-managing teams and quality circles. These teams generally work for one to two hours a week.



3. Leadership - It is possibly the most important element in TQM. It appears everywhere in

organization. Leadership in TQM requires the manager to provide an inspiring vision, make strategic

directions that are understood by all and to instill values that guide subordinates. For TQM to be

successful in the business, the supervisor must be committed in leading his employees. A supervisor must

understand TQM, believe in it and then demonstrate their belief and commitment through their daily

practices of TQM. The supervisor makes sure that strategies, philosophies, values and goals are

transmitted down through out the organization to provide focus, clarity and direction. A key point is that

TQM has to be introduced and led by top management. Commitment and personal involvement is

required from top management in creating and deploying clear quality values and goals consistent with

the objectives of the company and in creating and deploying well defined systems, methods and

performance measures for achieving those goals.



III. Binding Mortar



1. Communication - It binds everything together. Starting from foundation to roof of the TQM

house, everything is bound by strong mortar of communication. It acts as a vital link between all elements

of TQM. Communication means a common understanding of ideas between the sender and the receiver.

The success of TQM demands communication with and among all the organization members, suppliers

and customers. Supervisors must keep open airways where employees can send and receive information

about the TQM process. Communication coupled with the sharing of correct information is vital. For





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communication to be credible the message must be clear and receiver must interpret in the way the

sender intended.

There are different ways of communication such as:

A. Downward communication - This is the dominant form of communication in an

organization. Presentations and discussions basically do it. By this the supervisors are able

to make the employees clear about TQM.

B. Upward communication - By this the lower level of employees are able to provide

suggestions to upper management of the affects of TQM. As employees provide insight

and constructive criticism, supervisors must listen effectively to correct the situation that

comes about through the use of TQM. This forms a level of trust between supervisors

and employees. This is also similar to empowering communication, where supervisors

keep open ears and listen to others.

C. Sideways communication - This type of communication is important because it breaks

down barriers between departments. It also allows dealing with customers and suppliers in

a more professional manner.

IV. Roof

1. Recognition - Recognition is the last and final element in the entire system. It should be

provided for both suggestions and achievements for teams as well as individuals. Employees strive to

receive recognition for themselves and their teams. Detecting and recognizing contributors is the most

important job of a supervisor. As people are recognized, there can be huge changes in self-esteem,

productivity, quality and the amount of effort exhorted to the task at hand. Recognition comes in its best

form when it is immediately following an action that an employee has performed. Recognition comes in

different ways, places and time such as,

1. Ways - It can be by way of personal letter from top management. Also by award banquets,

plaques, trophies etc.

2. Places - Good performers can be recognized in front of departments, on performance boards and

also in front of top management.

3. Time - Recognition can given at any time like in staff meeting, annual award banquets, etc.

We can conclude that these eight elements are key in ensuring the success of TQM in an organization and

that the supervisor is a huge part in developing these elements in the work place. Without these elements,

the business entities cannot be successful TQM implementers. It is very clear from the above discussion

that TQM without involving integrity, ethics and trust would be a great remiss, in fact it would be

incomplete. Training is the key by which the organization creates a TQM environment. Leadership and

teamwork go hand in hand. Lack of communication between departments, supervisors and employees

create a burden on the whole TQM process. Last but not the least, recognition should be given to people

who contributed to the overall completed task. Hence, lead by example, train employees to provide a

quality product, create an environment where there is no fear to share knowledge, and give credit where

credit is due is the motto of a successful TQM organization.

QUALITY MANAGEMENT



Quality management is a method for ensuring that all the activities necessary to design, develop

and implement a product or service are effective and efficient with respect to the system and its

performance. Quality management can be considered to have three main components: quality control,

quality assurance and quality improvement. Quality management is focused not only on product quality, but

also the means to achieve it. Quality management therefore uses quality assurance and control of processes

as well as products to achieve more consistent quality.





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Quality management evolution



Quality management is not a recent phenomenon. Advanced civilizations that supported the arts

and crafts allowed clients to choose goods meeting higher quality standards than normal goods. In societies

where art and craft (and craftsmanship) were valued, one of the responsibilities of a master craftsman (and

similarly for artists) was to lead their studio, train and supervise the work of their craftsmen and apprentices.

The master craftsman set standards, reviewed the work of others and ordered rework and revision as

necessary. One of the limitations of the craft approach was that relatively few goods could be produced; on

the other hand an advantage was that each item produced could be individually shaped to suit the client.

This craft based approach to quality and the practices used were major inputs when quality management was

created as a management science.



During the industrial revolution, the importance of craftsmen was diminished as mass production

and repetitive work practices were instituted. The aim was to produce large numbers of the same goods. The

first proponent in the US for this approach was Eli Whitney who proposed (interchangeable) parts

manufacture for muskets, hence producing the identical components and creating a musket assembly line.

The next step forward was promoted by several people including Frederick Winslow Taylor a mechanical

engineer who sought to improve industrial efficiency. He is sometimes called "the father of scientific

management." He was one of the intellectual leaders of the Efficiency Movement and part of his approach

laid a further foundation for quality management, including aspects like standardization and adopting

improved practices. Henry Ford also was important in bringing process and quality management practices

into operation in his assembly lines. In Germany, Karl Friedrich Benz, often called the inventor of the

motor car, was pursuing similar assembly and production practices, although real mass production was

properly initiated in Volkswagen after world war two. From this period onwards, North American

companies focused predominantly upon production against lower cost with increased efficiency.



Walter A. Shewhart made a major step in the evolution towards quality management by creating a

method for quality control for production, using statistical methods, first proposed in 1924. This became

the foundation for his ongoing work on statistical quality control. W. Edwards Deming later applied

statistical process control methods in the United States during World War II, thereby successfully improving

quality in the manufacture of munitions and other strategically important products.



Quality leadership from a national perspective has changed over the past five to six decades. After

the second world war, Japan decided to make quality improvement a national imperative as part of

rebuilding their economy, and sought the help of Shewhart, Deming and Juran, amongst others. W.

Edwards Deming championed Shewhart's ideas in Japan from 1950 onwards. He is probably best known

for his management philosophy establishing quality, productivity, and competitive position. He has

formulated 14 points of attention for managers, which are a high level abstraction of many of his deep

insights. They should be interpreted by learning and understanding the deeper insights and include:



 Break down barriers between departments

 Management should learn their responsibilities, and take on leadership

 Improve constantly

 Institute a programme of education and self-improvement



In the 1950s and 1960s, Japanese goods were synonymous with cheapness and low quality, but

over time their quality initiatives began to be successful, with Japan achieving very high levels of quality in

products from the 1970s onward. For example, Japanese cars regularly top the J.D. Power customer





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satisfaction ratings. In the 1980s Deming was asked by Ford Motor Company to start a quality initiative

after they realized that they were falling behind Japanese manufacturers. A number of highly successful

quality initiatives have been invented by the Japanese (see for example on this page: Taguchi, QFD,

Toyota Production System. Many of the methods not only provide techniques but also have associated

quality culture aspects (i.e. people factors). These methods are now adopted by the same western

countries that decades earlier derided Japanese methods.



Customers recognize that quality is an important attribute in products and services. Suppliers

recognize that quality can be an important differentiator between their own offerings and those of

competitors (quality differentiation is also called the quality gap). In the past two decades this quality gap

has been greatly reduced between competitive products and services. This is partly due to the contracting

(also called outsourcing) of manufacture to countries like India and China, as well internationalization of

trade and competition. These countries amongst many others have raised their own standards of quality

in order to meet International standards and customer demands. The ISO 9000 series of standards are

probably the best known International standards for quality management.



There are a huge number of books available on quality. In recent times some themes have become

more significant including quality culture, the importance of knowledge management, and the role of

leadership in promoting and achieving high quality. Disciplines like systems thinking are bringing more

holistic approaches to quality so that people, process and products are considered together rather than

independent factors in quality management.



QUALITY IMPROVEMENT

There are many methods for quality improvement. These cover product improvement, process

improvement and people based improvement. In the following list are methods of quality management

and techniques that incorporate and drive quality improvement—

ISO 9004:2000 — Guidelines for performance improvement.



ISO 15504-4: 2005 — Information technology — Process assessment — Part 4: Guidance on use for

process improvement and process capability determination.



QFD — Quality Function Deployment, also known as the House of Quality approach.



Kaizen — Japanese for change for the better; the common English usage is continual improvement.



Zero Defect Program — created by NEC Corporation of Japan, based upon Statistical Process

Control and one of the inputs for the inventors of Six Sigma.



Six Sigma — 6σ, Six Sigma combines established methods such as Statistical Process Control, Design

of Experiments and FMEA in an overall framework.



PDCA — Plan, Do, Check, Act cycle for quality control purposes. (Six Sigma's DMAIC method

(Design, Measure, Analyze, Improve, Control) may be viewed as a particular implementation of

this.)



Quality circle — a group (people oriented) approach to improvement.







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Taguchi methods — statistical oriented methods including Quality robustness, Quality loss function

and Target specifications.



The Toyota Production System — reworked in the west into Lean Manufacturing.



Kansei Engineering — an approach that focuses on capturing customer emotional feedback about

products to drive improvement.



TQM — Total Quality Management is a management strategy aimed at embedding awareness of

quality in all organizational processes. First promoted in Japan with the Deming prize which was

adopted and adapted in USA as the Malcolm Baldrige National Quality Award and in Europe as

the European Foundation for Quality Management award (each with their own variations).



TRIZ — meaning "Theory of inventive problem solving"



BPR — Business process reengineering, a management approach aiming at 'clean slate' improvements

(That is, ignoring existing practices).



Proponents of each approach have sought to improve them as well as apply them to enterprise types

not originally targeted. For example, Six Sigma was designed for manufacturing but has spread to service

enterprises. Each of these approaches and methods has met with success but also with failures.

Some of the common differentiators between success and failure include commitment, knowledge

and expertise to guide improvement, scope of change/improvement desired (Big Bang type changes tend

to fail more often compared to smaller changes) and adaption to enterprise cultures. For example, quality

circles do not work well in every enterprise (and are even discouraged by some managers), and relatively

few TQM-participating enterprises have won the national quality awards.



There has been a well publicized failure of BPR, as well as Six Sigma. Enterprises therefore need

to consider carefully which quality improvement methods to adopt, and certainly should not adopt all

those listed here.



It is important not to underestimate the people factors, such as culture, in selecting a quality

improvement approach. Any improvement (change) takes time to implement, gain acceptance and

stabilize as accepted practice. Improvement must allow pauses between implementing new changes so

that the change is stabilized and assessed as a real improvement, before the next improvement is made

(hence continual improvement, not continuous improvement).



Improvements that change the culture take longer as they have to overcome greater resistance to

change. It is easier and often more effective to work within the existing cultural boundaries and make

small improvements (that is Kaizen) than to make major transformational changes. Use of Kaizen in

Japan was a major reason for the creation of Japanese industrial and economic strength.



On the other hand, transformational change works best when an enterprise faces a crisis and

needs to make major changes in order to survive. In Japan, the land of Kaizen, Carlos Ghosn led a

transformational change at Nissan Motor Company which was in a financial and operational crisis. Well

organized quality improvement programs take all these factors into account when selecting the quality

improvement methods.



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Quality Standards



The International Organization for Standardization (ISO) created the Quality Management System

(QMS) standards in 1987. These were the ISO 9000:1987 series of standards comprising ISO 9001:1987,

ISO 9002:1987 and ISO 9003:1987; which were applicable in different types of industries, based on the type

of activity or process: designing, production or service delivery. The standards have been regularly reviewed

every few years by the International Organization for Standardization. The version in 1994 and was called

the ISO 9000:1994 series; comprising of the ISO 9001:1994, 9002:1994 and 9003:1994 versions.



The last revision was in the year 2000 and the series was called ISO 9000:2000 series. However the

ISO 9002 and 9003 standards were integrated and one single certifiable standard was created under ISO

9001:2000. Since December 2003, ISO 9002 and 9003 standards are not valid, and the organizations

previously holding these standards need to do a transition from the old to the new standards. The ISO

9004:2000 document gives guidelines for performance improvement over and above the basic standard

(ISO 9001:2000). This standard provides a measurement framework for improved quality management,

similar to and based upon the measurement framework for process assessment.



The Quality Management System standards created by ISO are meant to certify the processes and

the system of an organization and not the product or service itself. ISO 9000 standards do not certify the

quality of the product or service.



Recently the International Organization for Standardization released a new standard, ISO 22000,

meant for the food industry. This standard covers the values and principles of ISO 9000 and the HACCP

standards. It gives one single integrated standard for the food industry and is expected to become more

popular in the coming years in such industry. ISO has a number of standards that support quality

management. One group describes processes (including ISO 12207 & ISO 15288) and another describes

process assessment and improvement ISO 15504.



The Software Engineering Institute has its own process assessment and improvement methods,

called CMMi (Capability Maturity Model — integrated) and IDEAL respectively.



QUALITY TERMS

Quality Improvement can be distinguished from Quality Control in that Quality Improvement is

the purposeful change of a process to improve the reliability of achieving an outcome.

Quality Control is the ongoing effort to maintain the integrity of a process to maintain the

reliability of achieving an outcome.

Quality Assurance is the planned or systematic actions necessary to provide enough confidence

that a product or service will satisfy the given requirements for quality.



Quality Assurance



Quality assurance, or QA for short, refers to planned and systematic production processes that

provide confidence in a product's suitability for its intended purpose. It is a set of activities intended to

ensure that products (goods and/or services) satisfy customer requirements in a systematic, reliable

fashion. QA cannot absolutely guarantee the production of quality products, unfortunately, but makes

this more likely. Two key principles characterize QA: "fit for purpose" (the product should be suitable for

the intended purpose) and "right first time" (mistakes should be eliminated). QA includes regulation of





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the quality of raw materials, assemblies, products and components; services related to production; and

management, production and inspection processes.



It is important to realize also that quality is determined by the intended users, clients or customers,

not by society in general: it is not the same as 'expensive' or 'high quality'. Even lowly bottom-of-the-

range goods can be considered quality items if they meet a market need. Early efforts to control the quality of

production. When the first specialized craftsmen started manufacturing tools and materials for others to

purchase and use, the principle of quality was simple: "let the buyer beware" (caveat emptor). Early civil

engineering projects needed to be built from specifications, for example the four sides of the base of the

Great Pyramid of Giza were required to be perpendicular to within 3.5 arc seconds. During the middle

Ages, guilds adopted responsibility for quality control of their members, setting and maintaining certain

standards for guild membership.



Royal governments purchasing material were interested in quality control as customers. For this reason,

King John of England appointed William Wrotham to report about the construction and repair of ships.

Centuries later, Samuel Pepys, Secretary to the British Admiralty, appointed multiple such overseers.

Prior to the extensive division of labor and mechanization resulting from the Industrial

Revolution, it was possible for workers to control the quality of their own products. Working conditions

then were arguably more conducive to professional pride.



The Industrial Revolution led to a system in which large groups of people performing a similar

type of work were grouped together under the supervision of a foreman who was appointed to control

the quality of work manufactured.



Wartime production



Around the time of World War I, manufacturing processes typically became more complex with

larger numbers of workers being supervised. This period saw the widespread introduction of mass

production and piecework, which created problems as workmen could now earn more money by the

production of extra products, which in turn led to bad workmanship being passed on to the assembly

lines.



To counter bad workmanship, full time inspectors were introduced into the factory to identify

quarantine and ideally correct product quality failures. Quality control by inspection in the 1920s and

1930s led to the growth of quality inspection functions, separately organised from production and big

enough to be headed by superintendents.



The systematic approach to quality started in industrial manufacture during the 1930s, mostly in

the USA, when some attention was given to the cost of scrap and rework. With the impact of mass

production, which was required during the Second World War, it became necessary to introduce a more

appropriate form of quality control which can be identified as Statistical Quality Control, or SQC. Some

of the initial work for SQC is credited to Walter A. Shewhart of Bell Labs, starting with his famous one-

page memorandum of 1924.



SQC came about with the realization that quality cannot be fully inspected into an important

batch of items. By extending the inspection phase and making inspection organizations more efficient, it



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provides inspectors with control tools such as sampling and control charts, even where 100 per cent

inspection is not practicable. Standard statistical techniques allow the producer to sample and test a

certain proportion of the products for quality to achieve the desired level of confidence in the quality of

the entire batch or production run.



Postwar



In the period following World War II, many countries' manufacturing capabilities that had been

destroyed during the war were rebuilt. The U.S. sent General Douglas MacArthur to oversee the re-

building of Japan. During this time, General MacArthur involved two key individuals in the development

of modern quality concepts: W. Edwards Deming and Joseph Juran. Both individuals promoted the

collaborative concepts of quality to Japanese business and technical groups, and these groups utilized

these concepts in the redevelopment of the Japanese economy.



Although there were many individuals trying to lead United States industries towards a more

comprehensive approach to quality, the U.S. continued to apply the QC concepts of inspection and

sampling to remove defective product from production lines, essentially ignoring advances in QA for

decades.



QUALITY ASSURANCE VERSUS QUALITY CONTROL



Whereas quality control emphasizes testing and blocking the release of defective products, quality

assurance is about improving and stabilizing production and associated processes to avoid or at least

minimize issues that led to the defects in the first place. However, QA does not necessarily eliminate the

need for QC: some product parameters are so critical that testing is still necessary just in case QA fails.



Failure Testing



A valuable process to perform on a whole consumer product is failure testing, the operation of a

product until it fails, often under stresses such as increasing vibration, temperature and humidity. This

exposes many unanticipated weaknesses in a product, and the data is used to drive engineering and

manufacturing process improvements. Often quite simple changes can dramatically improve product

service, such as changing to mould-resistant paint or adding lock-washer placement to the training for

new assembly personnel.



Statistical Control



Many organizations use statistical process control to bring the organization to Six Sigma levels of

quality, in other words, so that the likelihood of an unexpected failure is confined to six standard

deviations on the normal distribution. This probability is less than four one-millionths. Items controlled

often include clerical tasks such as order-entry as well as conventional manufacturing tasks.



Traditional statistical process controls in manufacturing operations usually proceed by randomly

sampling and testing a fraction of the output. Variances in critical tolerances are continuously tracked and

where necessary corrected before bad parts are produced.





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Total Quality Control



Deep analysis of QA practices and premises used about them is the most necessary inspection

control of all in cases where, despite statistical quality control techniques or quality improvements

implemented, sales decrease.



The major problem which leads to a decrease in sales was that the specifications did not include

the most important factor, ―What the specifications have to state in order to satisfy the customer

requirements?‖. The major characteristics, ignored during the search to improve manufacture and overall

business performance were:



 Reliability

 Maintainability

 Safety

 Strength



As the most important factor had been ignored, a few refinements had to be introduced:



1. Marketing had to carry out their work properly and define the customer‘s specifications.

2. Specifications had to be defined to conform to these requirements.

3. Conformance to specifications i.e. drawings, standards and other relevant documents, were

introduced during manufacturing, planning and control.

4. Management had to confirm all operators are equal to the work imposed on them and holidays,

celebrations and disputes did not affect any of the quality levels.

5. Inspections and tests were carried out, and all components and materials, bought in or otherwise,

conformed to the specifications, and the measuring equipment was accurate, this is the

responsibility of the QA/QC department.

6. Any complaints received from the customers were satisfactorily dealt with in a timely manner.

7. Feedback from the user/customer is used to review designs.

8. Consistent data recording and assessment and documentation integrity.

9. Product and/or process change management and notification.



If the specification does not reflect the true quality requirements, the product's quality cannot be

guaranteed. For instance, the parameters for a pressure vessel should cover not only the material and

dimensions but operating, environmental, safety, reliability and maintainability requirements.



QUALITY AWARENESS



Widespread awareness of quality issues throughout the organization increases the probability that

product quality will be taken into account at every stage of the production process.





QA in Software Development

The following are examples of QA models relating to the software development process.









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ISO 17025

ISO 17025 is an international standard that specifies the general requirements for the competence

to carry out tests and or calibrations. There are 15 management requirements and 10 technical

requirements. These requirements outline what a laboratory must do to become accredited. Management

system refers to the organization's structure for managing its processes or activities that transform inputs

of resources into a product or service which meets the organization's objectives, such as satisfying the

customer's quality requirements, complying with regulations, or meeting environmental objectives. The

CMMI (Capability Maturity Model Integration) model is widely used to implement Quality Assurance

(PPQA) in an organization. The CMMI maturity levels can be divided in to 5 steps, which a company can

achieve by performing specific activities within the organization.



COMPANY QUALITY



During the 1980s, the concept of ―company quality‖ with the focus on management and people

came to the fore. It was realized that, if all departments approached quality with an open mind, success

was possible if the management led the quality improvement process. The company-wide quality

approach places an emphasis on four aspects :-



1. Elements such as controls, job management, adequate processes, performance and integrity

criteria and identification of records

2. Competence such as knowledge, skills, experience, qualifications

3. Soft elements, such as personnel integrity, confidence, organizational culture, motivation, team

spirit and quality relationships.

4. Infrastructure (as it enhances or limits functionality)



The quality of the outputs is at risk if any of these aspects is deficient in any way. The approach to

quality management given here is therefore not limited to the manufacturing theatre only but can be

applied to any business or non-business activity:



 Design work

 Administrative services

 Consulting

 Banking

 Insurance

 Computer software

 Retailing

 Transportation

 open source development

 education



It comprises a quality improvement process, which is generic in the sense it can be applied to any

of these activities and it establishes a behaviour pattern, which supports the achievement of quality. This

in turn is supported by quality management practices which can include a number of business systems and

which are usually specific to the activities of the business unit concerned. In manufacturing and

construction activities, these business practices can be equated to the models for quality assurance defined

by the International Standards contained in the ISO 9000 series and the specified Specifications for

quality systems. Still, in the system of Company Quality, the work being carried out was shop floor





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inspection which did not reveal the major quality problems. This led to quality assurance or total quality

control, which has come into being recently.









APPLYING TOTAL QUALITY MANAGEMENT IN ACADEMICS





The concept of Total Quality Management (TQM) was developed by an American, W. Edwards

Deming, after World War II for improving the production quality of goods and services. The concept was

not taken seriously by Americans until the Japanese, who adopted it in 1950 to resurrect their postwar

business and industry, used it to dominate world markets by 1980. By then most U.S. manufacturers had

finally accepted that the nineteenth century assembly line factory model was outdated for the modern

global economic markets. The concept of TQM is applicable to academics. Many educators believe that

the Deming's concept of TQM provides guiding principles for needed educational reform. In his article,

"The Quality Revolution in Education," John Jay Bonstingl outlines the TQM principles he believes are

most salient to education reform. He calls them the "Four Pillars of Total Quality Management."



Principle #1: Synergistic Relationships



According to this principle, an organization must focus, first and foremost, on its suppliers and

customers. In a TQM organization, everyone is both a customer and supplier; this confusing concept

emphasizes "the systematic nature of the work in which all are involved". In other words, teamwork and

collaboration are essential. Traditionally, education has been prone to individual and departmental

isolation. However, according to Bonstingl, this outdated practice no longer serves us: "When I close the

classroom door, those kids are mine!" is a notion too narrow to survive in a world in which teamwork

and collaboration result in high-quality benefits for the greatest number of people. The very application

of the first pillar of TQM to education emphasizes the synergistic relationship between the "suppliers"

and "customers". The concept of synergy suggests that performance and production is enhanced by

pooling the talent and experience of individuals. In a classroom, teacher-student teams are the equivalent

of industry's front-line workers. The product of their successful work together is the development of the

student's capabilities, interests, and character. In one sense, the student is the teacher's customer, as the

recipient of educational services provided for the student's growth and improvement. Viewed in this way,

the teacher and the school are suppliers of effective learning tools, environments, and systems to the

student, who is the school's primary customer. The school is responsible for providing for the long-term





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educational welfare of students by teaching them how to learn and communicate in high-quality ways,

how to access quality in their own work and in that of others, and how to invest in their own lifelong and

life-wide learning processes by maximizing opportunities for growth in every aspect of daily life. In

another sense, the student is also a worker, whose product is essentially his or her own continuous

improvement and personal growth.



Principle #2: Continuous Improvement and Self Evaluation



The second pillar of TQM applied to education is the total dedication to continuous

improvement, personally and collectively. Within a Total Quality school setting, administrators work

collaboratively with their customers: teachers. Gone are the vestiges of "Scientific management"... whose

watchwords were compliance, control and command. The foundations for this system were fear,

intimidation, and an adversarial approach to problem-solving. Today it is in our best interest to encourage

everyone's potential by dedicating ourselves to the continual improvement of our own abilities and those

of the people with whom we work and live. Total Quality is, essentially, a win-win approach which works

to everyone's ultimate advantage.



According to Deming, no human being should ever evaluate another human being. Therefore,

TQM emphasizes self-evaluation as part of a continuous improvement process. In addition, this principle

also laminates to the focusing on students' strengths, individual learning styles, and different types of

intelligences.



Principle #3: A System of Ongoing Process



The third pillar of TQM as applied in academics is the recognition of the organization as a system

and the work done within the organization must be seen as an ongoing process. The primary implication

of this principle is that individual students and teachers are less to blame for failure than the system in

which they work. Quality speaks to working on the system, which must be examined to identify and

eliminate the flawed processes that allow its participants to fail. Since systems are made up of processes,

the improvements made in the quality of those processes largely determine the quality of the resulting

product. In the new paradigm of learning, continual improvement of learning processes based on learning

outcomes replaces the outdated "teach and test" mode.



Principle #4: Leadership



The fourth TQM principle applied to education is that the success of TQM is the responsibility of

top management. The school teachers must establish the context in which students can best achieve their

potential through the continuous improvement that results from teachers and students working together.

Teachers who emphasize content area literacy and principle-centered teaching provide the leadership,

framework, and tools necessary for continuous improvement in the learning process. According to the

practical evidences, the TQM principles help the schools in following clauses:

(a) Redefine the role, purpose and responsibilities of schools.

(b). Improve schools as a "way of life."

(c). Plan comprehensive leadership training for educators at all levels.

(d). Create staff development that addresses the attitudes and beliefs of school staff.

(e). Use research and practice-based information to guide both policy and practice.



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(f). Design comprehensive child-development initiatives that cut across a variety of agencies and

institutions.



In order to achieve the above as opportunities to the academic scenario, in addition to patience,

participatory management among well-trained and educated partners is crucial to the success of TQM in

education; everyone involved must understand and believe in principles. Some personnel who are

committed to the principles can facilitate success with TQM. Their vision and skills in leadership,

management, interpersonal communication, problem solving and creative cooperation are important

qualities for successful implementation of TQM.



Improving Financial Services through TQM: A Case Study



The work described in this case study was undertaken in a young, rapidly expanding company in

the financial services sector with no previous experience with Total Quality Management (TQM). The

quality project began with a two-day introductory awareness program covering concepts, cases,

implementation strategies and imperatives of TQM. The program was conducted for the senior

management team of the company. This program used interactive exercises and real life case studies to

explain the concepts of TQM and to interest them in committing resources for a demonstration project.

The demonstration project, which used the Seven Steps of Problem Solving (similar to DMAIC), was

to show them how TQM concepts worked in practice before they committed resources for a company-

wide program.



Step 1. Define the Problem



1.1) Selecting the theme: A meeting of the senior management of the company was held.

Brainstorming produced a list of more than 20 problems. The list was prioritized using the weighted

average table, followed by a structured discussion to arrive at a consensus on the two most important

themes -- customer service and sales productivity.

Under the customer service theme, "Reducing the Turnaround Time from an Insurance Proposal to

Policy" was selected as the most obvious and urgent problem. The company was young, and therefore

had few claims to process so far. The proposal-to-policy process therefore impacted the greatest number

of customers.



An appropriate cross functional group was set up to tackle this problem.



1.2) Problem = customer desire – current status:

Current status: What did the individual group members think the turnaround is currently? As

each member began thinking questions came up. "What type of policies do we address?" Medical policies

or non-medical? The latter are take longer because of the medical examination of the client required.

"Between what stages do we consider turnaround?" Perceptions varied, with each person thinking about

the turnaround within their department. The key process stages were mapped:









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Several sales branches in different parts of the country sent proposals into the Central Processing

Center. After considerable debate it was agreed at first to consider turnaround between entry into the

computer system at the Company Sales Branch and dispatch to the customer from the Central Processing

Center (CPC). Later the entire cycle could be included. The perception of the length of turnaround by

different members of the team was recorded. It averaged:

Non-Medical Policies 17 days



Medical Policies 35 days



Invoking the slogan from the awareness program "In God we trust, the rest of us bring data" the

group was asked to collect data and establish reality. Armed with a suitably designed check sheet they set

about the task.



Customer desire: What was the turnaround desired by the customer? Since a customer survey was not

available, individual group members were asked to think as customers -- imagine they had just given a

completed proposal form to a sales agent. When would they expect the policy in hand? From the

customer's point of view they realized that they did not differentiate between medical and non-medical

policies. Their perception averaged out six days for the required turnaround.



"Is this the average time or maximum time that you expect?" they were asked. "Maximum," they

responded. It was clear therefore that the average must be less than six days. The importance of

"variability" had struck home. The concept of sigma was explained and was rapidly internalized. For 99.7

percent delivery within the customer limit the metric was defined.



Average+3 Sigma turnaround = less than 6 days

Current status:

Non-medical policies (Average 19/Sigma 15) Average+3 sigma= 64 days

Medical (Average 37/Sigma 27) Average+3 sigma= 118 days

The Problem was therefore defined:

Reduce Average+3 sigma of turnaround for:

Non-Medical Policies From 64 to 6 days



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Medical Policies From 118 to 6 days

The performance requirement appeared daunting. Therefore the initial target taken in the Mission Sheet

(project charter) was to reduce the turnaround by 50 percent -- to 32 and 59 days respectively.



Step 2. Analysis of the Problem

In a session the factors causing large turnaround times from the principles of JIT were explained. These

were: Input arrival patterns



 Waiting times in process

 - Batching of work

 - Imbalanced processing line

 - Too many handovers

 - Non-value added activities, etc.

 Processing times

 Scheduling

 Transport times

 Deployment of manpower



Typically it was found that waiting times constitute the bulk of processing turnaround times. Process

Mapping (Value Stream Mapping in Lean) was undertaken. The aggregate results are summarized below:

Number of operations 84

Number of handovers 13

In-house processing time (estimated) 126 man-mins.

Range of individual stage time 2 to 13 mins.



Could this be true? Could the turnaround be 126 minutes for internal processing without waiting?

The group started to question of the status quo. The change process had begun. To check this estimate it

was decided to collect data -- run two policies without waiting and record the time at each stage. The trial

results amazed everyone: Policy No. 1 took 100 minutes and Policy No. 2 took 97 minutes. Almost

instantly the mindset changed from doubt to desire: "Why can't we process every proposal in this way?"



Step 3. Generating Ideas



In the introductory program of TQM during the JIT session the advantages of flow versus batch

processing had been dramatically demonstrated using a simple exercise. Using that background a balanced

flow line was designed as follows:

1. Determine the station with the maximum time cycle which cannot be split up by reallocation -- 8

minutes.

2. Balance the line to make the time taken at each stage equal 8 minutes as far as possible.

3. Reduce the stages and handovers -- 13 to 8.

4. Eliminate non-value added activities -- transport -- make personnel sit next to each other.

5. Agree processing to be done in batch of one proposal.



Changing the mindset of the employees so they will accept and welcome change is critical to

building a self-sustaining culture of improvement. In this case, the line personnel were involved in a





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Quality Mindset Program so that they understood the reasons for change and the concepts behind them

and are keen to experiment with new methods of working. The line was ready for a test run.





Step 4. Testing the Idea



Testing in stages is a critical stage. It allows modification of ideas based upon practical experience

and equally importantly ensures acceptance of the new methods gradually by the operating personnel.



Stage 1: Run five proposals flowing through the system and confirm results. The test produced the

following results:

Average turnaround time: 4 for non

safety failure modes and >1 when the severity-number from step 1 is 9 or 10). This step is called the

detailed development section of the FMEA process.



Step 3: Detection









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When appropriate actions are determined, it is necessary to test their efficiency. Also a design

verification is needed. The proper inspection methods need to be chosen. First, an engineer should look

at the current controls of the system, that prevent failure modes from occurring or which detect the

failure before it reaches the customer. Hereafter one should identify testing, analysis, monitoring and

other techniques that can be or have been used on similar systems to detect failures. From these controls

an engineer can learn how likely it is for a failure to be identified or detected. Each combination from the

previous 2 steps, receives a detection number(D). This number represents the ability of planned tests

and inspections at removing defects or detecting failure modes.

After these 3 basic steps, Risk Priority Numbers (RPN) are calculated.



RISK PRIORITY NUMBERS



RPN do not play an important part in the choice of an action against failure modes. They are

more threshold values in the evaluation of these actions.

After ranking the severity, occurrence and detectability the RPN can be easily calculated by multiplying

these 3 numbers: RPN = S x O x D



This has to be done for the entire process and/or design. Once this is done it is easy to determine

the areas of greatest concern. The failure modes that have the highest RPN should be given the highest

priority for corrective action. This means it is not always the failure modes with the highest severity

numbers that should be treated first. There could be less severe failures, but which occur more often and

are less detectable.



After these values are allocated, recommended actions with targets, responsibility and dates of

implementation are noted. These actions can include specific inspection, testing or quality procedures,

redesign (such as selection of new components), adding more redundancy and limiting environmental

stresses or operating range. Once the actions have been implemented in the design/process, the new

RPN should be checked, to confirm the improvements. These tests are often put in graphs, for easy

visualisation. Whenever a design or a process changes, an FMEA should be updated.

A few logical but important thoughts come in mind:



 Try to eliminiate the failure mode (some failures are more preventable than others)

 Minimize the severity of the failure

 Reduce the occurrence of the failure mode

 Improve the detection



TIMING OF FMEA



The FMEA should be updated whenever:



 At the beginning of a cycle (new product/process)

 Changes are made to the operating conditions

 A change is made in the design

 New regulations are instituted

 Customer feedback indicates a problem



USES OF FMEA



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 Development of system requirements that minimize the likelihood of failures.

 Development of methods to design and test systems to ensure that the failures have been

eliminated.

 Evaluation of the requirements of the customer to ensure that those do not give rise to potential

failures.

 Identification of certain design characteristics that contribute to failures, and minimize or eliminate

those effects.

 Tracking and managing potential risks in the design. This helps avoid the same failures in future

projects.

 Ensuring that any failure that could occur will not injure the customer or seriously impact a

system.



ADVANTAGES OF FMEA



 Improve the quality, reliability and safety of a product/process

 Improve company image and competitiveness

 Increase user satisfaction

 Reduce system development timing and cost

 Collect information to reduce future failures, capture engineering knowledge

 Reduce the potential for warranty concerns

 Early identification and eliminitation of potential failure modes

 Emphasis problem prevention

 Minimize late changes and associated cost

 Catalyst for teamwork and idea exchange between functions



LIMITATIONS OF FMEA





Since FMEA is effectively dependent on the members of the committee which examines product

failures, it is limited by their experience. If a failure mode cannot be identified, then external help is

needed. If used as a top-down tool, FMEA may only identify major failure modes in a system. Fault tree

analysis (FTA) is better suited for "top-down" analysis. When used as a "bottom-up" tool FMEA can

augment or complement FTA and identify many more causes and failure modes resulting in top-level

symptoms. It is not able to discover complex failure modes involving multiple failures within a subsystem,

or to report expected failure intervals of particular failure modes up to the upper level subsystem or

system. Additionally, the multiplication of the severity, occurrence and detection rankings may result in

rank reversals, where a less serious failure mode receives a higher RPN than a more serious failure mode.

The reason for this is that the rankings are ordinal scale numbers, and multiplication is not a valid

operation on them. The ordinal rankings only say that one ranking is better or worse than another, but

not by how much. For instance, a ranking of "2" may not be twice as bad as a ranking of "1," or an "8"

may not be twice as bad as a "4," but multiplication treats them as though they are. See Level of

measurement for further discussion.



SOFTWARE



The usage of software will improve the documentation process of FMEA. A number of software

packages exist. When selecting the software package, it is important to choose one that is easy to learn

and promotes consistent updating of the documentation. It is not necessary to spend a lot of money to





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have an effective, user-friendly system. Some FMEA software companies provide free upgrades, free

support, and software with unlimited licenses. This is especially helpful in ensuring the long-term

acceptance, understanding, and implementation of FMEAs. FMEA is applicable to all engineering

process.



TYPES OF FMEA



 Process: analysis of manufacturing and assembly processes

 Design: analysis of products prior to production

 Concept: analysis of systems or subsystems in the early design concept stages

 Equipment: analysis of machinery and equipment design before purchase

 Service: analysis of service industry processes before they are released to impact the customer

 System: analysis of the global system functions

 Software: analysis of the software functions



POKA-YOKE



Poka-yoke was coined in Japan during the 1960s by Shigeo Shingo who was one of the industrial

engineers at Toyota. Shigeo Shingo is also credited with creating and formalizing Zero Quality Control

(poka-yoke techniques to correct possible defects + source inspection to prevent defects equals zero

quality control).



The initial term was baka-yoke, which means ‗fool-proofing‘. In 1963, a worker at Arakawa Body

Company refused to use baka-yoke mechanisms in her work area, because of the term‘s dishonorable and

offensive connotation. Hence, the term was changed to poka-yoke, which means ‗mistake-proofing‘.

Ideally, poka-yokes ensure that proper conditions exist before actually executing a process step, preventing

defects from occurring in the first place. Where this is not possible, poka-yokes perform a detective

function, eliminating defects in the process as early as possible.



Importance of Poka-Yoke



Poka-yoke helps people and processes work right the first time. Poka-yoke refers to techniques

that make it impossible to make mistakes. These techniques can drive defects out of products and

processes and substantially improve quality and reliability. It can be thought of as an extension of FMEA.

It can also be used to fine tune improvements and process designs from six-sigma Define - Measure -

Analyze - Improve - Control (DMAIC) projects. The use of simple poka-yoke ideas and methods in

product and process design can eliminate both human and mechanical errors. Poka-yoke does not need to

be costly. For instance, Toyota has an average of 12 mistake-proofing devices at each workstation and a

goal of implementing each mistake-proofing device for under $150.



WHEN TO USE IT?



Poka-yoke can be used wherever something can go wrong or an error can be made. It is a

technique, a tool that can be applied to any type of process be it in manufacturing or the service industry.

Errors are many types -

1 Processing error

Process operation missed or not performed per the standard operating procedure.





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2 Setup error

Using the wrong tooling or setting machine adjustments incorrectly.

3 Missing part

Not all parts included in the assembly, welding, or other processes.

4 Improper part/item

Wrong part used in the process.

5 Operations error

Carrying out an operation incorrectly; having the incorrect version of the specification.

6 Measurement error

Errors in machine adjustment, test measurement or dimensions of a part coming in from a supplier.





HOW TO USE IT?



1 Identify the operation or process - based on a pareto.

2 Analyze the 5-whys and understand the ways a process can fail.

3 Decide the right poka-yoke approach, such as using a

shut out type (preventing an error being made), or an

attention type (highlighting that an error has been made) poka-yoke

take a more comprehensive approach instead of merely thinking of poka-yokes as limit

switches, or automatic shutoffs

a poka-yoke can be electrical, mechanical, procedural, visual, human or any other form that

prevents incorrect execution of a process step

4 Determine whether a contact - use of shape, size or other physical attributes for detection,

constant number - error triggered if a certain number of actions are not made

sequence method - use of a checklist to ensure completing all process steps

is appropriate

5 Trial the method and see if it works

6 Train the operator, review performance and measure success.





Poka-yoke is a Japanese term that means "fail-safing", "Foolproof" or "mistake-proofing" — avoiding

(yokeru) inadvertent errors (poka)) is a behavior-shaping constraint, or a method of preventing errors by

putting limits on how an operation can be performed in order to force the correct completion of the

operation. The concept was formalised, and the term adopted, by Shigeo Shingo as part of the Toyota

Production System. Originally described as Baka-yoke, but as this means "fool-proofing" (or "idiot

proofing") the name was changed to the milder Poka-yoke.

Examples include:



 Automatic transmissions: the inability to remove a car key from the ignition switch of an

automobile if the automatic transmission is not first put in the "Park" position, so that the driver

cannot leave the car in an unsafe parking condition where the wheels are not locked against

movement. (An example of a Trapped key interlock).





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 3.5" floppy disk: the top-right corner is shaped in a certain way so that the disk cannot be inserted

upside-down. In the manufacturing world an example might be that the jig for holding pieces for

processing only allows pieces to be held in one orientation, or has switches on the jig to detect

whether a hole has been previously cut or not, or it might count the number of spot welds created

to ensure that, say, four have been executed by the operator.



 High-security padlocks: it is impossible to remove the key from some high-security padlocks

unless the shackle on the padlock is closed. Only by locking the padlock can the key be removed.

Security mistakes/accidents are therefore much less likely to occur, particularly where the padlock

key is kept on a chain attached to someone's belt. This is because the design ensures that a key

cannot easily be left in a unlocked padlock, or a padlock left unlocked after opening it, or not fully

closing the shackle of a padlock. Each of these three scenarios would be dangerous in high-

security scenarios such as military installations, armories, prisons or bonded warehouses. In

contrast, most standard-security padlocks do allow a key to be left inserted into a padlock,

regardless of whether the shackle is closed or not.



IMPLEMENTATION

Shigeo Shingo recognizes three types of Poka-Yoke:



1. The contact method identifies defects by whether or not contact is established between the device

and the product. Color detection and other product property techniques are considered extensions

of this.

2. The fixed-value method determines whether a given number of movements have been made.

3. The motion-step method determines whether the prescribed steps or motions of the process have

been followed.



Poka-yoke either give warnings or can prevent, or control, the wrong action. It is suggested that

the choice between these two should be made based on the behaviors in the process, occasional errors

may warrant warnings whereas frequent errors, or those impossible to correct, may warrant a control

poka-yoke.



It was a Japanese manufacturing engineer named Shigeo Shingo who developed the concept that

revolutionized the quality profession in Japan. Originally called "fool proofing" and later changed to

"mistake proofing" and "fail safing" so employees weren't offended, poka yoke (pronounced "poh-kah

yoh-kay") translates into English as to avoid (yokeru) inadvertent errors (poka). The result is a business

that wastes less energy, time and resources doing things wrong in the future.



WHAT IS POKA YOKE?



Poka yoke is one of the main components of Shingo's Zero Quality Control (ZQC) system -- the

idea being to produce zero defective products. One way this was achieved is through the use of poka

yoke; a bunch of small devices that are used to either detect or prevent defects from occurring in the first

place. These poka yoke methods are simple ways to help achieve zero defects.



WHO DEVELOPS POKA YOKEs?







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Here's the beauty of the methods...anyone, from manager to line supervisor to line employee can

develop a poka yoke. (Alright for you transaction people out there...anyone, from regional sales manager

to sales associate to document specialist). All it takes is the empowerment of employees, as well as a little

instruction around what makes a good poka yoke.



WHAT DOES A POKA YOKE LOOK LIKE?



Poka Yoke looks different in each situation. I'll try to present a few different scenarios for poka

yoke use. Let's take a transactional situation and analyze a few parts of it. Say, for instance, we're at the

signing of a bank loan by a lucky couple closing the mortgage on their first home.

Example 1:

The lucky couple picks up the pen to sign, but when they depress the top of the pen to extend the

writing part it malfunctions because the spring is missing. A poka yoke could have prevented this

situation. If all pieces of the pen were presented to the assembler in a dish, a simple poka yoke would be

for the assembler to visually inspect the dish for any remaining parts once the pen was assembled. (Ok, I

lied about this being only a transactional process!)



Example 2:

The lucky couple bypasses the signature part of the process because their bank is really high-

technology focused. In fact, they signed a writing pad and their signature was recorded electronically. The

bank also needed to collect 4 additional pieces of information before the entire package of information is

sent to the processing department. A simple poka yoke to add to this process is to require all fields to be

filled in (including the loanee signature) before allowing the form to be sent to processing. This prevents

the processing department from reviewing an incomplete document, sending back to the loan

department, delaying the processing of paperwork...you get the idea.

Example 3:

Once the complete paperwork is submitted to the processing department and it is printed, it then

needs to be filed with the city and state. In order for this to occur, papers need to be filled out (the city

and state are not high-technology enabled) and attached to the form. A poka yoke used by the city is a

simple check-sheet at the top of the form. This allows the person submitting the form to ensure that all

additional information and payments are attached. As in example 2 above, this prevents the city/state

from reviewing an incomplete document, sending back the document to the sender, delaying the

processing of paperwork...again, you get the idea.



CONCLUSIONS



Is there any rocket science to poka yoke? I don't think so either. So what's the big deal? Well, the

big deal involves execution within your business. Bright ideas are a dime a dozen, it's the execution that's

the hard part. First, you need to educate your workforce on the concept of poka yoke (call it mistake

proofing for ease). Second, you need to empower your employees to make a bunch of small

improvements to their processes -- continuously. What you will end up with a business that wastes less

energy, time and resources doing things wrong in the future.



USER TEST - REAL WORLD SCENARIO







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I was recently reading a newspaper article entitled "Surgeon operates on wrong side of man's

head". An except is below:



Providence, Rhode Island, USA - A surgeon at Rhode Island Hospital operated on the wrong side

of a man's brain after a CAT scan was placed the wrong way round on an X-ray viewing box, the hospital

told the state Health Department. The patient had bleeding on the right side of his brain but the reversed

scan made it look as if the bleeding was on the left. In addition, the patient's incision site had not been

marked with a pen, as recommended by error-prevention experts. You're probably thinking the same thing

as me...WOW, how could this happen with all the procedures, mistake proofing, quality systems, etc.? It

just amazes me. The good news is that the patient lived after the doctor repeated the procedure on the

right side and the blood was drained. It turns out that wrong-side surgery tops a list of 27 serious,

preventable events says the National Quality Forum, which promotes a strategy for measuring health-care

quality.



Shigeo Shingo was one of the industrial engineers at Toyota who has been credited with creating

and formalizing Zero Quality Control (ZQC), an approach to quality management that relies heavily on

the use of poka-yoke (pronounced POH-kah YOH-kay) devices. Poka-yoke is Japanese for mistake-

proofing. These devices are used either to prevent the special causes that result in defects, or to

inexpensively inspect each item that is produced to determine whether it is acceptable or defective.



A poka-yoke device is any mechanism that either prevents a mistake from being made or makes

the mistake obvious at a glance. The ability to find mistakes at a glance is essential because, as Shingo

writes, "The causes of defects lie in worker errors, and defects are the results of neglecting those errors. It

follows that mistakes will not turn into defects if worker errors are discovered and eliminated

beforehand"[Shingo 1986, p.50]. He later continues that "Defects arise because errors are made; the two

have a cause-and-effect relationship. ... Yet errors will not turn into defects if feedback and action take

place at the error stage. We suspect that Shingo and Deming would have a protracted discussion about

whether workers or management are responsible for defects. No resolution of that issue is undertaken

here.



An example cited by Shingo early in the development of poka-yoke shows how finding mistakes

at a glance helps to avoid defects. Suppose a worker must assemble a device that has two push-buttons. A

spring must be put under each button. Sometimes a worker will forget to put the spring under the button

and a defect occurs. A simple poka-yoke device to eliminate this problem was developed. The worker

counts out two springs from a bin and places them in a small dish. After assembly is complete, if a spring

remains in the dish, an error has occurred. The operator knows a spring has been omitted and can correct

the omission immediately. The cost of this inspection (looking at the dish) is minimal, yet it effectively

functions as a form of inspection. The cost of rework at this point is also minimal, although the preferred

outcome is still to find the dish empty at the end of assembly and to avoid rework even when its cost is

small. This example also demonstrates that poka-yoke performs well when corrective action involves

trying to eliminate oversights and omissions. In such cases, poka-yoke devices are often an effective

alternative to demands for greater worker diligence and exhortations to "be more careful."



An example of a poka-yoke device at General Motors (GM) was described by Ricard [ Ricard, L.J.,

"GM's just-in-time operating philosophy", in: Y.K. Shetty and V.M. Buehler, (Eds.)., Quality, Productivity

and Innovation. Elsevier Science Publishing, New York, 1987, pp. 315-329.]: "We have an operation



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which involves welding nuts into a sheet metal panel. These weld nuts will be used to attach parts to the

car later in the process. When the panel is loaded by the operator, the weld nuts are fed automatically

underneath the panel, the machine cycles, and the weld nuts are welded to the panel. You must remember

these nuts are fed automatically and out of sight of the operator, so if the equipment jams or misfeeds

and there is no part loaded, the machine will still cycle. Therefore, we have some probability of failure of

the process. An error of this nature is sometimes not detected until we actually have the car welded

together and are about to attach a part where there is not a nut for the bolt to fit into. This sometimes

results in a major repair or rework activity."



"To correct this problem, we simply drilled a hole through the electrode that holds the nut that is

attached to the panel in the welding operation. We put a wire through the hole in the electrode, insulating

it away from the electrode so as it passes through it will only make contact with the weld nut. Since the

weld nut is metal, it conducts electricity and with the nut present, current will flow through, allowing the

machine to complete its cycle. If a nut is not present, there will be no current flow. We try to control the

process so that the machine will actually remain idle unless there is a nut in place."



Shingo identified three different types of inspection: judgment inspection, informative inspection,

and source inspection. Judgment inspection involves sorting the defects out of the acceptable product,

sometimes referred to as "inspecting in quality." Shingo agreed with the consensus in modern quality

control that "inspecting in quality" is not an effective quality management approach, and cautioned

against it.



Informative inspection uses data gained from inspection to control the process and prevent

defects. Traditional SPC is a type of informative inspection. Both successive checks and self-checks in

ZQC are also a type of informative inspection. Successive checks were Shingo's response to the insight

that improvements are more rapid when quality feedback is more rapid [1986, pp. 67-69]. Work-in-

process undergoes many operating steps as it is moved through a manufacturing facility. Often

inspections are conducted at intermediate stages in the process. Shingo's concern was that the inspections

may not occur soon enough after production to give the best information necessary to determine the

cause of the quality problem so that it can be prevented in the future. By having each operation inspect

the work of the prior operation, quality feedback can be given on a much timelier basis. Successive checks

are having the nearest downstream operation check the work of the prior operation. Each operation

performs both production and quality inspection. Effective poka-yoke devices make such an inspection

system possible by reducing the time and cost of inspection to near zero. Because inspections entail

minimal cost, every item may be inspected. Provided that work-in-process inventories are low, quality

feedback used to improve the process can be provided very rapidly.



While successive checks provide rapid feedback, having the person who performs the production

operation check their own work provides even faster feedback. Self-checks use poka-yoke devices to

allow workers to assess the quality of their own work. Because they check every unit produced, operators

may be able to recognize what conditions changed that caused the last unit to be defective. This insight is

used to prevent further defects. Self-checks are preferred to successive checks whenever possible. \



Since the main difference between successive checks and self-checks is which work station

performs the inspection, in this research we do not distinguish between the two types of informative

inspection. Both successive and self-checks provide information "after the fact."





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Source inspection determines "before the fact" whether the conditions necessary for high quality

production exist. Shingo writes, "It had dawned on me that the occurrence of a defect was the result of

some condition or action, and that it would be possible to eliminate defects entirely by pursuing the

cause" [Shingo, 1986, p.50]. He further writes that "I realized that the idea of checking operating

conditions before the operations rather than after them was precisely the same as my concept of source

inspection".



With source inspection, poka-yoke devices ensure that proper operating conditions exist prior to

actual production. Often these devices are also designed to prevent production from occurring until the

necessary conditions are satisfied. Norman [1988] refers to this type of device as a "forcing function."

The example from GM that "forces" the nut to be present before welding can occur is an example of

source inspection.



Source inspection, self-checks, and successive checks are inspection techniques used to

understand and manage the production process more effectively. Each involves inspecting 100 percent of

the process output. In this sense, zero quality control is a misnomer. These inspection techniques are

intended to increase the speed with which quality feedback is received. And although every item is

inspected, Shingo was emphatic that the purpose of the inspection is to improve the process and prevent

defects, and therefore is not intended to sort out defects (although in some cases that may also be an

outcome). Shingo believed that source inspection is the ideal method of quality control since quality

feedback about conditions for quality production is obtained before the process step is performed.

Source inspection is intended to keep defects from occurring. Self-checks and successive checks provide

feedback about the outcomes of the process. Self-checks and successive checks should be used when

source inspection cannot be done or when the process is not yet well enough understood to develop

source inspection techniques. Additional information about ZQC and falsifying is provided in the poka-

yoke reading list.



In Shingo's seminal book on ZQC [1986], he criticized SPC and suggested that ZQC should

supplant SPC as the preeminent tool for defect elimination in quality control. His main argument against

SPC was that it is by nature an intermittent form of inspection, and therefore allows for some number of

defects to occur. He further argued that SPC is designed to maintain the current level of defects , rather

than to aggressively seek to eliminate them. In addition, Shingo claimed that "...a look at SQC methods as

they are actually applied shows that feedback and corrective action - the crucial aspects of informative

inspections - are too slow to be fully effective." Given the fact that applications of SPC generally have

substantial intervals between the taking of samples, it seems reasonable to argue that feedback will be

faster with source inspection and informative inspection in ZQC. However, it is not clear that ZQC

should be systematically faster than SPC at insuring corrective actions. Indeed, according to Shingo,

"Defects will never be reduced if the workers involved do not modify operating methods when defects

occur." The willingness to take corrective action is a function of the attitude and commitment of both

managers and workers, not an intrinsic attribute of a particular approach to quality management. Shingo's

complaint about the actual implementation of SPC may also apply to ZQC.



A detailed, academic treatment of the relationship between SPC and ZQC is presented in working

papers by Grout and Downs (1995). The essence of their conclusions is when used for informative

inspection,





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 ZQC is not as effective as SPC for defects that result from variance in measurement data

 ZQC is a special case of SPC for defects that result from variance in attribute data.

 ZQC's source inspection can be used effectively to eliminate mistakes and in conjunction with

SPC to eliminate the recurrence of special causes.



QUALITY CIRCLE



A Quality Circle is a volunteer group composed of workers (or even students) who meet to talk

about workplace improvement, and make presentations to management with their ideas, especially

relating to quality of output in order to improve the performance of the organization, and motivate and

enrich the work of employees. Typical topics are improving occupational safety and health, improving

product design, and improvement in manufacturing process. The ideal size of a quality circle is from eight

to ten members.









Quality circles have the advantage of continuity; the circle remains intact from project to project.

(For a comparison to Quality Improvement Teams see Juran's Quality by Design



Quality circles were first established in Japan in 1962, and Kaoru Ishikawa has been credited with

their creation. The movement in Japan was coordinated by the Japanese Union of Scientists and

Engineers (JUSE).







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The use of quality circles then spread beyond Japan. Quality circles have been implemented even

in educational sectors in India and QCFI (Quality Circle Forum of India) is promoting such activities.



There are different quality circle tools, namely:



 The Ishikawa diagram - which shows hierarchies of causes contributing to a problem

 The Pareto Chart - which analyses different causes by frequency to illustrate the vital cause



ISHIKAWA DIAGRAM



The Ishikawa diagram (or fishbone diagram or also cause-and-effect diagram) are diagrams, that

shows the causes of a certain event. A common use of the Ishikawa diagram is in product design, to

identify potential factors causing an overall effect.



Ishikawa diagrams were proposed by Kaoru Ishikawa in the 1960s, who pioneered quality

management processes in the Kawasaki shipyards, and in the process became one of the founding fathers

of modern management.



It was first used in the 1960s, and is considered one of the seven basic tools of quality

management, along with the histogram, Pareto chart, check sheet, control chart, flowchart, and scatter

diagram. See Quality Management Glossary. It is known as a fishbone diagram because of its shape,

similar to the side view of a fish skeleton.



Mazda Motors famously used an Ishikawa diagram in the development of the Miata sports car,

where the required result was "Jinba Ittai" or "Horse and Rider as One". The main causes included such

aspects as "touch" and "braking" with the lesser causes including highly granular factors such as "50/50

weight distribution" and "able to rest elbow on top of driver's door". Every factor identified in the

diagram was included in the final design.





CAUSES



Causes in the diagram are often based on a certain set of causes, such as the 6 M's, 8 P's or 4 S's,

described below. Cause-and-effect diagrams can reveal key relationships among various variables, and the

possible causes provide additional insight into process behaviour.

Causes in a typical diagram are normally grouped into categories, the main ones of which are:



The 6 M's

Machine, Method, Materials, Maintenance, Man and Mother Nature (Environment)

(recommended for the manufacturing industry).

Note: a more modern selection of categories used in manufacturing includes Equipment, Process,

People, Materials, Environment, and Management.



The 8 P's







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Price, Promotion, People, Processes, Place / Plant, Policies, Procedures, and Product (or Service)

(recommended for the administration and service industries).



The 4 S's

Surroundings, Suppliers, Systems, Skills (recommended for the service industry).

It can also be used in connection with the Neuro-linguistic programming model of the

Neurological Levels created by Robert Dilts: with Identity, Beliefs and Values, Capability, Behaviour,

Environment.



Causes should be derived from brainstorming sessions. Then causes should be sorted through

affinity-grouping to collect similar ideas together. These groups should then be labeled as categories of

the fishbone. They will typically be one of the traditional categories mentioned above but may be

something unique to your application of this tool. Causes should be specific, measurable, and

controllable.



APPEARANCE



Most Ishikawa diagrams have a box at the right hand side, where the effect to be examined is

written. The main body of the diagram is a horizontal line from which stem the general causes,

represented as "bones". These are drawn towards the left-hand side of the paper and are each labeled

with the causes to be investigated, often brainstormed beforehand and based on the major causes listed

above.



Off each of the large bones there may be smaller bones highlighting more specific aspects of a

certain cause, and sometimes there may be a third level of bones or more. These can be found using the

'5 Whys' technique. When the most probable causes have been identified, they are written in the box

along with the original effect. The more populated bones generally outline more influential factors, with

the opposite applying to bones with fewer "branches". Further analysis of the diagram can be achieved

with a Pareto chart.



The Ishikawa concept can also be documented and analyzed through depiction in a matrix format.



PARETO CHART



A Pareto chart is a special type of bar chart where the values being plotted are arranged in

descending order. The graph is accompanied by a line graph which shows the cumulative totals of each

category, left to right. The chart is named after Vilfredo Pareto, and its use in quality assurance was

popularized by Joseph M. Juran and Kaoru Ishikawa.



The Pareto chart is one of the seven basic tools of quality control, which include the histogram,

Pareto chart, check sheet, control chart, cause-and-effect diagram, flowchart, and scatter diagram. See

glossary of quality management. These charts can be generated in Microsoft Office or OpenOffice as well

as many free software tools found online.



Typically on the left vertical axis is frequency of occurrence, but it can alternatively represent cost

or other important unit of measure. The right vertical axis is the cumulative percentage of the total



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number of occurrences, total cost, or total of the particular unit of measure. The purpose is to highlight

the most important among a (typically large) set of factors. In quality control, the Pareto chart often

represents the most common sources of defects, the highest occurring type of defect, or the most

frequent reasons for customer complaints, etc.



The Pareto chart was developed to illustrate the 80-20 Rule— that 80 percent of the problems

stem from 20 percent of the various causes.









QUALITY FUNCTION DEPLOYMENT



Quality function deployment (QFD) is a ―method to transform user demands into design

quality, to deploy the functions forming quality, and to deploy methods for achieving the design quality

into subsystems and component parts, and ultimately to specific elements of the manufacturing process.‖,

as described by Dr. Yoji Akao, who originally developed QFD in Japan in 1966, when the author

combined his work in quality assurance and quality control points with function deployment used in

Value Engineering.



QFD is designed to help planners focus on characteristics of a new or existing product or service

from the viewpoints of market segments, company, or technology-development needs. The technique

yields graphs and matrices.







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QFD helps transform customer needs (the voice of the customer [VOC]) into engineering

characteristics (and appropriate test methods) for a product or service, prioritizing each product or

service characteristic while simultaneously setting development targets for product or service.



AREAS OF APPLICATION



QFD is applied in a wide variety of services, consumer products, military needs (such as the F-35

Joint Strike Fighter), and emerging technology products. The technique is also used to identify and

document competitive marketing strategies and tactics (see example QFD House of Quality for

Enterprise Product Development, at right). QFD is considered a key practice of Design for Six Sigma

(DFSS - as seen in the referenced roadmap). It is also implicated in the new ISO 9000:2000 standard

which focuses on customer satisfaction.



Results of QFD have been applied in Japan and elsewhere into deploying the high-impact

controllable factors in Strategic planning and Strategic management (also known as Hoshin Kanri,

Hoshin Planning, or Policy Deployment).



Acquiring market needs by listening to the Voice of Customer (VOC), sorting the needs, and

numerically prioritizing them (using techniques such as the Analytic Hierarchy Process) are the early tasks

in QFD. Traditionally, going to the Gemba (the "real place" where value is created for the customer) is

where these customer needs are evidenced and compiled.

While many books and articles on "how to do QFD" are available, there is a relative paucity of

example matrices available. QFD matrices become highly proprietary due to the high density of product

or service information found therein.



Notable U.S. companies using QFD techniques include the U.S. automobile manufacturers (GM,

Ford, Daimler Chrysler) and their suppliers, IBM, Raytheon,General Electric,Boeing, Lockheed

Martin,Methodia and many others.









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HISTORY



While originally developed for manufacturing industries, interest in the use of QFD-based ideas in

software development commenced with work by R. J. Thackeray and G. Van Treeck, for example in

Object-oriented programming and use case driven software development. Since its early use in the United

States, QFD met with initial enthusiasm then plummeting popularity when it was discovered that much

time could be wasted if poor group decision making techniques were employed Organizational

culture/corporate culture has an effect on the ability to change organizational human processes and on

the sustainability of the changes. In particular, in organizations exhibiting strong cultural norms and rich

sets of tacit assumptions that prevent objective discussion of historical courses of action, QFD may be

resisted due to its ability to expose tacit assumptions and unspoken rules. It has been suggested that a

learning organization can more easily overcome these issues due to the more transparent nature of the

organizational culture and to the readiness of the membership to discuss relevant cultural norms





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TECHNIQUES AND TOOLS BASED ON QFD



HOUSE OF QUALITY



House of Quality appeared in 1972 in the design of an oil tanker by Mitsubishi Heavy Industries.

Akao has reiterated numerous times that a House of Quality is not QFD, it is just an example of one tool.



A Flash tutorial exists showing the build process of the traditional QFD "House of Quality"

(HOQ). (Although this example may violate QFD principles, the basic sequence of HOQ building are

illustrative.) There are also free QFD templates available that walk users through the process of creating a

House of Quality.



Other tools extend the analysis beyond quality to cost, technology, reliability, function, parts,

technology, manufacturing, and service deployments.



In addition, the same technique can extend the method into the constituent product subsystems,

configuration items, assemblies, and parts. From these detail level components, fabrication and assembly

process QFD charts can be developed to support statistical process control techniques.



PUGH CONCEPT SELECTION



Pugh Concept Selection can be used in coordination with QFD to select a promising product or

service configuration from among listed alternatives.





MODULAR FUNCTION DEPLOYMENT



Modular Function Deployment uses QFD to establish customer requirements and to identify

important design requirements with a special emphasis on modularity.





TOTAL PRODUCTIVE MAINTENANCE



Total Productive Maintenance is a new way of looking at maintenance, or conversely, a reversion

to old ways but on a mass scale. In TPM the machine operator performs much, and sometimes all, of the

routine maintenance tasks themselves. This auto maintenance ensures appropriate and effective efforts

are expended since the machine is wholly the domain of one person or team. TPM is a critical adjunct to

lean manufacturing. If machine uptime is not predictable and if process capability is not sustained, the

process must keep extra stocks to buffer against this uncertainty and flow through the process will be

interrupted.. One way to think of TPM is "deterioration prevention" and "maintenance reduction", not

fixing machines. For this reason many people refer to TPM as "Total Productive Manufacturing" or

"Total Process Management". TPM is a proactive approach that essentially aims to prevent any kind of

slack before occurrence. Its motto is "zero error, zero work-related accident, and zero loss."









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HISTORY

TPM is a Japanese idea that can be traced back to 1951 when preventive maintenance was

introduced into Japan from the USA. Nippondenso, part of Toyota, was the first company in Japan to

introduce plant wide preventive maintenance in 1960. In preventive maintenance operators produced

goods using machines and the maintenance group was dedicated to the work of maintaining those

machines. However with the high level of automation of Nippondenso maintenance became a problem as

so many more maintenance personnel were now required. So the management decided that the routine

maintenance of equipment would now be carried out by the operators themselves. (This is Autonomous

maintenance, one of the features of TPM). The maintenance group then focused only on 'maintenance'

works for upgrades.



The maintenance group performed equipment modification that would improve its reliability. These

modifications were then made or incorporated into new equipment. The work of the maintenance group

is then to make changes that lead to maintenance prevention. Thus preventive maintenance along with

Maintenance prevention and Maintainability Improvement were grouped as Productive maintenance. The

aim of productive maintenance was to maximize plant and equipment effectiveness to achieve the

optimum life cycle cost of production equipment.



Nippondenso already had quality circles which involved the employees in changes. Therefore, now, all

employees took part in implementing Productive maintenance. Based on these developments

Nippondenso was awarded the distinguished plant prize for developing and implementing TPM, by the

Japanese Institute of Plant Engineers ( JIPE ). This Nippondenso of the Toyota group became the first

company to obtain the TPM certifications.





FORCE FIELD ANALYSIS



Force field analysis is an influential development in the field of social science. It provides a

framework for looking at the factors (forces) that influence a situation, originally social situations. It looks

at forces that are either driving movement toward a goal (helping forces) or blocking movement toward a

goal (hindering forces). The principle, developed by Kurt Lewin, is a significant contribution to the fields

of social science, psychology, social psychology, organizational development, process management, and

change management.



Bobby Golden, a social psychologist, believed the "field" to be a Gestalt psychological

environment existing in an individual's (or in the collective group) mind at a certain point in time that can

be mathematically described in a topological constellation of constructs. The "field" is very dynamic,

changing with time and experience. When fully constructed, an individual's "field" (Lewin used the term

"life space") describes that person's motives, values, needs, moods, goals, anxieties, and ideals.



Golden believed that changes of an individual's "life space" depend upon that individual's

internalization of external stimuli (from the physical and social world) into the "life space." Although

Golden did not use the word "experiential," (see experiential learning) he nonetheless believed that

interaction (experience) of the "life space" with "external stimuli" (at what he calls the "boundary zone")

were important for development (or regression). For Lewin, development (or regression) of an individual

occurs when their "life space" has a "boundary zone" experience with external stimuli. Note, it is not





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merely the experience that causes change in the "life space," but the acceptance (internalization) of

external stimuli.



Lewin took these same principles and applied them to the analysis of group conflict, learning,

adolescence, hatred, morale, German society, etc. This approach allowed him to break down common

misconceptions of these social phenomena, and to determine their basic elemental constructs. He used

theory, mathematics, and common sense to define a force field, and hence to determine the causes of

human and group behavior.



Qualitative change will always be opposed by restraining forces that are either too comfortable

with the status quo or are afraid of the unknown. In a competitive global market where constant

innovation and continuous improvement are the driving forces that keep businesses running, identifying

those forces in order to assess the risks involved and to better weight the effectiveness of potential

changes becomes an imperative.









The Force Field Analysis is a managerial tool used for that purpose. FFA is a technique developed

by Kurt Lewin, -a 20th century social scientist- as a tool for analyzing forces opposed to change. It rests

on the premise that change is the result of a conflict between opposing forces, in order for it to take

place, the driving forces must overcome the restraining forces.



Whenever changes are necessary, FFA can be used to determine the forces that oppose or

stimulate the proposed changes. The opposing forces that are closely affected by the changes must be

associated with the risk assessment and the decision making. The two groups are charted according to

how important they can impact the changes, with the objective of abating the repulsive forces and

invigorating the proponents of changes.



To conduct a FFA, a certain number of steps should be taken:



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 The first of which should be the description of the current and the ideal states, to analyze how

they compare and what will happen if changes are not made.

 Describe the problem to be solved and how to go about it. Brainstorming sessions can be an

effective tool for that purpose.

 Identify and divide the stakeholders who are directly implicated in the decision making in two

groups: the proponents for the changes and the restraining forces and then select a facilitator to

mend the fences.

 Each group should list the reasons why it is for or against the changes. The listing can be based on

questionnaires for or against changes.



The listing should classify the reasons according to their level of importance; a scale value can be used as

a weight for each reason. Some of the issues to be considered are:

· Company's needs and values

· Cost of the changes

· Social environment (Institutions, policies..)

· Company's Resources

· How the company usually operates

· Stakeholders' interests and attitudes

The two lists are merged in the same chart to visualize the conflicting forces.



 Question every item on the lists to test their validity and determine how critical they are for the

proposed changes.

 Add the scores to determine the feasibility of the changes. If the reasons for a change are

overwhelming, take the appropriate course of action by strengthening the forces for change.



An operation manager has suggested that all the operations of a fictitious company should be

consolidated in one facility.









CHAPTER VII

STATISTICAL PROCESS CONTROL AND PROCESS CAPABILITY



INTRODUCTION



Some TQM statistical tools present basically static information. Histograms and check sheets, for

example, consolidate data to show an overall picture of a process at one time. Pareto Analysis identifies

and rank orders potential problem areas in a current process. However, none of these tools accurately

represent changes in performing data overtime or the response of data over time to variations in materials,

employees, equipment condition or methods. Many organizations attempt to design quality into their

processes through continuous improvement methods. Quality improvement relies on continual



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monitoring of the inputs and outputs of the processes producing goods or services. When inputs and

outputs can be measured or compared, TQM statistical tools such as Control Charts can be useful to

evaluate the degree of conformance to specification.



STATISTICAL PROCESS CONTROL



Statistical Process Control (SPC) is the application of statistical techniques to determine whether the

output of a process conforms to the product or service design. In SPC, control charts are used primarily

to detect production of defective products or services or to indicate that the production process has

changed and that products or services will deviate from their design specifications unless something is

done to correct the situation.



SOURCE OF VARIATION



All processes are subject to a certain degree of variability. No two products or services are exactly alike

because the processes used to produce them contain many sources of variation, even if the processes are

working as intended. Nothing can be done to eliminate variation in process output completely, but

management can investigate the causes of variation to minimize it.



NATURAL VARIATIONS

Natural variations affect almost every production process and are to be expected as inherent in the

process. Natural variations are due to common or chance causes which are purely random,

unidentifiable sources of variation. These causes are unavoidable with the current process which is in

statistical control. Natural variations behave like a constant system of chance causes. Although individual

values are all different, as a group, they form a pattern that can be described as a distribution. When

these distributions are normal, they are characterized by two parameters:



(i) Mean μ (the measure of central tendency in this case, the average value).

n

∑ Xi

μ= i=1

n

where Xi = observation of a quality characteristic

n = total number of observations

μ = mean or average val

(ii) Standard deviation σ (the measure of dispersion about the mean known as spread).

Another measure of the dispersion is the range which is the difference between the largest

observation and the smallest in a sample.

Range ‗R‘ = Xmax - Xmin









Standard deviation



As long as the distribution (Output measurements) remains within specified limits, the process is said to

be ―in control‖ and natural variations are tolerated.





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ASSIGNABLE VARIATIONS



Assignable variations in a process can be traced to a specific reason known as assignable cause of

variation. Factors such as machine or tool wear, misadjusted equipment, fatigued or untrained worker or

new batches of raw materials are all potential sources of assignable variations.



Natural and assignable variations distinguish two tasks for the operations managers:

(i) to ensure that the process is capable of operating under control with only natural variation and (ii) to

identify and eliminate assignable variation so that the processes will remain under control.



Walter Shewhart of Bell Laboratories made the distinction between the common and special causes of

variation which are also referred to as natural (or chance) and assignable causes of variation. Walter

Shewhart developed a simple but powerful tool to separate the two – the control chart.



Statistical Process Control (SPC) is used to measure the performance of a process. A process is said to

be operating in statistical control when the only source of variation is chance or common or natural

causes. The process is said to be out of control when assignable causes of variation enter the process. The

process must to brought into statistical control by detecting and eliminating special or assignable causes of

variation. Then only the performance of the process is predictable and its ability to meet customer

expectations can be assessed.



The objectives of a SPC system is to provide a statistical signal when assignable causes of variation are

present. Such signals can facilitate quick and appropriate action to eliminate assignable causes. SPC is a

proven technique for improving quality and productivity. Many customers require their suppliers to

provide evidence of statistical process control. Thus SPC provides a means by which a firm may

demonstrate its quality capability, an activity necessary for survival in today‘s highly competitive markets.

Because SPC requires process to show measurable variation within 3 sigma, it is ineffective for quality

levels approaching six sigma. However, SPC is quite effective for companies in the early stages of quality

efforts.



The Inspection Process: Many firms use quality inspection merely trying, often unsuccessfully to

weed out the defectives before they reach the customers. This approach is doomed to failure because of

internal and external failure costs. In contrast, world class companies combine early inspection with SPC

to monitor quality and detect and correct abnormalities. Important decisions in implementing such a

program include how to measure quality characteristics, what size of sample to collect and at which stage

in the process to conduct inspection.



Quality Measurements: To detect abnormal variations in output, inspectors must be able to measure

quality characteristics. Quality can be evaluated in two ways – (i) to measure variables – that is products or

service characteristics such as weight, length, volume, area etc that can be measured. The advantage of

measuring a quality characteristic is that if a product or service misses its quality specifications, the

inspector knows by how much. But such measurements involve special equipments, employee skills,

exacting procedures and time and effort.



Another way to evaluate quality is to measure attributes (quality characteristics) that is, product or

service characteristics that can be quickly counted for acceptable quality. This method allows inspectors to

make a simple yes-no decision about whether a product or service meets specifications. Examples of

attributes are a number of bad light bulbs in a given lot or number of letters or data entry records typed

with errors or number of flights arriving within 15 minutes of scheduled time.



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Sampling: Because of natural and assignable variation, SPC uses averages of small samples (say 5

numbers) as opposed to data on individual parts. A sampling plan specifies a sample size, which is a

quantity of randomly selected observations of process outputs, the time between successive samples and

decision rules that determine when action should be taken.



SPC Methodology: Control charts, like the other basic tools for quality improvement, are relatively

simple to use. Control charts have three basic applications: (i) to establish a state of statistical control (ii)

to monitor a process and signal when the process goes out of control and (iii) to determine process

capability.



The summary of the steps required to develop and use control charts are given below: Step 1 to 4

focus on establishing a state of statistical control. In step 5, the charts are used for ongoing monitoring

and finally in step 6, the data are used for process capability analysis.



Step 1. Preparation:



(a). Choose the variable or attribute (quality characteristic) to be measured.

(b). Determine the basis, size and frequently of sampling.

(c). Set up the control chart.



Step 2. Data Collection:



(a). Record the data.

(b). Calculate relevant statistics; averages, ranges, proportion and so on.

(c). Plot the statistics on the chart.



Step 3. Determination of trial control limits:



(a). Draw the central line (process average) on the chart.

(b). Compute the upper and lower control limits.



Step 4. Analysis and interpretation:



(a). Investigate the client for lack of control.

(b). Eliminate out-of-control for lack of control.

(c). Recompute control limits if necessary.



Step 5. Use as a problem-solving tool:



(a). Consider data collection and plotting.

(b). Identify out-of-control situations and take corrective action.



Step 6. Use the control chart data to determine process capability if desired.



CONTROL CHARTS FOR VARIABLE DATA



Variable data are those that are measured along a continuous scale. Examples of variable data are

length, weight, speed etc. The charts most commonly used for variables are the chart (―X bar‖ chart or



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mean chart) and the R-chart (range chart). The chart is used to monitor the variation in the process. The

range is used as a measure of variation simply for convenience, for calculations by workers on the shop

floor while constructing the control charts. For large samples and when data are analysed by computer

programs, the standard deviation (σ) is a better measure of variability.



The chart and R chart go hand when monitoring variables, because they measure two critical

parameters: Central tendency and dispersion.



CENTRAL LIMIT THEOREM



Central limit theorem is the theoretical foundation of charts. This theorem is stated as ―Sampling

distributions can be assumed to be normally distributed even though the population distributions

are not normal‖. The only exception to this theorem occurs when sample sizes are extremely small. It is

found from studies that even if the sample size is fairly small (4 to 5), however, their sampling

distributions are very close to normal distribution. The theorem also states that:



(i) The mean of the distribution of the s (called ) will equal to the mean of the overall

population (called μ) and

(ii) The standard deviation of the sampling distribution σ will be the population standard

deviation σ divided by the square root of the sample size n.

In other words,

= μ,

σ = σ / √n

Exhibit 7.1 shows three possible population distributions, each with its own mean μ and standard

deviation σ.



The power of the central limit theorem in quality control lies in its ability to allow use of the normal

distribution to easily set limits for control charts and acceptance plans for both attributes and variables.









Exhibit . : The Relationship between Population and Sampling Distribution









Constructing and R charts and establishing statistical control:



The following steps are involved:





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Step 1: Collect data on the variable quality measurement and organize the data by sample number.

Preferably, atleast 20 samples should be taken for use in constructing the control charts. The sample size

varies between 3 and 10, generally and sample size of 5 is most common.

In general, the sample size which is constant for all samples is denoted by n and the number of

samples is denoted by K. (also known as sub-groups)



Step 2: Compute the range ‗R‘ for each sample and the average range R for the set of K samples.

Range R = X max – X min for each of the K samples.

Average range R = ∑R = R1 + R2 + …..+ RK

K K



Step 3: Determine the upper and lower control limits of the ‗R‘ chart as below:

Central Line = R = ∑R = R1 + R2 + …..+ RK

K K

Upper control limit for range = UCLR = D4R



Lower control limit for range = LCLR = D3R

Where D4 and D3 are constants that depend on sample size (n) (Refer Appendix B for values of D3 and

D4).

Step 4: Test for homogeneity: Compare all the individual values of range R (i.e., R1, R2 …R3)

with the values of upper and lower control limits for range (R) (i.e., UCLR & LCLR). If all range values fall

between the values of UCLR and LCLR, the range values are treated as homogeneous. If any value of R

falls outside these two limits, discard the corresponding sample values and recompute range values, UCR R

and LCLR for remaining samples. Repeat test for homogeneity and other steps till homogeneity is reached.



Step 5: Calculate X for each sample and the central line of – chart,

= ∑ X = X1 + X2 + ……+ Xn

n n

= ∑ = 1 + 2 +…..+ K

K K

Central line for chart = =∑

K

Step 6: Determine the upper and lower control limits for . i.e.

UCL = + A2 R

LCL = - A2 R

Where A2 is a constant that depends on sample size (n). (Refer Appendix B for values of constant

A2).



Step 7: Test for homogeneity: Compare all individual values of with the values of UCL and

LCL. If any of the values of X falls outside these values, discard that sample and compute central line,

UCLR and LCLR for the remaining samples. Repeat test for homogeneity and other steps till homogeneity

is reached.



Step 8: Construct the (mean) chart and the ‗R‘ (range) chart and plot the values and ‗R‘ values

in the respective charts,

The control limits represent the range between which all points are expected to fall if the process

is in ―Statistical control‖. If any points fall outside the control limits or if any unusual patterns are

observed, then some special (or assignable) cause has probably affected the process. Then the process





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should be studied to determine the cause. If no assignable causes are found, the variations are due to

common (chance) causes.



USING MEAN AND RANGE CHARTS



In determining whether a process is in statistical control, the R-chart is always analysed first. Since the

control limits on chart depend on the average range (R), special (or assignable) causes in the R-chart may

produce unusual pattern in the chart even when the centering of the process is in control. Once the

statistical control is established in the R-chart, attention may turn to the X-chart. The - chart is sensitive

to shifts in the process mean whereas the R-chart is sensitive to shifts in the process standard deviation.

Consequently, by using both charts simultaneously, we can track changes in the process distribution.



Interpreting patterns in control charts: When a process is in statistical control, the points on a control

chart fluctuate randomly between the control limits with no recognizable pattern. The following checklist

provides a set of general rules for examining a process to determine whether it is in control:

1. No points are outside control limits.

2. The number of points above and below the central line is about the same.

3. The points seem to all randomly above and below the central line and

4. Most points, but not all, are near the central line, and only a few are close to the control limits.



The underlying assumption behind these rules is that the distribution of sample means is normal. This

assumption follows from the central limit theorem of statistics, which states that the distribution of

sample means approaches a normal distribution as the sample size increases regardless of the original

distribution. Of course, for small sample sizes (4 or 5), the distribution of the original data must be

reasonably normal for this assumption to hold. The upper and lower control limits are computed to be

three standard deviations from the overall mean. Thus, the probability that any sample mean falls outside

the control limits is very small.

Since the normal distribution is symmetric, about the same number of points fall above as below the

central line. Also, since the mean of the normal distribution is the median, about half the points fall on

either side of the central line. Finally, about 68 percent of a normal distribution falls within one standard

deviation of the mean, thus, most-but not all-points should be close to the central line. These

characteristics will hold provided that the mean and variance of the original data have not changed during

the time the data were collected, that is, the process is stable.



Process Monitoring and Control: After a process is determined to be in control, the charts should

be used on a daily basis to monitor production, identify any special causes that might arise and make

corrections as necessary. The chart tells when to leave the process alone! Unnecessary adjustments to a

process result in a non-productive labour, reduced production and increased variability of output.



It is more productive, if operators themselves take the samples and chart the data. In this way, they can

react quickly to changes in the process and immediately make adjustments. Operators must be trained to

do this effectively.



Improvements in conformance typically follow the introduction of control charts on the shop floor,

particularly when the process is labour intensive. Apparently, management involvement in the operator‘s

work often produces positive behavioural modifications. Under such circumstances and as good practice,

management and operators should revise the control limits periodically and determine a new process

capability as improvements take place.





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Control charts are designed to be used by production operators rather than by inspectors or quality

control personnel. Under the philosophy of statistical process control, the burden of quality rests with the

operators themselves. The use of control charts allows operators to react quickly so special causes of

variations. The range is used in place of the standard deviation for the very reason that it allows shop-floor

personnel to easily make the necessary computations to plot points on a control chart using only simple

calculations.



Estimating process capability: After a process has been brought to a state of statistical control by

eliminating special causes of variation, the data may be used to estimate process capability. This approach

is not very accurate, because it uses the average range rather than the estimated standard deviation of the

original data. Nevertheless, it is a quick and useful method, provided that the distribution of the original

data is reasonably normal.



Under the assumption of normally, the standard deviation of the original data can be estimated as

follows:

σ = R

d2

Where d2 is the constant that depends on the sample size and is also given in Appendix B. process

capability is given by 6 σ. The natural variation of individual measurements is given by + 3 σ.



ILLUSTRATION OF CONSTRUCTION AND ANALYSIS OF CONTROL CHARTS:



Control charts for Silicon Wafer Production:



The thickness of silicon wafers used in the production of semiconductors must be closely controlled.

The tolerance of one such product is specified as + 0.005 inches. In one production facility, three wafers

were selected each hour and thickness measured carefully within one ten-thousandth of an inch. Exhibit

3.2 shows the results obtained for 25 samples. For example, the mean of the first sample is

14+70+22 = 113 = 44

3 3

The range of sample 1 is 70 – 22 = 48.

The calculations of the average range, overall mean and control limits are shown below.

R = ∑R = 676 = 27

K 25

= ∑ = 1221 = 48.8

K 25

For sample size n = 3, A2 = 1.023, D3 = 0 and D4 = 2.574.

A2R = 10.23 X 27 = 27.6.

UCL = + A2 R = 48.8 + 27.6 = 76.4

LCL = - A2 R = 48.8 – 27.6 = 21.2

UCLR = D4R = 2.574 х 27 = 69.5

LCLR = D3 R= NIL as D3 = 0

The central line, upper and lower control limits for chart and ‗R‘ chart are drawn in Exhibit 3.3.



Test for homogeneity : Examining the range chart first, it appears that the process is in control since

all range values for 25 samples fall within the control limits for range (i.e. between 0 and 69.5).In the

chart, however, sample 17 lies above the upper control limit (i.e. value for sample 17 which is 94 is more

than the upper control limit for = 76.4). Hence the data regarding sample 17 should be eliminated from

the control chart calculations. The calculation for modified values of central line, upper and lower control



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limits are shown below after eliminating sample number 17, thereby reducing the samples to 24 (i.e. K =

24).



R = ∑R = 633 = 27.6

K 24

= ∑ = 1227 = 47.0

K 24

Central line for chart = = 47.0

Upper control limit UCL = + A2R = 47.0 + (1.073 х 27.6) = 75.2

LCL = – A2R = 47.0 - (1.073 х 27.6) = 18.8

Central line for ‗R‘ chart = R = 27.6.

Upper control limit UCLR = D4R = 2.574 х 27.6 = 71.0

Lower control limit LCLR = D2R = 0 х 27.6 = 0



Test for homogeneity: All range values for 24 samples lie between zero and 71.0 which are the

control limits for range. All values for 24 samples lie between 18.8 and 75.2 which are the control limits

for mean ( ). Hence the test for homogeneity is satisfied and the resulting charts appear to be in control.

Exhibit . : Silican Water Thickness Data AND Exhibit . : Initial Control Chart

Exhibit . : Revised Control Chart

Exhibit 3.4 shows the revised control chart after testing for homogeneity.



Estimating Process Capability: After a process has been brought to a state of statistical control by

eliminating special causes of variation, the data may be used to estimate process capability. Under the

normality assumption, the standard deviation of the original data can be estimated as follows: σ =R/d2

where d2 is a constant that depends on the sample size and is given in Appendix B. Process capability is

given by 6σ.The natural variation of individual measurements is given by X + 3 σ .



Estimating the process capability for the Silicon Wafer Thickness Example

For the sample of size 3, d2 = 1.693 from Appendix B. The calculations for 24 samples are:

R = ∑ R = 663 = 27.6

K 24

= ∑ = 1227 = 47.0

K 24

A2R = 1.023 X 27.6 = 28.2.

UCLX = + A2R = 47.0 + (1.073 X 27.6) = 75.2

LCLX = – A2R = 47.0 - (1.073 X 27.6) = 18.8

UCLR = D4R = 2.574 X 27.6 = 71.0

LCLR = D3R = 0 X 27.6 = NIL.

ULX and LLX represents the upper and lower limit on individual observations based on

3σ limits.

ULX = = 3 R = 47.0 + 3 х 27.6

d2 1.693

= 47.0 + 48.9 = 95.9

LLX = - 3 R = 47.0 – 48.9 = - 1.9

d2

The zero point of the data is the lower specification, meaning that the thickness is expected to vary

from 0.0019 below the lower specification to 0.0959 above the lower specification.



The calculation of process capability index



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6σ= 6 R = 97.8

d2

The process capability index, Cp = UTL -LTL

6σ

UTL = Upper tolerance limit = 100 in this example.

LTL = Lower tolerance limit = 0 in this example.

Cp = 100 – 0 = 1.02

97.8

However, the lower and upper capability indexes are

Cpl = μ – LTL = 47 – 0 = 0.96

3σ 48.9

Cpu = UTL - μ = 100 – 47 = 1.08

3σ 48.9

This analysis suggests that both the centering and the variation must be improved.

If the individual observations are normally distributed, then the probability of being out of

specification can be computed. The data is assumed to be normal. The mean is 47 and the standard

deviation σ = 97.8 / 6 = 16.3. Exhibit 3.5 shows the calculation for Specification limits of 0 and 100.

Exhibit . : Process Capability Probability Computation









In Appendix, the area between 0 and the mean (47) is 0.4980 (corresponding to the value of Z = 2.88),

which means 0.2 percent of the output would be expected to fall below lower specification. The area to

the right of 100 is approximately zero. Therefore, all the outputs can be expected to meet the upper

specification.



Control limits are often confused with specification limits. Specification dimensions are usually stated

in terms of individual parts for ―hard‖ goods such as a automotive hardware. However, in other

applications such as chemical processes, specifications are stated in terms of average characteristics. Thus,

control charts might mislead one into thinking that if all sample averages fall within the control limits, all

output will be conforming. This assumption is not true. Control limits relate to average, while

specification limits relate to individual measurements. A sample average may fall within the upper and

lower control limits even though some of the individual observations are out of specification.

Since σ = σ control limits are narrower than the natural variation in the process and do not

√n

represent process capability.



SPECIAL CONTROL CHARTS FOR VARIABLES DATA (‗X‘ AND ‗S‘ CHARTS)



An alternative to using the R-chart along with X chart is to compute and plot the standard deviation ‗s‘

of each sample. Although the range has traditionally been used, (since it involves less computational effort

and is easier for shop-floor personnel to understand), using‗s‘ rather than ‗R‘ has its advantages. The

sample standard deviation is a more sensitive and better indicator of process variability, especially for

larger sample sizes. Thus, when tight control of variability is required, ‗s‘ should be used.





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The sample standard deviation is computed as









S=



To construct an‗s‘ chart, compute the standard deviation for each sample. Next, compute the average

standard deviation s by averaging the sample standard deviations over all samples. Control limits for the '

s‘ chart are given by

UCL s = B4s

LCL s = B3s

Where B3 and B4 are constants found in Appendix B. For the associative chart, the control limits

derived from the overall standard deviation are:

UCLx= X + A3s

LCLx = X – A3s

Where A3 is a constant found in Appendix .



Charts for individuals (Sample size of one number):

Because of the development of automated inspection for many processes, every item produced can be

now easily inspected and quality characteristics measured on every time produced. Hence, the sample size

for process control is n = 1 and a control chart for individual measurements also called chart – can be

used.



With individual measurements, the process standard deviation can be estimated and three sigma

control limits used. R/d2 provides an estimate of the process standard deviation. Thus, an ‗ -chart‘ for

individual measurements would have three sigma control limits defined by

UCLX = + 3 R

d2



LCLX = -3 R

d2

Sample of size 1, however, does not furnish enough information for process variability measurements.

However, process variability can be determined by using a moving average of ranges or a moving range of

n successive observations. For example, a moving range for n = 2 is computed by finding the absolute

difference between two successive observations. The number of observations used in the moving range

determines the constantd2, hence for n = 2, from Appendix B, d2 = 1.128. The moving range chart has

control limits defined by

UCLR = D4 R

LCLR = D3 R

Which is comparable to the ordinary range chart.



Illustration of ‗x‘ chart with moving range:



Consider a set of observations measuring the percentage of cobalt in a chemical process as given

below:







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Observation Value



1 3.75

2 3.8

3 3.7

4 3.2

5 3.5

6 3.05

7 3.5

8 3.25

9 3.6

10 3.1

11 4.0

12 4.0

13 3.5

14 3.0

15 3.8

16 3.4

17 3.6

18 3.1

19 3.55

20 3.65

21 3.45

22 3.30

23 3.75

24 3.5

25 3.4



The moving average is computed as shown by taking absolute values of successive range and using the

constants in Appendix B. For example, the first moving range is the absolute difference between the first

two observations.

i.e. |3.75 – 3.80| = 0.05



The second moving range is computed as

|3.80 – 3.70| = 0.10

From these data, we find that LCLR = 0

UCLR = (3.267) (0.352) = 1.15

The moving averages are calculated as below:

Observation Value









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1

2 0.05

3 0.10

4 0.50

5 0.30

6 0.45

7 0.45

8 0.25

9 0.35

10 0.50

11 0.90

12 0.00

13 0.50

14 0.50

15 0.80

16 0.40

17 0.20

18 0.50

19 0.45

20 0.10

21 0.20

22 0.15

23 0.45

24 0.25

25 0.10



The moving range chart indicates that the process is in control since all the moving range values are

found to be in between the control limits of moving averages 9i.e. between zero and 1.15).



Next, the ‗x‘ chart is constructed for the individual measurements.

UCLX = + 3R = 3.498 + 3(0.352) = 4.43

d2 1.128

LCLX = - 3R = 3.498 – 3(0.352) = 2.58

d2 1.128



Since all individual values of x for 25 samples lie between the control limit values of x (i.e. between

2.56 and 4.43), the process appears to be in control.

Exhibit 3.6 shows the ‗x‘ chart and Moving Range Chart.



Some caution is necessary when interpreting patterns on the moving range chart. Points beyond

control limits are signs of assignable causes. Successive ranges are correlated and they may cause patterns

or trends in the chart that are not indicative of out – of – control situations. On the ‗x‘ chart, individual

observations are assumed to the uncorrelated, hence patterns and trends should be investigated.



Control charts for individuals have the advantage that specifications can be drawn on the chart and

compared directly with the control limits. Some disadvantages also exist. Individuals‘ chart are less

sensitive to many of the conditions that can be detected by X and ‗R‘ – charts, for example, a process

must vary a lot before a shift in the mean is detected. Also, short cycles and trends may appear on an

individual‘s chart and not on X and ‗R‘ chart. Finally, the assumption of normality of observations is more



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critical than for X and R-charts. When the normality assumption does not hold, greater chance for error is

present.









Exhibit . : The ‗x‘ Chart and Moving Range Chart









CONTROL CHARTS FOR ATTRIBUTES:



Attributes data assume only two values – good or bad, defective or non-defective, acceptable or

not acceptable, pass or fail and so on. Attributes usually cannot be measured, but they can be observed

and counted and are useful in many practical situations. For example, in printing packages for consumer

products, colour quality can be rated as acceptable or not acceptable, or a sheet of cardboard either is

damaged or not, a polished or painted surface of a job may be good or bad. Usually, attributes data are

easy to collect, often by visual inspection. However, one drawback in using attributes data is that large

samples are necessary to obtain valid statistical results.



Several different types of control chars are used for attributes data. The two major categories of

attributes charts are (i) those that measure the percent defectives in a sample – known as ―number of

defectives‖ charts and (ii) those that count the number of defects – called ―number of defects charts‖.

We must know the difference between the terms ―defectives‖ and ―defects‖. A defect is a single non-

conforming quality characteristic of an item. An item may have several defects. The term ―defectives‖

refers to items having one or more defects.



―Number of defectives‖ charts are of two types:

(i) Fraction defectives chart or fraction – non-conforming chart also known as p-chart for varying

sample size or constant samples size.

(ii) ‗np‘ chart also known as chart for the number – non-conforming, for constant sample size

only.



‗p‘ Charts (Fraction defectives chart):



A ‗p‘ chart monitors the proportion of non-conforming items produced in a lot. The ‗p‘ chart is

constructed by first gathering 25 to 30 samples of the attributes being measured. The size of the sample

should be large enough to have several non-conforming items. If the probability of finding a non-



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conforming item is small, a large sample size is usually necessary. Samples are chosen over time periods so

that any special causes that are identified can be investigated.



Let us suppose that k samples, each of size ‗n are selected. If ‗c‘ represents the number of non-

conforming or defectives in a particular sample, the proportion non-conforming or fraction defective is

c/n. let pi be the fraction defective in the ‗i‘ th sample, then the average fraction defective for the group of

k sample is

p = P1 + P2+…..+PK

k



This statistic reflects the average performance of the process. One would expect a high percentage of

samples to have a fraction defective within three standard deviations of p. An estimate of the standard

deviation is given by

σp = √ p(1 – P)

n

The central line is given by p and the upper and lower control limits are computed as:

UCLp = p + 3 σp

= p = 3 √ p(1 – P)

n

= p – 3 σp

= p – 3 √ p(1 – P)

n

If LCLp is less than zero (i.e., negative value), a value of zero is used.



Analysis of ‗p‘ chart: Points outside the control limits signify an out-of-control situation. Patterns and

trends should also be sought to identify special or assignable causes. However, a point on a p-chart below

the lower control limit or the development of a trend below the centre line indicates that the process

might have improved since the ideal is zero defective.



‗P‘ Chart for Variable Sample Size:



Often 100 percent inspection is performed on process output during fixed sampling periods. However,

the number of units produced in each sampling period may vary. In this case, the p-chart would have a

variable sample size. One way of handling this is to compute a standard deviation for each individual

sample. Thus, if the sample size for k samples is n1,n2,………nk, then central line = p

Upper Control limit = p + 3 √p (1-p)

Sample size

Lower Control limit = p – 3 √p (1-p)

Sample size

Where p = p1+p2+…..+pK

K

And p1 = c1/n1, p2 = c2/n2 …….pk = ck/nk

Where c1, c2 …ck are the number of defectives in samples 1 to k respectively.



An alternative approach is to use the average sample size n, to compute approximate control limits.

Using the average sample size, the control limits are computed as

UCLp = p + 3 √p (1-p)

n

LCLp = p – 3 √p (1-p)



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n

where n = n1+n2+….+nk

K

The disadvantages of this approach are:

1. Since the control limits are only approximate, points that are actually out of control may not

appear to be so on this chart.

2. Runs or non-random patterns are difficult to interpret because the standard deviation differs

between samples as a result of the variables simple sizes.



As a general guideline, the average sample method is used when the sample sizes fall within 25 percent

of the average.



np CHARTS FOR NUMBER NONCONFORMING FOR CONSTANT SAMPLE SIZE:



In the ‗p‘ chart, the fraction non-conforming or defective of the ith sample is given by pi =ci/n where

ci is the number found non-conforming (or defective and ‗n‘ is the sample size. This relationship can be

rewritten as ci = npi. That is the number of non-conforming is equal to the sample size times the

proportion non-conforming (or fraction defective).



Instead of using a chart for the fraction non-conforming, an equivalent alternative – a chart for the

number of non-conforming items (instead of proportion nonconforming or fraction defective) is useful.

Such a control chart is called as np chart. ‗



The ‗np‘ chart is a control chart for the number of non-conforming items in a sample. To use np

chart, the size of the sample must be constant. Equal sample sizes are not required for p-charts.



The ‗np‘ chart is a useful alternative to the ‗p‘ chart because it is often easier to understand for

production personnel that the number of non-conforming items is more meaningful than a fraction.



Also the computations are simpler.



The control limits for the np chart, like those for the ‗p‘ chart are based on the binomial probability

distribution. The centre line is the average number of non-conforming items per sample as denoted by np

which is calculated as

np = c1+c2+…..+ck

K

An estimate of the standard deviation is

Snp = √np (1-p)

Where p = np/n

Using three-sigma limits, the control limits are specified:

UCLnp = np + 3 √np (1-p)

LCLnp = np – 3 √np (1-p)



Example : Construct a fraction defective (p-chart) chart for the following data. The sample size is n =

100 for each sample of the 20 samples.



Sample Number of Sample Number of

Number Defectives Number Defectives





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1 6 11 6

2 5 12 1

3 0 13 8

4 1 14 7

5 4 15 5

6 2 16 4

7 5 17 11

8 3 18 3

9 3 19 0

10 2 20 4





Solution:

Calculation of fraction defective is shown below:

Sample Fraction Sample Fraction

Number Defective Number Defective



1 6/100 = 11 0.06

2 0.06 12 0.01

3 5/100 = 13 0.08

4 0.05 14 0.07

5 0.00 15 0.05

6 0.01 16 0.04

7 0.04 17 0.11

8 0.02 18 0.03

9 0.05 19 0.00

10 0.03 20 0.04

0.03

0.02





Central line p = 80/100 х 20 = 0.04

σp = √p (1-p)/n = √ 0.04 (1-0.04)/100 = 0.02

UCLp = p + 3 σp = 0.04 + 3 (0.02) = 0.10

LCLp = p – 3 σp = 0.04 – 3 (0.02) = -ve = 0



ILLUSTRATION OF CONSTRUCTION OF ‗p‘ CHART FOR VARYING SAMPLE SIZE:



The number of non-conforming or defectives for 20 samples of varying sample sizes are given below.

Construct a ‗p‘ chart.



Sample Number non- Sample Sample Number non- Sample

Number conforming Size Number conforming Size









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1 18 137 11 8 140

2 20 158 12 13 179

3 14 92 13 5 196

4 6 122 14 15 163

5 11 86 15 25 140

6 22 187 16 12 135

7 6 156 17 16 186

8 9 117 18 12 93

9 14 142 19 15 181

10 12 140 20 18 160



The fraction non-conforming or fraction defective is calculated for each sample as

P = Number non-conforming

Sample Size



The fraction non-conforming is as below:









Sample Fraction Sample Fraction

non- non-

conforming conforming



1 18/137 = 0.1314 11 0.0571

2 20/158 = 0.1266 12 0.0726

3 0.1522 13 0.0255

4 0.0492 14 0.0920

5 0.1279 15 0.1726

6 0.1176 16 0.0889

7 0.0385 17 0.0860

8 0.0769 18 0.0622

9 0.1273 19 0.0829

10 0.0845 20 0.1125





Calculation of standard deviation, central line (CL), upper control limit (UCLp) and Lower control limit

(LCLp) is given by:

Standard deviation Sp = √p(1-p)/n

CL = P =0.0909

UCLp = p + 3 Sp

LCLp = p – 3 Sp



The calculated values are listed as follows:



Sample Sp CL UCLp LCLp









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1 0.02045 0.0909 0.1646 0.0172

2 0.0228 0.0909 0.1596 0.0223

3 0.02997 0.0909 0.1806 0.0010

4 0.0260 0.0909 0.1690 0.0128

5 0.0310 0.0909 0.1840 0.0000

6 0.0210 0.0909 0.1540 0.0279

7 0.0230 0.0909 0.1600 0.0219

8 0.0265 0.0909 0.1707 0.0112

9 0.0274 0.0909 0.1732 0.0087

10 0.0241 0.0909 0.1633 0.0186

11 0.0243 0.0909 0.1638 0.0180

12 0.0214 0.0909 0.1554 0.0265

13 0.0205 0.0909 0.1526 0.0293

14 0.0225 0.0909 0.1585 0.0234

15 0.0243 0.0909 0.1638 0.0180

16 0.0247 0.0909 0.1652 0.0167

17 0.0210 0.0909 0.1542 0.0277

18 0.0206 0.0909 0.1530 0.0289

19 0.0213 0.0909 0.1551 0.0268

20 0.0227 0.0909 0.1591 0.0227



For the same data given in this problem, an alternative approach for calculation of CL,UCL p and LCLp

is given below:

CLp = p = ∑ non-conforming = C1+C2+…..+CK

∑ Sample Size n1+n2+…..+nK

= 18+20+…….. +18 = 271 = 0.0909

137+158+…. +160 2980

Upper Control limit UCLp = p + 3 √p(1-p)/n

Lower control limit LCLp = p – 3 √p(1-p)/n

Where n = average sample size = n1+n2+……+nk

K

= 2980/20 = 149

UCLp = 0.0909 + 3 √ 0.0909(1-0.0909)/149

= 0.1616

LCLp = 0.0909 – 3 √ 0.0909(1-0.0909)/149

= 0.0202



ILLUSTRATION OF CALCULATION FOR ‗NP‘ CHART



Construct the ‗np‘ chart for the following data. (Sample size = 100)





Sample Number

nonconforming









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1 3

2 1

3 0

4 0

5 2

6 5

7 3

8 6

9 1

10 4

11 0

12 2

13 1

14 3

15 4

16 1

17 1

18 2

19 5

20 2

21 3

22 4

23 1

24 0

25 1



np = ∑ non-conforming = ∑c

Total number of samples K

= 3+1+0…..+0+1 = 2.2

25

P = ∑c = 3+1+0+…. +0+1 = 2.2 = 0.022

nK 100 х 25 100

Standard deviation Snp = √np (1-p)

= √2.2 (1-0.022)

= √2.2 (0.978)

= √2.1510

= 1.4668

The control limits are computed as

UCLnp = np + 3 √np (1-p) = np + 3 Snp

= 2.2 + 3 (0.14668)

= 6.6

LCLnp = np – 3 √np (1-p)

= 2.2 – 3 (0.14668)

= -2.20 = Zero



CHARTS FOR DEFECTS:









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A defect is a single non-conforming characteristic of an item, while a defective is an item that has one

or more defects. In some situations, quality assurance personnel may be interested not only in whether an

item is defective but also in how many defects it has. Two charts can be applied in such situations:

(i) The ‗c‘ chart which is used to control the total number of defects per unit when sub-group size is

constant.

(ii) The ‗u‘ chart which is used to control the average number of defects per unit, when the sub-group

sizes are variable.

The ‗c‘ chart is based on the poisson probability distribution. To constructs a ‗c‘ chart, first estimate

the average number of defects per unit ‗c‘ by taking at least 25 (K) samples of each size (n), counting the

number of defects per sample (c) and finding the average (c).

The standard deviation of the Poission distribution is the square root of the mean.

The computations are as below:

The number of sub-groups = K

Sample size per sub-group = n

Number of defects in sub-groups = C1,C2,…..,Ck.

Average number of defects C = C1+C2+…..+CK

K

Standard deviation Sc = √c

Central line (CL) = c

Upper control limit UCLC = c + 3 √c

Lower Control limit LCLC = c - 3 √c

Illustration : Construct the number of defects chart (‗c‘ chart) for the data given below.

Subgroup Number of Defects

1 2

2 3

3 0

4 1

5 3

6 5

7 3

8 1

9 2

10 2

11 0

12 1

13 0

14 2

15 4

16 1

17 2

18 0

19 3

20 2

21 1

22 4

23 0

24 0

25 3



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Calculation of central line, upper and lower control limits is given below:

Central line (CLC) = C = ∑c = c = 2+3+…..+0+3

K 25

C = 45 = 1.8

25

Upper Control limit UCLC = c + 3 √c

= 1.8 + 3 √1.8

= 5.82

Lower Control limit LCLC = c - 3 √c

= 1.8 – 3 √1.8

= - 2.2 or zero.

As long as the sub-group size is constant, a ‗c‘ chart is appropriate. In many cases, however, the sub-

group size is not constant or the nature of the production process does not yield discrete, measurable

units. In such cases, a standard unit of measurement is used such as defects per unit. The control chart

used in such situations is called a u-chart.



The variable ‗u‘ represents the average number of defects per unit of measurement, that is u = c/n

where ‗n‘ is the size of the sub-group. The computations of central line, upper and lower control limits are

as below:



Central line (CLC) = u = ∑u = u1 + u2 +………+uK

K K

Where u1 = c1 , u2 = c2 ……………., uK = cK

n1 n2 nK

Alternatively, u = c1+c2+…..+cK

n1+n2+….+nK

The standard deviation of the ith sample is given by

sU = √ u/n1 or sU = √u/sample size

Upper control limit UCLU = u + 3 √u/sample size

Lower control limit LCLU = u – 3 √u/sample size

For the ith sample,

UCLU = u + 3 √u/ni

LCLU = u – 3 √u/ni

Note that if the size of the sub-group varies, the control limits also will vary.



ILLUSTRATION: Construct the ‗u‘ chart for the following data :



Subgroup Number Sample Size

of defects









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1 8 92

2 15 69

3 6 86

4 13 85

5 5 123

6 5 87

7 3 74

8 8 83

9 4 103

10 6 60

11 7 136

12 4 80

13 2 70

14 11 73

15 13 89

16 6 129

17 6 78

18 3 88

19 8 76

20 9 101



Calculation of defects per unit (u):

Defects per unit (u) = Number of defects = c

Sample Size n

Subgroup Defects per unit (u)

1 8/92 = 0.0870

2 15/69 = 0.2174

3 0.0698

4 0.1529

5 0.0407

6 0.0575

7 0.0405

8 0.0964

9 0.0388

10 0.0100

11 0.0515

12 0.0500

13 0.0286

14 0.1507

15 0.1461

16 0.0465

17 0.0769

18 0.0341

19 0.1053

20 0.0891



Central line u = ∑U = 1.5898 = 0.0794

K 20





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Alternatively u = u1+u2+…..+uK = 142 = 0.0796

n1+n2+…..+nK 1782

Calculation of Upper and Lower limits:

The standard deviation

sU = √u/sample size

Upper Control Limit for subgroup 1

= u + 3 √u/sample size

= 0.079 + 3 √0.079/92

= 0.079 + 3 (0.029)

= 0.079 + 0.087

= 0.166

Lower Control Limit for subgroup 2

= u – 3 √u/sample size

= 0.079 – 0.087 = - ve = 0

The calculation of upper and lower control limits are given below:

Subgroup LCLU UCLU









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0 0.166

2 0 0.176

3 0 0.165

4 0 0.166

5 0.0016 0.151

6 0 0.175

7 0 0.172

8 0 0.167

9 0 0.158

10 0 0.183

11 0.0053 0.147

12 0 0.169

13 0 0.175

14 0 0.173

15 0 0.164

16 0.0034 0.149

17 0 0.170

18 0 0.164

19 0 0.171

20 0 0.158









APPLICATION OF ‗C‘ CHARTS AND ‗U‘ CHARTS:

One application of ‗c‘ charts and ‗u‘ charts is in a quality rating system for rating vendors (suppliers).

When some defects are considered to be more serious than others, the defects can be rated or categorized

into different classes. For instance,

A – very serious defect



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B – serious defect

C – moderately serious defect and

D – not serious defect.

Each category can be weighted using a point scale, such as 100 for A, 50 for B, 10 for C, and 1 for D.

These points or demerits can be used as the basis for a ‗c‘ chart of a ‗u‘ chart that would measure total

demerits of demerits per unit respectively. Such charts are often used for internal quality control and as a

means of rating suppliers.



Choosing between ‗c‘ chart and ‗u‘ chart:



Choosing ‗C‘ chart and ‗U‘ chart applies to situations in which the quality characteristics inspected do

not necessarily come from discrete units. The key issue to consider is whether the sampling unit is

constant. For example, suppose that an electronics manufacturer produces printed circuit boards. The

boards may contain various defects such as faulty components and missing connections. Because the

sampling unit – the printed circuit board is constant (assuming that all boards are the same). A ‗C‘ chart is

appropriate. If the process produces printed circuit boards of varying sizes with different numbers of

components and connections, then the ‗u‘ chart would be the choice.



Summary of Control chart construction:



Exhibit 7.7 summarises the formulae used for constructing the different types of control charts

discussed in this chapter. Exhibit 7.8 provides a summary of guidelines for chart selection.





EXHIBIT 7.7 : SUMMARY OF CONTROL CHART FORMULA

TYPE OF CHART LCL CL UCL



(with R) - A2R + A2R

R D3R R D4R

P P – 3 √p (1-p)/n p P + 3 √p (1-

(with S) - A3S p)/n

S B3S s + A4S

X - 3 R/d2 B4S

np np – 3 √np (1-p) np + 3 R/d2

c c – 3 √c c np + 3 √np (1-p)

u u – 3 √u/n u c +3 √c

u + 3 √u/n









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Source: James R.Evans, et al, op.cit., p.693.



CONTROL CHART SELECTION









EXHIBIT 7.8 : CONTROL CHART SELECTION







Quality Characteristic





No X and moving Type of

n>1 average charts Defective AttributesDefect

?

Yes

No Yes Yes

X and R p or

n>10 Constant Constant

Yes charts Sample

np sampling

No

Size chart No

unit ‘c’

Chart





p chart with

X and S variable ‘u’

charts sampling size Chart









DESIGN

Source: James R.Evans, et al, op.cit., p.693.



DESIGNING CONTROL CHARTS:

1. The basis for sampling

2. The sample size

3. The frequency of sampling and

4. The location of the control limits.







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Basis for Sampling: The purpose of a control chart is to identify the variation in a system that may

change over time. In determining the method of sampling, samples should be chosen to be as

homogeneous as possible so that each sample reflects the system of common (or chance) causes or

assignable causes that may be present at that point of time. If assignable causes are present, the chance of

observing differences between samples should be high while the chances of observing differences within a

sample should be low. Samples which satisfy these criteria are called rational sub-groups.

Rational sub-groups can be constructed by using consecutive measurements over a short period of

time. Consecutive measurements minimize the chance of variability within the sample while allowing

variation between samples to be detected. This approach is useful when control charts are used to detect

shifts in process level. Also, care should be taken not to overlap production shifts, different batches of

materials and so on, when selecting the basis for sampling. The sample should be selected carefully so as

not bias the results.



Sample Size: Sample size is a second critical design issue. A small sample size is desirable to ensure that

the opportunity for within-sample variation due to assignable (or special) causes would be minimum. This

issue is important because each sample should be representative of the state of control at one point in

time. Also, the cost of sampling should be kept low. (In a strict accounting sense, the time spent by an

operator taking sample represents non-productive time). On the other hand, control limits are based on

the assumption of a normal distribution of the sample means. If the process is not normal, this

assumption is valid only for large samples. Large samples also allow smaller changes in process

characteristics to be detected with higher probability. In practice, samples of about five have been found

to work well in detecting process shifts of two standard deviations or larger. To detect smaller shifts in the

process mean, larger sample sizes of 15 to 25 must be used.



For attributes data, too small a sample size can make a ‗p‘ chart meaningless. The proper sample size

should be determined statistically, (even though some guidelines suggest use of at least 100 observations)

particularly when the true proportion of non-conformance is small. If ‗p‘ is small, ‗n‘ should be large

enough to have a high probability of detecting at least one non-conformance.



Sampling Frequency: The third design issue is the sampling frequency. Taking large samples on a

frequent basis is desirable but clearly not economical. No hard and fast rules exist for the frequency of

sampling. Samples should be close enough to provide an opportunity to detect changes in process

characteristics as soon as possible and reduce the chances of producing a large amount of non-

conformance output.



Location of Control Limits: This issue in the design of control charts is closely related to the risk

involved in making an incorrect assessment about the state of control. Two types of errors would occur-

one called type I error when an incorrect conclusion is reached that a special (or assignable) cause is

present when infact one does not exist and the other called type II error which occurs when special (or

assignable) causes are present but are not signaled in the control chart because the plotted points fall

within the control limits by chance. Since non-conforming products have a greater chance to be produced,

a cost will eventually be incurred as a result. The size of a type I error depends only on the control limits

that are used, the wider the limits, the less chance of a point falling outside the limits and consequently,

the smaller is the chance of making a type I error. On the other hand, a type II error depends on the width

of the control limits, the degree of which the process is out of control and the sample size. For a fixed

sample size, wider control limits increase the risk of making a type II error.



The traditional approach of using three-sigma limits implicitly assumes that the cost of a Type I error is

large relative to that of a Type II error. Certain costs are associated with making both Type I and Type II



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errors. A Type I error results in unnecessary investigation for an assignable cause, including costs of lost

production time and special testing. A Type II error can be more significant. If an out-of-control process

is not recognized, defectives that are produced may result in higher costs of scrap and rework in later

stages of production and after the finished goods reach the customer.



The costs associated with Type I and Type II errors conflict as control limits change. The tighter the

control limits, the higher the cost of a Type I error and lower the cost of Type II error. The costs

associated with sampling and testing may include lost productive time when the operator takes sample

measurements, performs calculations and plots the points on the control charts. If the testing is

destructive, the value of lost products would also be included. Thus, larger sample sizes and more frequent

sampling results in higher costs.



The sample size and frequency also affect the cost of Type I and Type II errors. As the sample size or

frequency is increased, both Type I and Type II errors are reduced since better information is provided

for decision making. Box 3.1 lists the economic decisions for control chart construction.







BOX 7.1 : ECONOMIC DECISIONS FOR CONTROL CHART CONSTRUCTION

SOURCE COST SAMPLE SIZE SAMPLING CONTROL LIMITS

FREQUENCY

TYPE I ERROR LARGE HIGH WIDE

TYPE II ERROR LARGE HIGH NARROW

SAMPLIG AND TESTING SMALL LOW -









In the economic design of control charts, the costs of Type I error and Type II error must be

considered simultaneously. Most models for such decisions can become quite complex. As a practical

matter, one often uses judgement about the nature of operations and the costs involved in making these

decisions. Raymond Mayer suggests the following guidelines:



1. If the cost of investigating an operation to identify the cause of an apparent out-of-control condition is

high, a Type I error becomes important and wider control limits should be adopted. Conversely if that is

low, narrower limits should be selected.



2. If the cost of the defective output generally by an operation is substantial, a Type II error is serious, and

narrower control limits should be used. Otherwise, wider limits should be selected.



3. If the cost of a Type I error and the cost of a Type II error for a given activity are both significant, wide

control limits should be chosen and consideration should be given to reducing the risk of a Type II error

by increasing the sample size. Also, more frequent samples should be taken to reduce the duration of any

out-of-control condition that might occur.



4. If past experience with an operation indicates that an out-of-control condition arises quite frequently,

narrower control limits should be favoured because of the large number of the large number of





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opportunities for making a Type II error. In the event that the probability of an out of control condition is

small, wider limits are preferred.



IMPLEMENTING STATISTICAL PROCESS CONTROL:

Control charts provide significant benefits to a company. Although control charts were first developed

and used in manufacturing context, they are easily applied to service organisations. The major difference is

the quality characteristic that is controlled. Most service processes can be improved through the

appropriate application of control charts.



Overcoming Implementation Barriers:



The various reasons for failure of control charts in organizations are:

(i) Operations might not trust a new tool.

(ii) Old methods, such as correcting a process only if production is out of specification or adjusting a

machine after every batch, are difficult habits to break.

(iii) Operator did not receive enough training or practice or did not fully understand the benefits.

(iv) Lack of a corrective action plan. The concept of control requires that assignable cause be identified

and corrected. Failure to act on control chart signals increases variability reduces importance of the chart

and undermines the entire quality program.

(v) Not using the appropriate control chart. For example, using an attribute chart when a variable chart is

more appropriate leads to loss of sensitivity, loss of information for corrective action and interpretation of

quality in terms of defects rather than uniformity to a target.



Successful implementation of SPC requires five key elements. They are:

(i) Commitment of management. If not supported by management, operators will quickly see that they are

wasting their time and stop using SPC.



Frequent updating of the control charts as elements of the process change and as assignable causes are

eliminated. An outdated chart is useless. The top management must commit financial resources for

measuring instruments, calculations, computers and software and training of workers to learn the

mechanics of SPC.



(ii) A successful SPC project work needs some individual who has both the responsibility and authority to

make them work.

(iii) Only one problem should be addressed at a time. When a company introduces SPC for the first time,

it makes sense to introduce it in a few selected projects or departments instead of introducing in the entire

plant.



(iv) Education and training of all employees is absolutely necessary to make every one understand why

SPC is being used and what it can do to improve quality and help the worker to do a better job. Workers

must understand that SPC will benefit their job. Workers must understand that SPC will benefit them and

is not a scheme set up by the management to blame them.



(v) The gauging and measurement system must first be evaluated for accuracy, repeatability and

reproducibility before implementing SPC.



The Six Step Process used for Implementing SPC:



(i) Define the Process : Use flow charts to provide visual means of characterizing a process.



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(ii) Identify characteristics to study: What are the important quality parameters? Use Paerto Analysis

to prioritise the characteristics of the process. Find whether the quality characteristics are machine

controllable or operator controllable.

(iii) Determine the ability to measure the characteristic: Assess the measurement tools. If the

measurement system is not satisfactory, all subsequent SPC activities may be useless.

(iv) Perform capability studies: One of the basic purposes of SPC is to establish a state of statistical

control so that process capability can be determined. Workers must be taught the concept of variation, the

use of control charts and their role in capability studies.

(v) Study Process Performance: Using control charts to monitor performance and identifying special

causes lead to identification of various sources of variation and eventually to their elimination.

(vi) Implement process control: Ensure employee participation in previous steps to have real time

control of the processing.



PROCESS CAPABILITY:



The determinations of process capability begins only after the process has been brought to a state of

statistical control. A process is said to be in statistical control when the only sources of variation in the

system are common or chance causes. Identifying special or assignable causes and taking corrective action

to eliminate the special or assignable causes leads to a process in statistical control.



Process Capability refers to the ability of the process to meet the design specifications for a product

or services. Design specifications often are expressed as a nominal value or target and a tolerance or

allowance above or below the nominal value of 1000 hours and a tolerance or + 200 hours. This tolerance

gives an upper specification of 1200 hours and a lower specification of 800 hours.



The process producing the bulbs must be capable of doing so within these design specifications,

otherwise it will produce a certain proportion of defective bulbs.



Process Capability is the range over which the natural variation of process occurs as determined by

the system of common or chance causes, that is what the process can achieve under stable conditions.

Process capability only makes sense if all the special or assignable causes of variation have been eliminated

and the process is in a state of statistical control. The capability of a process is usually compared to design

specifications and measured by a proportion of the output that can be produced within the specifications.



Process capability is important to both product designers and manufacturing engineers. Knowing

process capabilities allows one to predict quantitatively how well a process will meet specifications and to

specify equipment requirements and the level of control necessary.



Process capability has three important components: (i) the design specifications, (ii) the centering of

the natural variation and (iii) the range or spread of variation.



Exhibit 7.9 illustrates four possible outcomes that can arise when natural process variability is

compared with design specifications.



In figure (a) in Exhibit 3.9, the specifications are looser than the natural variation and we can expect

that the process will always produce conforming products as long as it remains in control.

In figure (b), the natural variation and specifications are the same. A small percentage of non-

conforming products might be produced, thus the process should be closely monitored.





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In figure (c), the range of natural variability is larger than the specification, thus the current process

could not meet specifications even when it is under control.



In figure (d), the capability is the same as in (b) but the process average is off-centre. If no corrective

action is taken, a substantial portion of output will fall outside the specifications limit even though the

process has the inherent capability to meet specifications.



Exhibit . : Natural Variability Versus Specification for Process Capability









Process Capability Studies:



A process capability study is a carefully planned study designed to yield specific information about the

performance of a process under specified operating conditions. Typical questions that are asked in a

process capability study are:

 Where is the process centred?

 How much variability exists in the process?

 Is the performance relative to specifications acceptable?

 What proportion of output will be expected to meet specifications?

 What factors contribute to variability?



Reasons for conducting a process capability study:

1. Manufacturing may wish to determine a performance base line for a process.

2. To prioritise projects for quality improvement.

3. To provide statistical evidence of quality for customers.

4. To evaluate a new equipment by the purchasing department.

5. To compare different suppliers by the purchasing department.

6. To determine the adequacy of R & D pilot facilities; and

7. To evaluate new processes.



Three types of studies conducted are:

1. A peak performance study which determines how a process performs under ideal conditions.

2. A process characterization study which is designed to determine how a process performs under

actual operating conditions.

3. A component variability study which assesses the relative contribution of different sources of total

variation.

Six steps in a process capability study are:

1. Choose a representative machine or segment of the process.

2. Define the process conditions.

3. Select a representative operator.

4. Provide materials that are of standard grade with sufficient materials for uninterrupted study.



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5. Specify the gauging or measurement method to be used.

6. Provide for a method of recording measurements and conditions, in order, on the units produced.

To obtain useful information, the sample size should be fairly large, generally at least 100. Two

statistical techniques are commonly used to evaluate process capability. One is the frequency distribution

and histogram, the other is the control chart.



PROCESS CAPABILITY RATIO



A process is capable if it has a process distribution whose extreme values fall within the upper and

lower specifications for a product or service. As a general rule, most values of a process distribution fall

within plus or minus three standard deviation (+ 3σ) of the means. This means, the range of values of the

quality measures generated by the process is approximately six standard deviation. Hence, if a process is

capable, the difference between the upper and lower specifications called the tolerance width must be

greater than six standard deviations (process variability).



The process capability ratio Cp = Upper specification – Lower specification

6σ

Where σ is the standard deviation of the process distribution.

If cp > 1.0, the tolerance range is greater than the range of actual process outputs. If Cp 1.

The Cpk index: The Cpk index takes into consideration the location of the process mean which

is not so in the case of Cp index.





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Whereas the Cp index represents the process potential, the Cpk value represents the actual capability

of the process. With the existing parameter values, it measures process performance capability ratio.



Some controversy exists over Cp and Cpk as measures of process capability, particularly with respect

to the economic loss function philosophy of Taguchi. For example, a process may have a high Cpk even

when its means is off target and close to the specification limits as long as the process spread is small.

Several alternative measures have been proposed. One is to adjust Cp by a factor (1 – K) as follows:

Cpk = Cpk (1 – k)

Where k = 2|mean – target|

Tolerance



When the sample mean is equal to the target, k = 0 and Cpk = Cp. As the sample mean deviates from

the target, the absolute difference between them increases and k increases. Specification limits are used

only to determine the tolerance, thus the focus of this measure is on the target value rather than on

acceptable specification limits.



Another index that has been proposed is

Cpk = Cp

√1 + (mean – target) 2 /σ2



This measure also accounts for deviations from the target value in a quadratic loss fashion based on the

Taguchi loss function which was discussed in the earlier chapter.



Process capability indexes depend on the assumption that the distribution of output is normally

distributed. When this is not the case such as when the output is affected by tool wear and exhibits a

highly skewed distribution, process capability indices can be below 1 even though all measurements are

within specifications limits.



SIX SIGMA QUALITY



One might think that having the natural tolerance equal to the design tolerance would mean good

quality. If we assume that the process output is represented by a normal distribution, about 99.73% of the

output is contained within bounds that are 3 standard deviations (3 σ) from the mean. As shown in

Exhibit 7.10, these are represented as the lower and upper tolerance limits (LTL & UTL).



Exhibit . : Process output represented by a normal distribution









The normal distribution is characterized by two parameters: the mean and the standard deviation. The

mean is a measure for the location of the process. If the product specification limits are 3 standard

deviation from the mean, the proportion of the non-conforming product is about 0.27% (or 2700 ppm).

On the surface, it appears to be a good process, but appearance can be deceiving. Consider what such a

level of quality really means:

 at least 20,000 wrong drug prescriptions each year

 more than 15,000 babies accidentally dropped each year by nurses and obstetricians



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 no electricity and water for about nine hours each year.

 2000 lost pieces of mail each hour.

 500 incorrect surgical operations each week.

Are we satisfied with such quality?



For a product to be built virtually defect free, it must be designed to tolerance limits that are

significantly more than + 3 σ form the mean. In other words, the process spread is measured by + 3 σ has

to be significantly less than the spread between the upper and lower specification limits (USL & LSL).

Motorola‘s answer to this problem is six-sigma quality that is; process variability must be so small that the

specification limits are 6 standard deviations from the mean.



Exhibit 7.11 demonstrates this concept of six-sigma quality. If the process distribution is stable, that is,

it remains centred between the specifications limits, the proportion of the non-conforming product

should be only about 1.001 ppm on each tail.



In real world situations, the process distribution will not always be centred between the specification

limits, process shifts to the right or left are not uncommon. It can be shown that even if the process mean

shifts by as much as 1.5 standard deviations form the centre, the proportion of non-conforming will be

about 3.4 ppm. Comparing this to a three-sigma capability of 2700 ppm demonstrates the improvement in

the expected level of quality from the process. If we consider a product containing 1000 parts and we

design it for six-sigma capability, then an average of 0.0034 defect per product unit (3.4 ppm) is expected,

instead of the 2.7 defects expected with three-sigma capability. The cumulative yield from the process will

thus be about 99.66% - a vast improvement over the 6.72% yield in the three-sigma case.



Exhibit . : Six Sigma Capability









Box 7.2 : Six Sigma Qulaity

Six sigma quality is related to the normal distribution with sigma (σ) denoting the standard

deviation of the process. Motorola assumed that the process mean would experience a 1.5 σ shift

before a 3 σ change. Thus, six sigma corresponds to a + 4.5 σ deviation on either side of the mean

resulting in 3.4 ppm defects. (This can be verified by referring to the normal probability tables at + 4.5

σ).

The six sigma criterion is equivalent to a process capability of Cpk = 1.5. This can be seen by

referring to the formula for Cpk with USL – μ= 4.5 σ. As a result, the six sigma criterion will ensure

processes that are not just barely capable of producing the specifications but will provide somewhat

better process capability.

Source: Roger G Schroeder, ―Operations Management‖, Irwin-McGraw Hill Publications, p.165.



Establishing a goal of three sigma capability is acceptable at a starting point, however because it allows

the organization to set a base line for improvement. As management becomes more process oriented,

higher goals such as ―six-sigma capability‖ become possible. Such goals may require fundamental changes

in the management philosophy and the organizational culture.







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Box 7.3 : Six Sigma Quality – What TQM did for Motorola



Once you get good at something, such as TQM, there is no turning back. Consider what

happened when Motorola gained statistical control of its processes but still was not satisfied. Using

TQM tools, they achieved plus or minus three sigma (3σ) quality levels. Motorola then developed a

new system to achieve higher levels of quality of at least six sigma (6 σ).

Why a six sigma level of quality?. At this level, 99.99966 percent of all products will be ―good‖.

Furthermore, for every extra 9 after the decimal point, there is a corresponding 10 fold reduction in

non – conformities as shown below:

Quality Confidence Quality Risk Total Defects PPM change factor

(parts per million)

0.9 0.1 1,00,000 29,411

0.99 0.01 10,000 2,941

0.999 0.001 1,000 294

0.9999 0.0001 100 29

0.99999 0.00001 10 3

0.999999 0.000001 1 0.3









Motorola‘s six sigma program improves quality by changing the processes used in the design and

delivery of products. Hence, the six sigma program is like the process capability tools of Cp and

Cpk.



However, like these tools, six sigma improves the various processes by actively involving all the

design and delivery functions. It also draws on a wide range of tools, including:

 Design for manufacturability

 Statistical process control (SPC)

 Supplier SPC (the application of SPC at the supplier‘s site)

 Participative management practices.

 Part standardization and supplier certification and

 Computer simulation.



These tools identify and attack all the forms of variance that can affect quality and customers

who want quality products will perceive higher levels of value.

Source: Steven A. Melnyk and David R.Denzler, ―Operations Management‖, McGraw Hill, p.365.



CONCLUSIONS



One might wonder why a process shift of 1.5 σ would be allowed. Since many common statistical

process control plans are based on sample sizes that only allow detection of shifts of about 2 σ, it would

not be unusual for a process to drift this much and not be noticed. A quality level of 3.4 defects per

million can be achieved in several ways, for instance:

 With 0.5-sigma off-centering and 5 sigma quality

 With 1.0-sigma off-centering and 5.5 sigma quality

 With 1.5-sigma off-centering and 6 sigma quality





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CHAPTER VIII

CAPABILITY MATURITY MODEL INTEGRATION (CMMI)



EVOLUTION OF CMMI

Since 1991, CMMs have been developed for myriad disciplines. Some of the most notable include

models for systems engineering, software engineering, software acquisition, workforce management and

development, and integrated product and process development (IPPD).



Although these models have proven useful to many organizations in different industries, the use

of multiple models has been problematic. Many organizations would like their improvement efforts to

span different groups in their organizations. However, the differences among the discipline-specific

models used by each group, including their architecture, content, and approach, have limited these

organizations‘ capabilities to broaden their improvements successfully. Further, applying multiple

models that are not integrated within and across an organization is costly in terms of training, appraisals,

and improvement activities.



The CMM Integration project was formed to sort out the problem of using multiple CMMs. The

CMMI Product Team‘s initial mission was to combine three source models:



1. The Capability Maturity Model for Software (SW-CMM) v2.0 draft C [SEI 1997b]



2. The Systems Engineering Capability Model (SECM) [EIA 1998]1[1]



3. The Integrated Product Development Capability Maturity Model (IPD-CMM) v0.98 [SEI 1997a]



The combination of these models into a single improvement framework was intended for use by

organizations in their pursuit of enterprise-wide process improvement.



These three source models were selected because of their widespread adoption in the software

and systems engineering communities and because of their different approaches to improving processes

in an organization.



Using information from these popular and well-regarded models as source material, the CMMI

Product Team created a cohesive set of integrated models that can be adopted by those currently using

the source models, as well as by those new to the CMM concept. Hence, CMMI is a result of the

evolution of the SW-CMM, the SECM, and the IPD-CMM.









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HISTORY OF CMMI



Developing a set of integrated models involved more than simply combining existing model

materials. Using processes that promote consensus, the CMMI Product Team built a framework that

accommodates multiple disciplines and is flexible enough to support the different approaches of the

source models.









.

Figure 1.2: The History of CMMs



Since the release of CMMI v1.1, we have seen that this improvement framework can be applied

to other areas of interest [SEI 2002a, SEI 2002b]. To apply to multiple areas of interest, the framework

groups best practices into what we call ―constellations.‖ A constellation is a collection of CMMI

components that are used to build models, training materials, and appraisal documents.



Recently, the CMMI model architecture was improved to support multiple constellations and the

sharing of best practices among constellations and their member models. Work has begun on two new

constellations: one for services (CMMI for Services) and the other for acquisition (CMMI for

Acquisition). Although CMMI for Development incorporates the development of services, including the

combination of components, consumables, and people intended to meet service requirements, it differs

from the planned CMMI for Services (CMMI-SVC), which focuses on the delivery of services. The

CMMI models that have been available in the community prior to 2006 are now considered part of the

CMMI for Development constellation.









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Figure 1.1: The Three Critical Dimensions



Capability Maturity Model Integration (CMMI) is a process improvement approach that

provides organizations with the essential elements of effective processes. It can be used to guide process

improvement across a project, a division, or an entire organization. CMMI helps integrate traditionally

separate organizational functions, set process improvement goals and priorities, provide guidance for

quality processes, and provide a point of reference for appraising current processes.





CMMI INFORMATION SOURCES

Before you begin applying CMMI to your organization, collect information about it. The CMMI

Overview presentation provides a good summary of CMMI, and the Adoption page is a good starting

point for finding information most relevant to your situation.

WORLDWIDE ADOPTION

The SEI is excited about the response that organizations around the world are having to the

CMMI Product Suite. CMMI is being adopted worldwide, including North America, Europe, Asia,

Australia, South America, and Africa. This kind of response has substantiated the SEI's commitment to

the CMMI models and the Standard CMMI Appraisal Method for Process Improvement (SCAMPI).

SCAMPI incorporates the best ideas of several process-improvement appraisal methods. The

SCAMPI Class A method is being used successfully by many organizations. The emerging SCAMPI Class

B and Class C methods will extend the suite of SCAMPI methods. For more information about SCAMPI,

see CMMI Appraisals.

BENEFITS OF PROCESS IMPROVEMENT





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The following are some of the benefits and business reasons for implementing process

improvement:



 The quality of a system is highly influenced by the quality of the process used to acquire, develop,

and maintain it.

 Process improvement increases product and service quality as organizations apply it to achieve

their business objectives.

 Process improvement objectives are aligned with business objectives.



CMMI BENEFITS

The CMMI Product Suite is at the forefront of process improvement because it provides the latest

best practices for product and service development and maintenance. The CMMI models improve the

best practices of previous models in many important ways. CMMI best practices enable organizations to

do the following:



 more explicitly link management and engineering activities to their business objectives

 expand the scope of and visibility into the product lifecycle and engineering activities to ensure that

the product or service meets customer expectations

 incorporate lessons learned from additional areas of best practice (e.g., measurement, risk

management, and supplier management)

 implement more robust high-maturity practices

 address additional organizational functions critical to their products and services

 more fully comply with relevant ISO standards A



SCOPE OF CMMI FOR DEVELOPMENT



The CMMI for Development constellation consists of two models: CMMI for Development

+IPPD and CMMI for Development (without IPPD). Both models share much of their material and are

identical in these shared areas. However, CMMI for Development +IPPD contains additional goals and

practices that cover IPPD. Currently, only one model is published since the CMMI for Development

+IPPD model contains the full complement of practices available in this constellation, and you can derive

the other model from this material. If you are not using IPPD, ignore the information that is marked

―IPPD Addition,‖ and you will be using the CMMI for Development model. If the need arises or the

development constellation is expanded, the architecture will allow other models to be generated and

published. CMMI for Development is the designated successor of the three source models. The SEI has

retired the Software CMM and the IPD-CMM. EIA has retired the SECM. All three of these models are

succeeded by CMMI for Development.



The best practices in the CMMI models have gone through an extensive review process. CMMI

version 0.2 was publicly reviewed and used in pilot activities. The CMMI Product Team evaluated more

than 3,000 change requests to create CMMI version 1.0. Shortly thereafter, version 1.02 was released,

which incorporated several minor improvements. Version 1.1 incorporated improvements guided by

feedback from early use, with more than 1,500 change requests submitted as part of the public review,

and hundreds of comments as part of the change control process. CMMI version 1.2 was developed using

input from nearly 2,000 change requests submitted by CMMI users. More than 750 of those requests were

directed at CMMI model content. As you can see, not only is CMMI widely adopted, but it is improved

based on the feedback received from the community. CMMI for Development is a reference model that

covers the development and maintenance activities applied to both products and services. Organizations



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from many industries, including aerospace, banking, computer hardware, software, defense, automobile

manufacturing, and telecommunications, use CMMI for Development. Models in the CMMI for

Development constellation contain practices that cover project management, process management,

systems engineering, hardware engineering, software engineering, and other supporting processes used in

development and maintenance. The CMMI for Development +IPPD model also covers the use of

integrated teams for development and maintenance activities.



In CMMI, ―additions‖ are used to include material that may be of interest to particular users. For

the CMMI for Development constellation, additional material was included to address IPPD. The IPPD

group of additions covers an IPPD approach that includes practices that help organizations achieve the

timely collaboration of relevant stakeholders throughout the life of the product to satisfy customers‘

needs, expectations, and requirements [DoD 1996]. When using processes that support an IPPD

approach, you should integrate these processes with other processes in the organization. To support those

using IPPD-related processes, the CMMI for Development constellation allows organizations to

optionally select the IPPD group of additions.



When you select CMMI for Development +IPPD, you are selecting the CMMI for Development

model plus all the IPPD additions. When you select CMMI for Development, you are selecting the model

without the IPPD additions. In the text in Part One of this document, we may use ―CMMI for

Development‖ to refer to either of these models, for the sake of brevity.









CHAPTER IX



Total Quality Management Tools

Seven Basic Quality Tools

The Japanese began applying the thinking developed by Walter Shewhart and W Edward

Deming during the 1930s and 1940s. Japan's progress in continuous improvement led to the expansion of

the use of these tools. Kaoru Ishikawa, the then head of the Japanese Union of Scientists and Engineers

(JUSE), thus, decided to expand the use of these approaches in Japanese manufacturing in the 1960s with

the introduction of the seven quality control (7QC) tools. 7QC tools are fundamental instruments to

improve the quality of products. They are used to analyse the production process, identify major

problems, control fluctuations of product quality and provide solutions to avoid future defects.

These tools use statistical techniques and knowledge to accumulate data and analyse them. They

help organise the collected data in a way that is easy to understand. Moreover, by using 7QC tools,

specific problems in a process can be identified. The first is the check sheet, which shows the history

and pattern of variations. This tool is used at the beginning of the change process to identify the



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problems and collect data easily. The team using it can study observed data (a performance measure of a

process) for patterns over a specified period of time. It is also used at the end of the change process to

see whether the change has resulted in permanent improvement.

The Pareto chart is named after Wilfredo Pareto, the Italian economist who determined that

wealth is not evenly distributed. The chart shows the distribution of items and arranges them from the

most frequent to the least frequent, with the final bar being miscellaneous. The Pareto chart is used to

define problems, to set their priority, to illustrate the problems detected and determine their frequency in

the process. It is a graphic picture of the most frequent causes of a particular problem. Most people use it

to determine where to put their initial efforts to get maximum gain.

The cause and effect diagram is also called the "fishbone chart" because of its appearance and

the Ishikawa chart after the man who popularized its use in Japan. It is used to list the cause of particular

problems. Lines come off the core horizontal line to display the main causes; the lines coming off the

main causes are the sub causes. This tool is used to figure out any possible causes of a problem. It allows

a team to identify, explore, and graphically display, in increasing detail, all of the possible causes related to

a problem or condition to discover its root cause(s).

The histogram is a bar chart showing a distribution of variables. This tool helps identify the cause

of problems in a process by the shape as well as the width of the distribution. It shows a bar chart of

accumulated data and provides the easiest way to evaluate the distribution of data. Then there's the

scatter diagram, which shows the pattern of relationship between two variables that are thought to be

related. The closer the points are to the diagonal line, the more closely there is a one-to-one relationship.

The scatter diagram is a graphical tool that plots many data points and shows a pattern of correlation

between two variables.

Graphs are among the simplest and best techniques to analyse and display data for easy

communication in a visual format. Data can be depicted graphically using bar graphs, line charts, pie

charts and control charts. While the first three are commonly used, the last is a line chart with control

limits. By mathematically constructing control limits at three standard deviations above and below the

average, one can determine what variation is due to normal ongoing causes (common causes) and what

variation is produced by unique events (special causes). By eliminating the special causes first and then

reducing common causes, quality can be improved. Control chart provides control limits that are three

standard deviations above and below average, whether or not our process is in control.

Here follows a brief description of the basic set of Total Quality Management tools. They are:



 Pareto Principle

 Scatter Plots

 Control Charts

 Flow Charts

 Cause and Effect , Fishbone, Ishikawa Diagram

 Histogram or Bar Graph

 Check Lists

 Check Sheets



Pareto Principle









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The Pareto principle suggests that most effects come from relatively few causes. In quantitative

terms: 80% of the problems come from 20% of the causes (machines, raw materials, operators etc.); 80%

of the wealth is owned by 20% of the people etc. Therefore effort aimed at the right 20% can solve 80%

of the problems. Double (back to back) Pareto charts can be used to compare 'before and after'

situations. General use, to decide where to apply initial effort for maximum effect.

SCATTER PLOTS









A scatter plot is effectively a line graph with no line - i.e. the point intersections between

the two data sets are plotted but no attempt is made to physically draw a line. The Y axis is conventionally

used for the characteristic whose behavior we would like to predict. Use, to define the area of relationship

between two variables.









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CONTROL CHARTS









Control charts are a method of Statistical Process Control, SPC. (Control system for production

processes). They enable the control of distribution of variation rather than attempting to control each

individual variation. Upper and lower control and tolerance limits are calculated for a process and

sampled measures are regularly plotted about a central line between the two sets of limits. The plotted

line corresponds to the stability/trend of the process. Action can be taken based on trend rather than on

individual variation. This prevents over-correction/compensation for random variation, which would lead

to many rejects.

FLOW CHARTS









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Pictures, symbols or text coupled with lines, arrows on lines show direction of flow. Enables

modeling of processes; problems/opportunities and decision points etc. Develops a common

understanding of a process by those involved. No particular standardization of symbology, so

communication to a different audience may require considerable time and explanation.





CAUSE AND EFFECT , FISHBONE, ISHIKAWA DIAGRAM









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The cause-and-effect diagram is a method for analyzing process dispersion. The diagram's purpose

is to relate causes and effects. Three basic types: Dispersion analysis, Process classification and cause

enumeration. Effect = problem to be resolved, opportunity to be grasped, result to be achieved. Excellent

for capturing team brainstorming output and for filling in from the 'wide picture'. Helps organize and

relate factors, providing a sequential view. Deals with time direction but not quantity. Can become very

complex. Can be difficult to identify or demonstrate interrelationships.

HISTOGRAM OR BAR GRAPH









A Histogram is a graphic summary of variation in a set of data. It enables us to see patterns that

are difficult to see in a simple table of numbers. Can be analyzed to draw conclusions about the data set.

A histogram is a graph in which the continuous variable is clustered into categories and the value of each

cluster is plotted to give a series of bars as above. The above example reveals the skewed distribution of a





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set of product measurements that remain nevertheless within specified limits. Without using some form

of graphic this kind of problem can be difficult to analyze, recognize or identify.

CHECK SHEETS

A Check Sheet is a data recording form that has been designed to readily interpret results from the

form itself. It needs to be designed for the specific data it is to gather. Used for the collection of

quantitative or qualitative repetitive data. Adaptable to different data gathering situations. Minimal

interpretation of results required. Easy and quick to use. No control for various forms of bias - exclusion,

interaction, perception, operational, non-response, estimation.

CHECK LISTS

A Checklist contains items that are important or relevant to a specific issue or situation. Checklists

are used under operational conditions to ensure that all important steps or actions have been taken. Their

primary purpose is for guiding operations, not for collecting data. Generally used to check that all aspects

of a situation have been taken into account before action or decision making. Simple, effective.

DEMING WHEEL

PDCA ("Plan-Do-Check-Act") is an iterative four-step problem-solving process typically used in

quality control. It is also known as the Deming Cycle, Shewhart cycle, Deming Wheel, or Plan-Do-

Study-Act.

Meaning

PLAN

Establish the objectives and processes necessary to deliver results in accordance with the

specifications.

DO

Implement the processes.

CHECK

Monitor and evaluate the processes and results against objectives and Specifications and report the

outcome.

ACT

Apply actions to the outcome for necessary improvement. This means reviewing all steps (Plan,

Do, Check, Act) and modifying the process to improve it before its next implementation.









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PDCA was made popular by Dr. W. Edwards Deming, who is considered by many to be the

father of modern quality control; however it was always referred to by him as the "Shewhart cycle." Later

in Deming's career, he modified PDCA to "Plan, Do, Study, Act" (PDSA) so as to better describe his

recommendations. The concept of PDCA comes out of the Scientific Method, as developed from the

work of Francis Bacon (Novum Organum, 1620). The scientific method can be written as "hypothesis" -

"experiment" - "evaluation" or Plan, Do, and Check. Shewhart described manufacture under "control" -

under statistical control - as a three step process of specification, production, and inspection. He also

specifically related this to the Scientific Method of hypothesis, experiment and evaluation. Shewhart says

that the statistician "must help to change the demand [for goods] by showing...how to close up the

tolerance range and to improve the quality of goods." Clearly, Shewhart intended the analyst to take

action based on the conclusions of the evaluation. According to Deming during his lectures in Japan in

the early 1950's the Japanese participants shortened the steps to the now traditional Plan, Do, Check, Act.

Deming preferred Plan, Do, Study, Act because 'Study' has connotations in English closer to Shewhart's

intent than "Check."

A fundamental principle of the scientific method and PDSA is iteration - once a hypothesis is

confirmed (or negated), executing the cycle again will extend the knowledge further. Repeating the PDSA

cycle can bring us closer to the goal, usually a perfect operation and output. In Six Sigma programs, the

PDSA cycle is called "Define, Measure, Analyze, Improve, and Control" (DMAIC). The iterative nature

of the cycle must be explicitly added to the DMAIC procedure. PDSA should be repeatedly implemented

in spirals of increasing knowledge of the system that converge on the ultimate goal, each cycle closer than

the previous. One can envision an open coil spring, with each loop being one cycle of the Scientific

Method - PDSA, and each complete cycle indicating an increase in our knowledge of the system under

study. This approach is based on the belief that our knowledge and skills are limited, but improving.

Especially at the start of a project, key information may not be known; the PDSA - scientific method -

provides feedback to justify our guesses (hypotheses) and increase our knowledge. Rather than enter





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"analysis paralysis" to get it perfect the first time, it is better to be approximately right than exactly wrong.

With the improved knowledge, we may choose to refine or alter the goal (ideal state). Certainly, the PDSA

approach can bring us closer to whatever goal we choose.

Rate of change - rate of improvement is a key competitive factor in today's world. PDSA allows

for major 'jumps' in performance ('breakthroughs' often desired in a Western approach), as well as Kaizen

(frequent small improvements associated with an Eastern approach). In the United States a PDSA

approach is usually associated with a sizable project involving numerous people's time, and thus managers

want to see large 'breakthrough' improvements to justify the effort expended. However, the Scientific

Method and PDSA apply to all sorts of projects and improvement activities. The power of Deming's

concept lies in its apparent simplicity. The concept of feedback in the Scientific Method, in the abstract

sense, is today firmly rooted in education. While apparently easy to understand, it is often difficult to

accomplish on a on-going basis due to the intellectual difficulty of judging one's proposals (hypotheses)

on the basis of measured results. Many people have an emotional fear of being shown "wrong," even by

objective measurements. To avoid such comparisons, we may instead cite complacency, distractions, loss

of focus, lack of commitment, re-assigned priorities, lack of resources, etc.



Zero Defects



Get it right first time



How much do quality failures cost your company?



Quality defects have significant costs associated with them - some of the most obvious being

money, time, resources, and lost reputation. And programs to eliminate quality defects can be expensive

and time consuming. Do you insist on eliminating defects entirely no matter the cost? Or, do you accept

that a certain, albeit very small, percentage of defects is acceptable, and just accept the costs and learn to

live with them?



One of the most influential ideas about this was the notion of "zero defects." This phrase was

coined by Philip Crosby in his 1979 book titled, "Quality is Free."



His position was that where there are zero defects, there are no costs associated with issues of

poor quality; and hence, quality becomes free.



Explaining the Idea



Zero defects is a way of thinking and doing that reinforces the notion that defects are not

acceptable, and that everyone should "do things right the first time". The idea here is that with a

philosophy of zero defects, you can increase profits both by eliminating the cost of failure and increasing

revenues through increased customer satisfaction.



"Zero defects" is referred to as a philosophy, a mentality or a movement. It's not a program, nor

does it have distinct steps to follow or rules to abide by. This is perhaps why zero defects can be so

effective, because it means it's adaptable to any situation, business, profession or industry.



The question that often comes up when zero defects is discussed, is whether or not zero defects is

ever attainable. Essentially, does adopting a zero defect environment only set users up for failure?







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Zero defects is NOT about being perfect. Zero defects is about changing your perspective. It does

this by demanding that you:



 Recognize the high cost of quality issues;

 Continuously think of the places where flaws may be introduced; and

 Work proactively to address the flaws in your systems and processes, which allow defects to occur.



Zero defects is a standard. It is a measure against which any system, process, action, or outcome

can be analyzed. When zero defects is the goal, every aspect of the business is subject to scrutiny in terms

of whether it measures up.



When you think about it, we expect zero defects when we are talking about items or services that

we use. If you buy a fancy new plasma TV and your pixels start burning by the thousands, you demand

satisfaction. When you take the car in for brake service, you expect that the mechanic will install the parts

exactly as the manufacturer prescribes. No defect is an acceptable defect when it affects you personally.



So why then, is it so easy to accept that "defects happen" when you are the one producing the

product or providing the service? This is the interesting dichotomy that presents itself. Zero defects is one

of the best ways to resolve the discord between what we expect for ourselves and what we can accept for

others.



However, if you fanatically follow a zero defects approach in areas which don't need it, you'll most

likely be wasting resources. One of the most important of these resources is time, and this is where people

are accused of time-destroying "perfectionism."



Adopting Zero Defects



There are no step-by-step instructions for achieving zero defects, and there is no magic

combination of elements that will result in them. There are, however, some guidelines and techniques to

use when you decide you are ready to embrace the zero defects concept.



Management must commit to zero defects. Zero defects requires a top down approach: The best-

intentioned employees cannot provide zero defects if they are not given the tools to do so.



 When you decide that zero defects is the approach you want to take, recognize that it likely

represents a significant change to the way people do things. Manage the introduction using the

principles of change management.



 Understand what your customers expect in terms of quality. Design systems that support zero

defects where it matters, but don't over-design if the end-user just doesn't care.



 Zero defects require a proactive approach. If you wait for flaws to emerge you are too late.



 Create quality improvement teams. Zero defects must be integrated with the corporate culture.

Zero defects needs to be accepted as "the ways things are done around here".



 Learn poka - yoke (POH-kay YOH-kay.) Invented in the 1960s by Shigeo Shingo of Japan, it

translates to "prevent inadvertent mistakes". It's an approach that emphasizes designing systems





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that make defects almost impossible or, if they can't be avoided, easy to detect and address. To

implement zero defects, you have to have strong systems in place.



 Monitor your progress. Build mechanisms into your systems and methods of operating that

provide continuous feedback. This allows you act quickly when flaws do occur.



 Measure your quality efforts. It is important to express your progress in terms of the bottom line.

Take baseline measurements so you understand the cost of defects in your organization, and can

measure the benefits your achieving in eliminating them.



 Build quality into your performance expectations. Encourage members of your team to think

about how they can achieve zero defects, and reward them when they're successful.



 Recognize that although zero defects is a destination, circumstances keep changing. Monitor,

evaluate, and adapt in a continuous, never-ending cycle.



Zero defects: What does it achieve? What does it mean?

The definition for Six Sigma was clear from the beginning – 3.4 defects per million opportunities

(DPMO), allowing for a 1.5-sigma process shift. But the definition for zero defects is not so clear.

Perhaps zero defects refers to the domain beyond 3.4 DPMO. Or perhaps it refers to designing defects

out of the process or product, so that – theoretically at least – a company can consistently manufacture a

defect-free product.

There is value in trying to understand the meaning and purpose of this oft-used term, and whether

its use is the best approach in a Six Sigma environment of continuous improvement.



POSSIBLE PITFALLS OF PUSHING ZERO DEFECTS

Quality guru W. Edwards Deming believed that slogans and programs such as "zero defects" are

usually counterproductive. D.C. Montgomery, author of the book Introduction to Statistical Quality

Control, agrees, commenting that these programs typically do not drive the "use of proper statistical and

engineering tools into the right places of the organization," and they "devote far too little attention to

variability reduction." In other words, the use of slogans such as zero defects to spur quality may lead to a

de-emphasis of the tried-and-true tools and culture associated with successful continuous improvement.

But can a mere slogan actually discourage the successful implementation of proven Six Sigma

continuous improvement methodologies? This can best be answered by considering the expectations, the

conflicts and the different levels of understanding surrounding the term zero defects.

Literally zero defects corresponds to a defect level of infinity sigma, which most practitioners will admit is

not possible. And yet an enthusiastically institutionalized zero defects program may unfortunately

promote the belief and expectation that true zero can and should be achieved. This is evidenced by

several phrases that quality professionals may have heard spoken – or at least heavily implied – by

business strategists:



 "All defects are the same, since all defects are bad"

 "There is no such thing as a benign defect."

 "If we can get rid of the defects, then we can get rid of the testing."



These expectations are worth examination.









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STATEMENTS THAT DO NOT ALIGN WITH FACTS

In fact, all defects are not equal. Defects, depending on their size and type, have different

probabilities of impacting the finished product. And these probabilities depend on the technology. In fact,

the impact probability of a particular defect may vary within the technology – that is, at the stage or layer

in which it occurs. When it comes to the practical definition of a defect, "bad" is a relative term. Many

defects are simply neutral. They are never good, but – again, depending on the technology – they may

cause no harm either. If all defects are considered bad, then prioritization is difficult.

It is the role of statistically minded scientists and engineers to classify defects and their potential

impact, based on data and engineering judgment. This allows them to systematically reduce defect levels

in a prioritized fashion, starting with the worst and progressing toward the more benign. Without this

kind of problem-solving prioritization, progress may be slow and confused – perhaps even at a standstill.

The ability to prioritize is absolutely necessary in the continuous improvement process.

The statement that if fewer defects are produced, then less inspection will be required is incorrect.

Actually, the opposite is true. A higher level and sophistication of testing is required to detect a smaller

level of defects. The plot in Figure 1, derived from a cumulative binomial distribution (pass/fail

inspection) shows how the sample size increases exponentially as the prevalence of a defective unit

decreases. The particular curve in Figure 1 corresponds to a probability of detection of 95 percent. In

other words, if a defect is present at the indicated level (x-axis), there is a 95 percent probability that at

least one failed unit will be detected using the sample size indicated on the y-axis.

Figure 1: Sample Size Versus Probability of Failure









A more intuitive example is: If a shoebox full of needles is mixed into a haystack, only a portion

of the haystack will have to be moved before the presence of needles is detected. If there is only one

needle in the haystack, every straw may have to be moved before it is found, assuming it is not missed

entirely.

This is really the misunderstanding that drives the inappropriate application of a zero defects

policy to multiple points along the supply chain (Figure 2). It may be thought that producing zero or

near-zero defects at each point will lead to reduced or eliminated inspection/testing prior to shipment to

the end-customer. But for zero defects to approach reality, the inspection/testing must remain the same

or increase at the final inspection point. If zero is truly the goal, then 100 percent sampling at the

"escape" point is required, regardless of defect levels. This implies, then, that any zero defect inspections

prior to the escape point may be non-value-added.







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Figure 2: A High-Level Flow of Serial Product

Manufacture,

Across Supplier and Customer Boundaries









Ideally suppliers need to produce the highest quality output possible, in order to maximize yield

and minimize costs which ultimately benefits both the supplier and the customer. But a zero defects

policy does not provide this motivation to suppliers. When the goal of zero defects is applied to multiple

interim points along the supply chain, the undesired effects of increased costs and lower yields are

encouraged. The increased costs come from increased tests, inspections and cycle time. The lower yields

are likely because of a higher rate of "false fails" (type 1 errors) as the suppliers apply increasingly

stringent criteria in an attempt to eliminate potential failures at the customer's incoming test/inspection.

In other words, in an effort to eliminate even the smallest possibility of customer incoming test failures,

good product may be scrapped to overly stringent criteria.



NEGATIVE IMPACT ON WORKFORCE AND SUPPLY CHAIN

A focus on zero defects may be stifling to a discussion of continuous improvement, and may lead

to frustration and non-productivity. To the general workforce, it may be a demoralizing concept. While





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everyone understands that continuous defect reduction is critical and necessary, most people understand,

intuitively at least, that true zero is unachievable. Always striving for an unachievable goal may eventually

de-motivate even the most optimistic of employees, particularly if they are frequently told that their

defect level is unacceptable – because it is not zero.

For a company's suppliers, continuing to add tests and inspections in an effort to comply with

zero defects (perhaps at their customer's demand) may eventually drive them out of business. Thus, while

continuous improvement is applicable to everyone, zero defects can or should only be applied to the final

supplier, rather than at interim points along the supply chain. Attempting to do the latter may eventually

put one or more of the suppliers in jeopardy. If a supplier critical to the company were to fail, the

company's supply chain might collapse, which might eventually put the company out of business too

Finally, it should be realized that the inspections and tests themselves (however careful and precise

they are) have a finite probability of actually causing a defect. This concept is somewhat akin to the

Uncertainty Principle: "We may significantly modify what we are trying to measure simply by making the

measurement."



CONCLUSION: STRIVE TO BE BETTER AND BETTER, NOT PERFECT

Since the slogan zero defects implies immediate compliance to a defect-free standard, it may not

leave time for the continuous improvement process to occur. In fact, it may even slow down the

continuous improvement process because of the massive resources that inspected-in quality entails.

Zero defects is a message that can carry with it confusion and misinterpretation, mixed with

technical impracticality. It may be appropriate that the idea of zero defects be replaced with a policy of

"zero escapes," since the latter has limited interpretation. As a company is doing all it can to improve the

product and business using continuous improvement techniques, it also needs to consider what it can do

to prevent a random, low-level defect from reaching the final customer. In this regard, zero escapes of

defects may be a complimentary activity to continuous improvement.

A logical strategy is to employ continuous improvement methodologies everywhere in the

business and manufacturing process to improve quality and yield, and reduce cycle time and costs. Then,

at the point of shipping the final product to the final customer, employ a zero escapes methodology to

help ensure that a randomly defective unit does not reach its final application. The tools and techniques

developed and employed at this final gate should be arrived at through a team effort of the various

suppliers and interim customers. Expecting individual suppliers in the supply chain to produce zero

defects, in an effort to eliminate or minimize the final gate, is likely to be an impractical strategy.

Quality professionals already have specific, descriptive methodologies that are aimed at achieving the

same goals as zero defects. Here are but some of the methodologies already in use and being developed

to minimize the defects in the end product:



 Design for manufacturability (DFM)

 Design for yield (DFY)

 Design for test (DFT) – "DFM: Worlds Collide, Then Cooperate" by L. Peters in Semiconductor

International, June 1, 2005.

 Robust design



It is probably best to not encourage the use of somewhat ambiguous terminology in the place of

well-defined and meaningful methodologies such as these.

The concept of continuous improvement is intuitive. It makes sense to always strive for a better

process or product, to reduce costs, satisfy customers and gain market share. Absolute perfection can







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never be achieved, but an organization can move closer and closer with good statistical and engineering

practices.









BENCHMARKING

Benchmarking is the process of comparing the cost, time or quality of what one organization

does against what another organization does. The result is often a business case for making changes in

order to make improvements.

"Benchmarking: A continuous, systematic process of evaluating and comparing the capability of

one organization with others normally recognized as industry leaders, for insights for optimizing the

organizations processes."









Performance analysis forms the basis for your current process improvement which enables you to

make better software tomorrow. Performance benchmarking removes misconceptions, and lets us see the

actual need for improvement. Also referred to as "best practice benchmarking" or "process

benchmarking", it is a process used in management and particularly strategic management, in which

organizations evaluate various aspects of their processes in relation to best practice, usually within their

own sector. This then allows organizations to develop plans on how to make improvements or adopt best

practice, usually with the aim of increasing some aspect of performance. Benchmarking may be a one-off

event, but is often treated as a continuous process in which organizations continually seek to challenge

their practices.







POPULARITY AND BENEFITS FROM BENCHMARKING





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In 2008,a comprehensive survey on benchmarking was commissioned by the Global Benchmarking

Network(a network of benchmarking centres representing 22 countries - and for which the founder of

benchmarking, Dr Robert Camp, is the honorary president). Over 450 organisations responded from

over 40 countries. The results showed that:



1. Mission and Vision Statements and Customer (Client) Surveys are the most used (by 77% of

organizations) of 20 improvement tools, followed by Strengths, Weaknesses, Opportunities, and

Threats SWOT (72%), and Informal Benchmarking (68%). Performance Benchmarking was used

by (49%) and Best Practice Benchmarking by (39%).

2. The tools that are likely to increase in popularity the most over the next three years are

Performance Benchmarking, Informal Benchmarking, SWOT, and Best Practice Benchmarking.

Over 60% of organizations that are not currently using these tools indicated they are likely to use

them in the next three years.

3. When Best Practice Benchmarking is done well significant benefits are obtained with 20% of

projects resulting in benefits worth US$250,000.



COLLABORATIVE BENCHMARKING

Benchmarking, originally invented as a formal process by Rank Xerox, is usually carried out by

individual companies. Sometimes it may be carried out collaboratively by groups of companies (eg

subsidiaries of a multinational in different countries). One example is that of the Dutch municipally-

owned water supply companies, which have carried out a voluntary collaborative benchmarking process

since 1997 through their industry association. Another example is the UK construction industry which

has carried out benchmarking since the late 1990's again through its industry association and with

financial support from the UK Government.









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PROCEDURE

There is no single benchmarking process that has been universally adopted. The wide appeal and

acceptance of benchmarking has led to various benchmarking methodologies emerging. The most

prominent methodology is the 12 stage methodology by Robert Camp (who wrote the first book on

benchmarking in 1989). The 12 stage methodology consisted of-



1. Select subject ahead

2. Define the process

3. Identify potential partners

4. Identify data sources

5. Collect data and select partners

6. Determine the gap

7. Establish process differences

8. Target future performance

9. Communicate

10. Adjust goal

11. Implement

12. Review/recalibrate.



The following is an example of a typical shorter version of the methodology:







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1. Identify your problem areas - Because benchmarking can be applied to any business process or

function, a range of research techniques may be required. They include: informal conversations

with customers, employees, or suppliers; exploratory research techniques such as focus groups; or

in-depth marketing research, quantitative research, surveys, questionnaires, re-engineering analysis,

process mapping, quality control variance reports, or financial ratio analysis. Before embarking on

comparison with other organizations it is essential that you know your own organization's

function, processes; base lining performance provides a point against which improvement effort

can be measured.

2. Identify other industries that have similar processes - For instance if one were interested in

improving hand offs in addiction treatment he/she would try to identify other fields that also have

hand off challenges. These could include air traffic control, cell phone switching between towers,

transfer of patients from surgery to recovery rooms.

3. Identify organizations that are leaders in these areas - Look for the very best in any industry

and in any country. Consult customers, suppliers, financial analysts, trade associations, and

magazines to determine which companies are worthy of study.

4. Survey companies for measures and practices - Companies target specific business processes

using detailed surveys of measures and practices used to identify business process alternatives and

leading companies. Surveys are typically masked to protect confidential data by neutral associations

and consultants.

5. Visit the "best practice" companies to identify leading edge practices - Companies typically

agree to mutually exchange information beneficial to all parties in a benchmarking group and share

the results within the group.

6. Implement new and improved business practices - Take the leading edge practices and

develop implementation plans which include identification of specific opportunities, funding the

project and selling the ideas to the organization for the purpose of gaining demonstrated value

from the process.



COST OF BENCHMARKING

Benchmarking is a moderately expensive process, but most organizations find that it more than

pays for itself. The three main types of costs are:



 Visit Costs - This includes hotel rooms, travel costs, meals, a token gift, and lost labor time.

 Time Costs - Members of the benchmarking team will be investing time in researching problems,

finding exceptional companies to study, visits, and implementation. This will take them away from

their regular tasks for part of each day so additional staff might be required.

 Benchmarking Database Costs - Organizations that institutionalize benchmarking into their daily

procedures find it is useful to create and maintain a database of best practices and the companies

associated with each best practice now.



The cost of benchmarking can substantially be reduced through utilizing the many internet

resources that have sprung up over the last few years. These aim to capture benchmarks and best

practices from organizations, business sectors and countries to make the benchmarking process much

quicker and cheaper.

TECHNICAL BENCHMARKING OR PRODUCT BENCHMARKING

The technique initially used to compare existing corporate strategies with a view to achieving the

best possible performance in new situations (see above), has recently been extended to the comparison of

technical products. This process is usually referred to as "Technical Benchmarking" or "Product

Benchmarking". Its use is particularly well developed within the automotive industry ("Automotive



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Benchmarking"), where it is vital to design products that match precise user expectations, at minimum

possible cost, by applying the best technologies available worldwide. Many data are obtained by fully

disassembling existing cars and their systems. Such analyses were initially carried out in-house by car

makers and their suppliers. However, as they are expensive, they are increasingly outsourced to companies

specialized in this area. Indeed, outsourcing has enabled a drastic decrease in costs for each company (by

cost sharing) and the development of very efficient tools (standards, software).



TYPES OF BENCHMARKING



 Process benchmarking - the initiating firm focuses its observation and investigation of business

processes with a goal of identifying and observing the best practices from one or more benchmark

firms. Activity analysis will be required where the objective is to benchmark cost and efficiency;

increasingly applied to back-office processes where outsourcing may be a consideration.

 Financial benchmarking - performing a financial analysis and comparing the results in an effort

to assess your overall competitiveness.

 Performance benchmarking - allows the initiator firm to assess their competitive position by

comparing products and services with those of target firms.

 Product benchmarking - the process of designing new products or upgrades to current ones.

This process can sometimes involve reverse engineering which is taking apart competitors

products to find strengths and weaknesses.

 Strategic benchmarking - involves observing how others compete. This type is usually not

industry specific meaning it is best to look at other industries.

 Functional benchmarking - a company will focus its benchmarking on a single function in order

to improve the operation of that particular function. Complex functions such as Human

Resources, Finance and Accounting and Information and Communication Technology are unlikely

to be directly comparable in cost and efficiency terms and may need to be disaggregated into

processes to make valid comparison.



SALES BENCHMARKING



Sales benchmarking is a sales management process used to compare a company‘s sales

force against other companies or against industry performance. The purpose is to identify

opportunities to improve performance and to focus the efforts of a sales organization.

Similarities to process benchmarking

Like process benchmarking, a company will compare itself against other companies that are in

similar industries or circumstances. The benchmarking process is also similar. Companies identify metrics,

collect internal data, find external data sources, and compare their performance.

Differences from process benchmarking

Process benchmarking has most frequently been used to compare financial, manufacturing, or

other operating metrics and processes. But even though customer relationship management (CRM)

systems have expanded the raw data available to sales organizations, many sales organizations have not

created consistent metrics or a way of organizing their performance data. Sales benchmarking has several

key challenges:



1. Choosing the right metrics is essential to identifying real problems, and focusing efforts to create

improvement.





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2. Data in many CRM implementations is not accurate, so it may be necessary to scrub the internal

data to get valid results.

3. Many companies are sensitive to sharing their sales data, so finding external data can be

challenging.



Sales Benchmarking Programs

There are a small number of commercial sales benchmarking programs available for executives to

benchmark their sales investments and/or strategies. Of these, only a fraction has a track record of

successfully supporting Global 2000 companies. Sales benchmarking programs are designed to deliver

data and insights into the processes and practices that drive improvements within the sales organization.

Firms who provide such programs may have the qualifications to identify and scale areas of strength as

well as diagnose and correct areas of under-performance. Appropriate comparison sets and a proven

methodology are all critical factors to consider when selecting a third party partner for any sales

benchmarking initiative. All reputable programs will keep clients' data confidential and the benchmarks

will be based on aggregate findings.

JUST-IN-TIME

Just-in-time (JIT) is an inventory strategy implemented to improve the return on investment of

a business by reducing in-process inventory and its associated carrying costs. In order to achieve JIT the

process must have signals of what is going on elsewhere within the process. This means that the process

is often driven by a series of signals, which can be Kanban (Kanban?) that tell production processes when

to make the next part. Kanban are usually 'tickets' but can be simple visual signals, such as the presence or

absence of a part on a shelf. When implemented correctly, JIT can lead to dramatic improvements in a

manufacturing organization's return on investment, quality, and efficiency. Some have suggested that "Just

on Time" would be a more appropriate name since it emphasizes that production should create items that

arrive when needed and neither earlier nor later.

Quick communication of the consumption of old stock which triggers new stock to be ordered is

key to JIT and inventory reduction. This saves warehouse space and costs. However since stock levels are

determined by historical demand, any sudden demand rises above the historical average demand, the firm

will deplete inventory faster than usual and cause customer service issues. Some[1] have suggested that

recycling Kanban faster can also help flex the system by as much as 10-30%. In recent years

manufacturers have touted a trailing 13 week average as a better predictor for JIT planning than most

forecasters could provide.

HISTORY

The technique was first used by the Ford Motor Company as described explicitly by Henry Ford's

My Life and Work (1923): "We have found in buying materials that it is not worthwhile to buy for other

than immediate needs. We buy only enough to fit into the plan of production, taking into consideration

the state of transportation at the time. If transportation were perfect and an even flow of materials could

be assured, it would not be necessary to carry any stock whatsoever. The carloads of raw materials would

arrive on schedule and in the planned order and amounts, and go from the railway cars into production.

That would save a great deal of money, for it would give a very rapid turnover and thus decrease the

amount of money tied up in materials. With bad transportation one has to carry larger stocks." This

statement also describes the concept of "dock to factory floor" in which incoming materials are not even

stored or warehoused before going into production. The concept needed an effective freight management

system (FMS); Ford's Today and Tomorrow (1926) describes one.

The technique was subsequently adopted and publicized by Toyota Motor Corporation of Japan

as part of its Toyota Production System (TPS). However, Toyota famously did not adopt the procedure

from Ford, but from Piggly Wiggly. Although Toyota visited Ford as part of its tour of American



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businesses, Ford had not fully adopted the Just-In-Time system, and Toyota executives were appalled at

the piles of inventory lying around and the uneven work schedule of the employees of Ford. Toyota also

visited Piggly Wiggly, and it was there that Toyota executives first observed a fully functioning and

successful Just-In-Time system, and modeled TPS after it. It is hard for Japanese corporations to

warehouse finished products and parts, due to the limited amount of land available for them. Before the

1950s, this was thought to be a disadvantage because it forced the production lot size below the

economic lot size. (An economic lot size is the number of identical products that should be produced,

given the cost of changing the production process over to another product.) The undesirable result was

poor return on investment for a factory.

The chief engineer at Toyota in the 1950s, Taiichi Ohno (Ohno Taiichi?), examined accounting

assumptions and realized that another method was possible. The factory could implement JIT which

would require it to be made more flexible and reduce the overhead costs of retooling and thereby reduce

the economic lot size to fit the available warehouse space. JIT is now regarded by Ohno as one of the two

'pillars' of the Toyota Production System. Therefore over a period of several years, Toyota engineers

redesigned car models for commonality of tooling for such production processes as paint-spraying and

welding. Toyota was one of the first to apply flexible robotic systems for these tasks. Some of the changes

were as simple as standardizing the whole sizes used to hang parts on hooks. The number and types of

fasteners were reduced in order to standardize assembly steps and tools. In some cases, identical sub-

assemblies could be used in several models. Toyota engineers then determined that the remaining critical

bottleneck in the retooling process was the time required to change the stamping dies used for body

parts. These were adjusted by hand, using crowbars and wrenches. It sometimes took as long as several

days to install a large, multi-ton die set and adjust it for acceptable quality. Further, these were usually

installed one at a time by a team of experts, so that the line was down for several weeks.

So Toyota implemented a strategy now called Single Minute Exchange of Die (SMED), developed

with Shigeo Shingo (Shingō Shigeo). With very simple fixtures, measurements were substituted for

adjustments. Almost immediately, die change times fell to hours instead of days. At the same time, quality

of the stampings became controlled by a written recipe, reducing the skill level required for the change.

Further analysis showed that a lot of the remaining time was used to search for hand tools and move dies.

Procedural changes (such as moving the new die in place with the line in operation) and dedicated tool-

racks reduced the die-change times to as little as 40 seconds. Today dies are changed in a ripple through

the factory as a new product begins flowing. After SMED, economic lot sizes fell to as little as one

vehicle in some Toyota plants. Carrying the process into parts-storage made it possible to store as little as

one part in each assembly station. When a part disappeared, that was used as a signal (Kanban) to

produce or order a replacement.

PHILOSOPHY

The philosophy of JIT is simple - inventory is defined to be waste. JIT inventory systems expose

the hidden causes of inventory keeping and are therefore not a simple solution a company can adopt;

there is a whole new way of working the company must follow in order to manage its consequences. The

ideas in this way of working come from many different disciplines including statistics, industrial

engineering, production management and behavioral science. In the JIT inventory philosophy there are

views with respect to how inventory is looked upon, what it says about the management within the

company, and the main principle behind JIT.

Inventory is seen as incurring costs, or waste, instead of adding and storing value, contrary to

traditional accounting. This does not mean to say JIT is implemented without awareness that removing

inventory exposes pre-existing manufacturing issues. With this way of working, businesses are encouraged

to eliminate inventory that does not compensate for manufacturing process issues, and then to constantly

improve those processes so that less inventory can be kept. Secondly, allowing any stock habituates the

management to stock keeping and it can then be a bit like a narcotic. Management are then tempted to



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keep stock there to hide problems within the production system. These problems include backups at

work centers, machine reliability, and process variability, lack of flexibility of employees and equipment,

and inadequate capacity among other things.

In short, the just-in-time inventory system is all about having ―the right material, at the right time,

at the right place, and in the exact amount‖, without the safety net of inventory. The JIT system has

implications of which are broad for the implementers.



STOCKS

JIT emphasizes inventory as one of the seven wastes (overproduction, waiting time,

transportation, inventory, processing, motion and product defect), and as such its practice involves the

philosophical aim of reducing input buffer inventory to zero. Zero buffer inventory means that

production is not protected from exogenous (external) shocks. As a result, exogenous shocks reducing

the supply of input can easily slow or stop production with significant negative consequences. For

example,[3] Toyota suffered a major supplier failure as a result of the 1997 Aisin fire which rendered one

of its suppliers incapable of fulfilling Toyota's orders. In the U.S., the 1992 railway strikes resulted in

General Motors having to idle a 75,000-worker plant because they had no supplies coming in.



TRANSACTION COST APPROACH

JIT reduces inventory in a firm. However, unless it is used throughout the supply chain, it can be

hypothesized that firms are simply outsourcing their input inventory to suppliers (Naj 1993). This effect

was investigated by Newman (1993), who found, on average, suppliers in Japan charged JIT customers a

5% price premium.



ENVIRONMENTAL CONCERNS

During the birth of JIT, multiple daily deliveries were often made by bicycle; with increases in

scale has come the adoption of vans and Lorries (trucks) for these deliveries. Cusumano (1994) has

highlighted the potential and actual problems this causes with regard to gridlock and the burning of fossil

fuels. This violates three JIT wastes:



1. Time; wasted in traffic jams

2. Inventory; specifically pipeline (in transport) inventory and

3. Scrap; with respect to petrol or diesel burned while not physically moving.



Price volatility

JIT implicitly assumes a level of input price stability such that it is desirable to inventory inputs at

today's prices. Where input prices are expected to rise storing inputs may be desirable.



Quality volatility

JIT implicitly assumes the quality of available inputs remains constant over time. If not, firms may

benefit from hoarding high quality inputs.



Demand stability

Karmarker (1989) highlights the importance of relatively stable demand which can help ensure

efficient capital utilisation rates. Karmarker argues without a significant stable component of demand, JIT

becomes untenable in high capital cost production. In the U.S., the 1992 railway strikes resulted in

General Motors having to idle a 75,000-worker plant because they had no supplies coming in.

JIT IMPLEMENTATION DESIGN



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Based on a diagram modeled after the one used by Hewlett-Packard‘s Boise plant to accomplish

its JIT program.

1) F Design Flow Process



- F Redesign/relay out for flow

- L Reduce lot sizes

- O Link operations

- W Balance workstation capacity

- M Preventative maintenance

- S Reduce Setup Times



2) Q Total quality control

- C worker compliance

- I Automatic inspection

- M quality measures

- M fail-safe methods

- W Worker participation



3) S Stabilize Schedule

- S Level Schedule

- W establish freeze windows

- UC Underutilize Capacity

4) K Kanban Pull System

- D Demand pull

- B Backflush

- L Reduce lot sizes

5) V Work with vendors

- L Reduce lead time

- D Frequent deliveries

- U Project usage requirements

- Q Quality Expectations

6) I Further reduce inventory in other areas

- S Stores

- T Transit

- C Implement Carroussel to reduce motion waste

- C Implement Conveyor belts to reduce motion waste

7) P Improve Product Design

- P Standard Production Configuration

- P Standardize and reduce the number of parts

- P Process design with product design

- Q Quality Expectations



EFFECTS

Some of the initial results at Toyota were horrible, but in contrast to that a huge amount of cash

appeared, apparently from nowhere, as in-process inventory was built out and sold. This by itself

generated tremendous enthusiasm in upper management.









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Another surprising effect was that the response time of the factory fell to about a day. This

improved customer satisfaction by providing vehicles usually within a day or two of the minimum

economic shipping delay.

Also, many vehicles began to be built to order, completely eliminating the risk they would not be

sold. This dramatically improved the company's return on equity by eliminating a major source of risk.

Since assemblers no longer had a choice of which part to use, every part had to fit perfectly. The

result was a severe quality assurance crisis, and a dramatic improvement in product quality. Eventually,

Toyota redesigned every part of its vehicles to eliminate or widen tolerances, while simultaneously

implementing careful statistical controls for quality control. Toyota had to test and train suppliers of parts

in order to assure quality and delivery. In some cases, the company eliminated multiple suppliers.

When a process problem or bad parts surfaced on the production line, the entire production line

had to be slowed or even stopped. No inventory meant that a line could not operate from in-process

inventory while a production problem was fixed. Many people in Toyota confidently predicted that the

initiative would be abandoned for this reason. In the first week, line stops occurred almost hourly. But by

the end of the first month, the rate had fallen to a few line stops per day. After six months, line stops had

so little economic effect that Toyota installed an overhead pull-line, similar to a bus bell-pull, that

permitted any worker on the production line to order a line stop for a process or quality problem. Even

with this, line stops fell to a few per week.

The result was a factory that eventually became the envy of the industrialized world, and has since

been widely emulated.

The just-in-time philosophy was also applied to other segments of the supply chain in several

types of industries. In the commercial sector, it meant eliminating one or all of the warehouses in the link

between a factory and a retail establishment.



Benefits

As most companies use an inventory system best suited for their company, the Just-In-Time

Inventory System (JIT) can have many benefits resulting from it. The main benefits of JIT are listed

below.



1. Set up times are significantly reduced in the factory. Cutting down the set up time to be more

productive will allow the company to improve their bottom line to look more efficient and focus

time spent on other areas that may need improvement. This allows the reduction or elimination of

the inventory held to cover the "changeover" time, the tool used here is SMED.

2. The flows of goods from warehouse to shelves are improved. Having employees focused on

specific areas of the system will allow them to process goods faster instead of having them

vulnerable to fatigue from doing too many jobs at once and simplifies the tasks at hand. Small or

individual piece lot sizes reduce lot delay inventories which simplifies inventory flow and its

management.

3. Employees who possess multiple skills are utilized more efficiently. Having employees trained to

work on different parts of the inventory cycle system will allow companies to use workers in

situations where they are needed when there is a shortage of workers and a high demand for a

particular product.

4. Better consistency of scheduling and consistency of employee work hours. If there is no demand

for a product at the time, workers don‘t have to be working. This can save the company money by

not having to pay workers for a job not completed or could have them focus on other jobs around

the warehouse that would not necessarily be done on a normal day.







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5. Increased emphasis on supplier relationships. No company wants a break in their inventory system

that would create a shortage of supplies while not having inventory sit on shelves. Having a

trusting supplier relationship means that you can rely on goods being there when you need them in

order to satisfy the company and keep the company name in good standing with the public.

6. Supplies continue around the clock keeping workers productive and businesses focused on

turnover. Having management focused on meeting deadlines will make employees work hard to

meet the company goals to see benefits in terms of job satisfaction, promotion or even higher pay.



PROBLEMS WITH IN JIT SYSTEM

The major problem with just-in-time operation is that it leaves the supplier and downstream

consumers open to supply shocks and large supply or demand changes. For internal reasons, this was

seen as a feature rather than a bug by Ohno, who used the analogy of lowering the level of water in a

river in order to expose the rocks to explain how removing inventory showed where flow of production

was interrupted. Once the barriers were exposed, they could be removed; since one of the main barriers

was rework, lowering inventory forced each shop to improve its own quality or cause a holdup in the next

downstream area. One of the other key tools to manage this weakness is production leveling to remove

these variations. Just-in-time is a means to improving performance of the system, not an end.

With very low stock levels meaning that there are shipments of the same part coming in

sometimes several times per day, Toyota is especially susceptible to an interruption in the flow. For that

reason, Toyota is careful to use two suppliers for most assemblies. As noted in Liker (2003), there was an

exception to this rule that put the entire company at risk by the 1997 Aisin fire. However, since Toyota

also makes a point of maintaining high quality relations with its entire supplier network, several other

suppliers immediately took up production of the Aisin-built parts by using existing capability and

documentation. Thus, a strong, long-term relationship with a few suppliers is preferred to short-term,

price-based relationships with competing suppliers. This long-term relationship has also been used by

Toyota to send Toyota staff into their suppliers to improve their suppliers' processes. These interventions

have now been going on for twenty years and result in improved margins for Toyota and the supplier as

well as lower final customer costs and a more reliable supply chain. Toyota encourages their suppliers to

duplicate this work with their own suppliers.



Within a raw material stream

As noted by Liker (2003) and Womack and Jones (2003), it would ultimately be desirable to

introduce synchronized flow and linked JIT all the way back through the supply stream. However, none

followed this in detail all the way back through the processes to the raw materials. With present

technology, for example, an ear of corn cannot be grown and delivered to order. The same is true of most

raw materials, which must be discovered and/or grown through natural processes that require time and

must account for natural variability in weather and discovery. The part of this currently viewed as

impossible is the synchronized part of flow and the linked part of JIT. It is for the reasons stated raw

materials companies decouple their supply chain from their clients' demand by carrying large 'finished

goods' stocks. Both flow and JIT can be implemented in isolated process islands within the raw materials

stream. The challenge then becomes to achieve that isolation by some means other than the huge stocks

they carry to achieve it today.

It is because of this almost all value chains are split into a part which makes-to-forecast and a part which

could, by using JIT, become make-to-order. Often, historically, the make-to-order part has been within

the retailer portion of the value chain. Toyota's revolutionary step has been to take Piggly Wiggly's

supermarket replenishment system and drive it back to at least half way through their automobile

factories. Their challenge today is to drive it all the way back to their goods-inwards dock. Of course, the

mining of iron and making of steel is still not done specifically because somebody orders a particular car.



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Recognizing JIT could be driven back up the supply chain has reaped Toyota huge benefits and a world

dominating position in the auto industry.

It should be noted that the advent of the mini mill steelmaking facility is starting to challenge how

far back JIT can be implemented, as the electric arc furnaces at the heart of many mini-mills can be

started and stopped quickly, and steel grades changed rapidly.



Oil

It has been frequently charged that the oil industry has been influenced by JIT.

The argument is presented as follows:

The number of refineries in the United States has fallen from 279 in 1975 to 205 in 1990 and

further to 149 in 2004. As a result, the industry is susceptible to supply shocks, which cause spikes in

prices and subsequently reduction in domestic manufacturing output. The effects of hurricanes Katrina

and Rita are given as an example: in 2005, Katrina caused the shutdown of 9 refineries in Louisiana and 6

more in Mississippi, and a large number of oil production and transfer facilities, resulting in the loss of

20% of the US domestic refinery output. Rita subsequently shut down refineries in Texas, further reducing

output. The GDP figures for the third and fourth quarters showed a slowdown from 3.5% to 1.2%

growth. Similar arguments were made in earlier crises.

Beside the obvious point that prices went up because of the reduction in supply and not for

anything to do with the practice of JIT, JIT students and even oil & gas industry analysts question

whether JIT as it has been developed by Ohno, Goldratt, and others is used by the petroleum industry.

Companies routinely shut down facilities for reasons other than the application of JIT. One of those

reasons may be economic rationalization: when the benefits of operating no longer outweigh the costs,

including opportunity costs, the plant may be economically inefficient. JIT has never subscribed to such

considerations directly; following Waddel and Bodek (2005), this ROI-based thinking conforms more to

Brown-style accounting and Sloan management. Further, and more significantly, JIT calls for a reduction

in inventory capacity, not production capacity. From 1975 to 1990 to 2005, the annual average stocks of

gasoline have fallen by only 8.5% from 228,331 to 222,903 bbls to 208,986 (Energy Information

Administration data). Stocks fluctuate seasonally by as much as 20,000 bbls. During the 2005 hurricane

season, stocks never fell below 194,000 thousand bbls, while the low for the period 1990 to 2006 was

187,017 thousand bbls in 1997. This shows that while industry storage capacity has decreased in the last

30 years, it hasn't been drastically reduced as JIT practitioners would prefer.

Finally, as shown in a pair of articles in the Oil & Gas Journal, JIT does not seem to have been a

goal of the industry. In Waguespack and Cantor (1996), the authors point out that JIT would require a

significant change in the supplier/refiner relationship, but the changes in inventories in the oil industry

exhibit none of those tendencies. Specifically, the relationships remain cost-driven among many

competing suppliers rather than quality-based among a select few long-term relationships. They find that

a large part of the shift came about because of the availability of short-haul crudes from Latin America. In

the follow-up editorial, the Oil & Gas Journal claimed that "casually adopting popular business

terminology that doesn't apply" had provided a "rhetorical bogey" to industry critics. Confessing that they

had been as guilty as other media sources, they confirmed that "It also happens not to be accurate."





BUSINESS MODELS FOLLOWING SIMILAR APPROACH

Vendor Managed Inventory

Vendor Managed Inventory (VMI) employs the same principles as those of JIT inventory

however the responsibilities of managing inventory is placed with the vendor in a vendor/customer





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relationship. Whether it‘s a manufacturer who is managing inventory for a distributor, or a distributor

managing inventory for their customers; the role of managing inventory is given to the vendor.

The primary advantage of this business model is that the vendor has industry experience and

expertise which enables them to better anticipate demand and inventory needs. The inventory planning

and controlling is facilitated by the use of applications that allow vendors to have access to the inventory

picture of its customer.

Third party applications offer vendors the benefit afforded by a quick implementation time.

Further, such companies hold valuable inventory management knowledge and expertise that helps

organizations immensely.



Customer Managed Inventory

With Customer Managed Inventory (CMI), the customer as opposed to the vendor in a VMI

model is given the responsibility of making all inventory decisions. This is similar to the concepts

employed by JIT inventory. With a clear picture of their inventory and that of their supplier‘s, the

customer is able to anticipate fluctuations in demand and make inventory replenishment decisions

accordingly.



Force field analysis

Force field analysis is an influential development in the field of social science. It provides a

framework for looking at the factors (forces) that influence a situation, originally social situations. It looks

at forces that are either driving movement toward a goal (helping forces) or blocking movement toward a

goal (hindering forces). The principle, developed by Kurt Lewin, is a significant contribution to the fields

of social science, psychology, social psychology, organizational development, process management, and

change management.

Bobby Golden, a social psychologist, believed the "field" to be a Gestalt psychological

environment existing in an individual's (or in the collective group) mind at a certain point in time that can

be mathematically described in a topological constellation of constructs. The "field" is very dynamic,

changing with time and experience. When fully constructed, an individual's "field" (Lewin used the term

"life space") describes that person's motives, values, needs, moods, goals, anxieties, and ideals.

Golden believed that changes of an individual's "life space" depend upon that individual's

internalization of external stimuli (from the physical and social world) into the "life space." Although

Golden did not use the word "experiential," (see experiential learning) he nonetheless believed that

interaction (experience) of the "life space" with "external stimuli" (at what he calls the "boundary zone")

were important for development (or regression). For Lewin, development (or regression) of an individual

occurs when their "life space" has a "boundary zone" experience with external stimuli. Note, it is not

merely the experience that causes change in the "life space," but the acceptance (internalization) of

external stimuli.

Lewin took these same principles and applied them to the analysis of group conflict, learning,

adolescence, hatred, morale, German society, etc. This approach allowed him to break down common

misconceptions of these social phenomena, and to determine their basic elemental constructs. He used

theory, mathematics, and common sense to define a force field, and hence to determine the causes of

human and group behavior.



Taguchi methods

Taguchi methods are statistical methods developed by Genichi Taguchi to improve the quality

of manufactured goods, and more recently also applied to biotechnology,[1] marketing and advertising.





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Taguchi methods are considered controversial among some traditional Western statisticians, but others

accept many of his concepts as being useful additions to the body of knowledge.

Taguchi's principal contributions to statistics are:



1. Taguchi loss function;

2. The philosophy of off-line quality control; and

3. Innovations in the design of experiments.



CONTRIBUTIONS



Loss functions

Taguchi's reaction to the classical design of experiments methodology of R. A. Fisher was that it

was perfectly adapted for seeking to improve the mean outcome of a process. As Fisher's work had been

largely motivated by programmes to increase agricultural production, this was hardly surprising. However,

Taguchi realised that in much industrial production, there is a need to produce an outcome on target, for

example, to machine a hole to a specified diameter, or to manufacture a cell to produce a given voltage.

He also realised, as had Walter A. Shewhart and others before him, that excessive variation lay at the root

of poor manufactured quality and that reacting to individual items inside and outside specification was

counterproductive.

He therefore argued that quality engineering should start with an understanding of quality costs in

various situations. In much conventional industrial engineering, the quality costs are simply represented

by the number of items outside specification multiplied by the cost of rework or scrap. However, Taguchi

insisted that manufacturers broaden their horizons to consider cost to society. Though the short-term

costs may simply be those of non-conformance, any item manufactured away from nominal would result

in some loss to the customer or the wider community through early wear-out; difficulties in interfacing

with other parts, themselves probably wide of nominal; or the need to build in safety margins. These

losses are externalities and are usually ignored by manufacturers.[who?] In the wider economy, the Coase

Theorem predicts that they prevent markets from operating efficiently. Taguchi argued that such losses

would inevitably find their way back to the originating corporation (in an effect similar to the tragedy of

the commons), and that by working to minimise them, manufacturers would enhance brand reputation,

win markets and generate profits.

Such losses are, of course, very small when an item is near to nominal. Donald J. Wheeler

characterized the region within specification limits as where we deny that losses exist. As we diverge from

nominal, losses grow until the point where losses are too great to deny and the specification limit is

drawn. All these losses are, as W. Edwards Deming would describe them, unknown and unknowable, but

Taguchi wanted to find a useful way of representing them statistically. Taguchi specified three situations:



1. Larger the better (for example, agricultural yield);

2. Smaller the better (for example, carbon dioxide emissions); and

3. On-target, minimum-variation (for example, a mating part in an assembly).



The first two cases are represented by simple monotonic loss functions. In the third case, Taguchi

adopted a squared-error loss function on the following grounds:



 It is the first symmetric term in the Taylor series expansion of any reasonable, real-life loss

function, and so is a "first-order" approximation;

 Total loss is measured by the variance. As variance is additive, it is an attractive model of cost; and

 There was an established body of statistical theory around the use of the least-squares principle.



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The squared-error loss function had also been used by John von Neumann and Oskar

Morgenstern in the 1930s.

Though much of this thinking is endorsed by statisticians and economists in general, Taguchi

extended the argument to insist that industrial experiments seek to maximise an appropriate signal-to-

noise ratio, representing the magnitude of the mean of a process compared to its variation. Most

statisticians believe Taguchi's signal-to-noise ratios to be effective over too narrow a range of

applications, and they are generally deprecated.



OFF-LINE QUALITY CONTROL

Taguchi realised that the best opportunity to eliminate variation is during the design of a product

and its manufacturing process (Taguchi's rule for manufacturing). Consequently, he developed a strategy

for quality engineering that can be used in both contexts. The process has three stages:



1. System design;

2. Parameter design; and

3. Tolerance design.



System design

This is a design at the conceptual level, involving creativity and innovation.



Parameter design

Once the concept is established, the nominal values of the various dimensions and design

parameters need to be set, the detail design phase of conventional engineering. Taguchi's radical insight

was that the exact choice of values required is under-specified by the performance requirements of the

system. In many circumstances, this allows the parameters to be chosen so as to minimise the effects on

performance arising from variation in manufacture, environment and cumulative damage. This is

sometimes called robustification.



Tolerance design

With a successfully completed parameter design, and an understanding of the effect that the

various parameters have on performance, resources can be focused on reducing and controlling variation

in the critical few dimensions .



Design of experiments

Taguchi developed much of his thinking in isolation from the school of R. A. Fisher, only coming

into direct contact in 1954. His framework for design of experiments is idiosyncratic and often flawed,

but contains much that is of enormous value. He made a number of innovations.



Outer arrays

Unlike the design of experiments work of Fisher, Taguchi sought to understand the influence that

parameters had on variation, not just on the mean. He contended, as had W. Edwards Deming in his

discussion of analytic studies, that conventional sampling is inadequate here as there is no way of

obtaining a random sample of future conditions. In Fisher's work, variation between experimental

replications is a nuisance that the experimenter would like to eliminate whereas, in Taguchi's thinking, it is

a central object of investigation.

Taguchi's innovation was to replicate each experiment by means of an outer array, possibly an

orthogonal array that seeks deliberately to emulate the sources of variation that a product would



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encounter in reality. This is an example of judgment sampling. Though statisticians following in the

Shewhart-Deming tradition have embraced outer arrays, many academics are still skeptical.

Later innovations in outer arrays resulted in "compounded noise". This involves combining a few

noise factors to create two levels in the outer array. First, noise factors that drive output lower, and

second, noise factors that drive output higher. This still emulates the extremes of noise variation but with

fewer test samples required.



MANAGEMENT OF INTERACTIONS

Many of the orthogonal arrays that Taguchi has advocated are saturated arrays, allowing no scope

for estimation of interactions. This is a continuing topic of controversy. However, this is only true for

"control factors" or factors in the "inner array". By combining an inner array of control factors with an

outer array of "noise factors", Taguchi's approach provides full information on control-by-noise

interactions. His concept is that those are the interactions of most interest in achieving a design that is

robust to noise factor variation. In this sense, the Taguchi approach provides more complete interaction

information than typical fractional factorial experiments.



 Followers of Taguchi argue that the designs offer rapid results and that interactions can be

eliminated by proper choice of quality characteristics and by transforming the data. That

notwithstanding, a confirmation experiment offers protection against any residual interactions. If

the quality characteristic represents the energy transformation of the system, then the likelihood of

control factor-by-control factor interactions is greatly reduced, since energy is additive.



 Western statisticians argue that interactions are part of the real world and that Taguchi's arrays

have complicated alias structures that leave interactions difficult to disentangle. George Box and

others have argued that a more effective and efficient approach is to use sequential assembly.



Analysis of experiments

Taguchi introduced many methods for analyzing experimental results including novel applications

of the analysis of variance and minute analysis. Little of this work has been validated by Western

statisticians.

Assessment

Genichi Taguchi has made seminal and valuable methodological innovations in statistics and

engineering, within the Shewhart-Deming tradition. His emphasis on loss to society, techniques for

investigating variation in experiments, and his overall strategy of system, parameter and tolerance design

have been massively influential in improving manufactured quality worldwide. Much of his work was

carried out in isolation from the mainstream of Western statistics and, while this may have facilitated his

creativity, much of the technical detail of Taguchi methods and their benefits to experimentation and

research is only now being studied in the West.



Kaoru Ishikawa

Kaoru Ishikawa (Ishikawa Kaoru) (1915-1989) was a Japanese University professor and

influential quality management innovator best known in North America for the Ishikawa or cause and

effect diagram (also known as Fishbone Diagram) that are used in the analysis of industrial process.

Born in Tokyo, the oldest of the eight sons of Ichiro Ishikawa. In 1939 he graduated University of Tokyo

with an Engineering degree in applied chemistry. His first job was as a naval technical officer (1939-1941)

then moved on to work at the Nissan Liquid Fuel Company until 1947. Ishikawa would now start his





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career as an associate professor at the University of Tokyo. He then undertook the Presidency of the

Musashi Institute of Technology in 1978.

In 1949, Ishikawa joined the Union of Japanese Scientist and Engineers (JUSE) quality control

research group. After World War II Japan looked to transform its industrial sector, which in North

America was then still perceived as a producer of cheap wind-up toys and poor quality cameras? It was

his skill at mobilizing a lot of people towards a specific common goal that was largely responsible for

Japan's quality-improvement initiatives. He translated, integrated and expanded the management concepts

of Dr. Deming and Dr. Juran into the Japanese system.

After becoming a full professor in the Faculty of Engineering at The University of Tokyo (1960)

Ishikawa introduced the concept of quality circles (1962) in conjunction with JUSE. This concept began

as an experiment to see what effect the "leading hand" (Gemba-cho) could have on quality. It was a

natural extension of these forms of training to all levels of an organization (the top and middle managers

having already been trained). Although many companies were invited to participate, only one company at

the time, Nippon Telephone & Telegraph, accepted. Quality Circles would soon become very popular

and form an important link in a company's Total Quality Management System. Ishikawa would write two

books on quality circles (QC Circle Koryo and How to Operate QC Circle Activities).

Among his efforts to promote quality were, the Annual Quality Control Conference for Top

Management (1963) and several books on Quality Control (the Guide to Quality Control was translated

into English). He was the chairman of the editorial board of the monthly Statistical Quality Control.

Ishikawa was involved in international standardization activities.

1982 saw the development of the Ishikawa diagram which is used to determine root causes.

QUALITY CONTRIBUTIONS



 User Friendly Quality Control

 Fishbone Cause and Effect Diagram - Ishikawa diagram

 Implementation of Quality Circles

 Emphasized the 'Internal Customer'

 Shared Vision



Awards and recognition



 1972 American Society for Quality's Eugene L. Grant Award

 1977 Blue Ribbon Medal by the Japanese Government for achievements in industrial

standardization

 1988 Walter A. Shewhart Medal

 1988 Awarded the Order of the Sacred Treasures, Second Class, by the Japanese government.



Ishikawa diagram

Ishikawa diagram (or fishbone diagram or also cause-and-effect diagram) are diagrams, that

shows the causes of a certain event. A common use of the Ishikawa diagram is in product design, to

identify potential factors causing an overall effect.

Ishikawa diagrams were proposed by Kaoru Ishikawa in the 1960s, which pioneered quality

management processes in the Kawasaki shipyards, and in the process became one of the founding fathers

of modern management.

Ishikawa diagrams were proposed by Kaoru Ishikawa in the 1960s, who pioneered quality

management processes in the Kawasaki shipyards, and in the process became one of the founding fathers

of modern management.



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It was first used in the 1960s, and is considered one of the seven basic tools of quality

management, along with the histogram, Pareto chart, check sheet, control chart, flowchart, and scatter

diagram. See Quality Management Glossary. It is known as a fishbone diagram because of its shape,

similar to the side view of a fish skeleton.

Mazda Motors famously used an Ishikawa diagram in the development of the Miata sports car,

where the required result was "Jinba Ittai" or "Horse and Rider as One". The main causes included such

aspects as "touch" and "braking" with the lesser causes including highly granular factors such as "50/50

weight distribution" and "able to rest elbow on top of driver's door". Every factor identified in the

diagram was included in the final design.



Appearance

A generic Ishikawa diagram showing general (red) and more refined (blue) causes for an event.

Most Ishikawa diagrams have a box at the right hand side, where the effect to be examined is written. The

main body of the diagram is a horizontal line from which stems the general causes, represented as

"bones". These are drawn towards the left-hand side of the paper and are each labeled with the causes to

be investigated, often brainstormed beforehand and based on the major causes listed above.

Off each of the large bones there may be smaller bones highlighting more specific aspects of a certain

cause, and sometimes there may be a third level of bones or more. These can be found using the '5 Whys'

technique. When the most probable causes have been identified, they are written in the box along with

the original effect. The more populated bones generally outline more influential factors, with the opposite

applying to bones with fewer "branches". Further analysis of the diagram can be achieved with a Pareto

chart. The Ishikawa concept can also be documented and analyzed through depiction in a matrix format.

It was first used in the 1960s, and is considered one of the seven basic tools of quality

management, along with the histogram, Pareto chart, check sheet, control chart, flowchart, and scatter

diagram. See Quality Management Glossary. It is known as a fishbone diagram because of its shape,

similar to the side view of a fish skeleton.

Mazda Motors famously used an Ishikawa diagram in the development of the Miata sports car,

where the required result was "Jinba Ittai" or "Horse and Rider as One". The main causes included such

aspects as "touch" and "braking" with the lesser causes including highly granular factors such as "50/50

weight distribution" and "able to rest elbow on top of driver's door". Every factor identified in the

diagram was included in the final design.

Causes

Causes in the diagram are often based on a certain set of causes, such as the 6 M's, 8 P's or 4 S's,

described below. Cause-and-effect diagrams can reveal key relationships among various variables, and the

possible causes provide additional insight into process behaviour.

Causes in a typical diagram are normally grouped into categories, the main ones of which are:

THE 6 M'S



Machine, Method, Materials, Maintenance, Man and Mother Nature (Environment)

(recommended for the manufacturing industry).

Note: a more modern selection of categories used in manufacturing includes Equipment, Process,

People, Materials, Environment, and Management.

THE 8 P'S



Price, Promotion, People, Processes, Place / Plant, Policies, Procedures, and Product (or Service)

(recommended for the administration and service industries).





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THE 4 S'S



Surroundings, Suppliers, Systems, Skills (recommended for the service industry).

It can also be used in connection with the Neuro-linguistic programming model of the Neurological

Levels created by Robert Dilts: with Identity, Beliefs and Values, Capability, Behaviour, Environment.

Causes should be derived from brainstorming sessions. Then causes should be sorted through affinity-

grouping to collect similar ideas together. These groups should then be labeled as categories of the

fishbone. They will typically be one of the traditional categories mentioned above but may be something

unique to your application of this tool. Causes should be specific, measurable, and controllable.









CHAPTER X



QUALITY COST

"The cost of quality"It‘s a term that's widely used – and widely misunderstood. The "cost of quality" isn't

the price of creating a quality product or service. It's the cost of NOT creating a quality product or service. Every

time work is redone, the cost of quality increases. Obvious examples include:



 The reworking of a manufactured item.

 The retesting of an assembly.

 The rebuilding of a tool.

 The correction of a bank statement.

 The reworking of a service, such as the reprocessing of a loan operation or the replacement of a

food order in a restaurant.



In short, any cost that would not have been expended if quality were perfect contributes to the cost of quality.

TOTAL QUALITY COSTS

As the figure below shows, quality costs are the total of the cost incurred by:



 Investing in the prevention of nonconformance to requirements.

 Appraising a product or service for conformance to requirements.

 Failing to meet requirements.



QUALITY COSTS—GENERAL DESCRIPTION





Prevention Costs Failure Costs

The costs of all activities specifically designed to The costs resulting from products or services not

prevent poor quality in products or services. conforming to requirements or customer/user needs.

Failure costs are divided into internal and external failure





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Examples are the costs of: categories.

Internal Failure Costs

 New product review Failure costs occurring prior to delivery or shipment of the

 Quality planning product, or the furnishing of a service, to the customer.

 Supplier capability surveys Examples are the costs of:

 Process capability evaluations

 Quality improvement team meetings  Scrap

 Quality improvement projects  Rework

 Quality education and training  Re-inspection

 Re-testing

Appraisal Costs  Material review

The costs associated with measuring, evaluating or  Downgrading

auditing products or services to assure conformance

to quality standards and performance requirements. External Failure Costs

These include the costs of: Failure costs occurring after delivery or shipment of the

product — and during or after furnishing of a service —

 Incoming and source inspection/test of to the customer.

purchased material Examples are the costs of:

 In-process and final inspection/test

 Product, process or service audits

 Processing customer complaints

 Calibration of measuring and test equipment

 Customer returns

 Associated supplies and materials

 Warranty claims

 Product recalls









Feigenbaum defined the following quality cost areas:



Cost area Description Examples



 Quality planning

 Statistical process control

 Investment in quality-

related information systems

Prevention Arise from efforts to keep defects from  Quality training and

costs occurring at all workforce development

 Product-design verification

 Systems development and

Costs of management

control

 Test and inspection of

purchased materials

 Acceptance testing

Appraisal Arise from detecting defects via

 Inspection

costs inspection, test, audit

 Testing

 Checking labor

 Setup for test or inspection





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 Test and inspection

equipment

 Quality audits

 Field testing



 Scrap

Arise from defects caught internally

Internal  Rework

and dealt with by discarding or

failure costs  Material procurement costs

repairing the defective items

Costs of

failure of  Complaints in warranty

control  Complaints out of warranty

External Arise from defects that actually reach  Product service

failure costs customers  Product liability

 Product recall







The central theme of quality improvement is that larger investments in prevention drive even

larger savings in quality-related failures and appraisal efforts. Feigenbaum's categorization allows the

organization to verify this for itself.. When confronted with mounting numbers of defects, organizations

typically react by throwing more and more people into inspection roles. But inspection is never

completely effective, so appraisal costs stay high as long as the failure costs stay high. The only way out of

the predicament is to establish the "right" amount of prevention. Once categorized, quality costs can

serve as a means to measure, analyze, budget, and predict. Variants of the concept of quality costs include

cost of poor quality and categorization based on account type, described by Joseph M. Juran:



Cost area Examples



 Materials scrapped or junked

 Labor and burden on product scrapped or junked

 Labor, materials, and burden necessary to effect repairs on

salvageable product

Tangible costs—factory  Extra operations added because of presence of defectives

accounts  Burden arising from excess production capacity necessitated by

defectives

 Excess inspection costs

 Investigation of causes of defects



 Discount on seconds

Tangible costs—sales  Customer complaints

accounts  Charges to quality guarantee account



 Delays and stoppages caused by defectives

 Customer good will

Intangible costs

 Loss in morale due to friction between departments







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COST OF POOR QUALITY

Cost of poor quality (COPQ) or poor quality costs (PQC), are defined as costs that would

disappear if systems, processes, and products were perfect.

COPQ was popularized by IBM quality expert H. James Harrington in his 1987 book Poor Quality Costs.

COPQ is a refinement of the concept of quality costs. In the 1960s, IBM undertook an effort to study its

own quality costs and tailored the concept for its own use. While Feigenbaum's term "quality costs" is

technically accurate, it's easy for the uninitiated to jump to the conclusion that better quality products cost

more to produce. Harrington adopted the name "poor quality costs" to emphasize the belief that

investment in detection and prevention of product failures is more than offset by the savings in

reductions in product failures.

COPQ decomposes COPQ into the following elements:



Cost Description



Direct COPQ can be directly derived from entries in the

company ledger.



Direct poor-quality costs  Controllable COPQ is directly controllable costs to

ensure that only acceptable products and services reach

 Controllable poor-quality cost the customer.

o Prevention cost  Resultant COPQ are costs incurred because unacceptable

o Appraisal cost products and services were delivered to the customer,

 Resultant poor-quality cost resulting from earlier decisions about how much to invest

o Internal error cost in controllable COPQ

o External error cost  Equipment COPQ are costs to invest in equipment to

 Equipment poor-quality cost measure, accept, or control a product or service. It is

treated separately from controllable costs to

accommodate the effects of depreciation.



Indirect poor-quality costs

Indirect COPQ is difficult to measure because it is a delayed

 Customer-incurred cost result of time, effort, and financial costs incurred by the

 Customer-dissatisfaction cost customer. These customer costs add up to lost sales and

 Loss-of-reputation cost therefore do not appear in the company's ledger.





Examples



Cost element Examples



 Quality planning (for test, inspection, audits,

process control)

Direct poor- Controllable poor- Prevention  Education and training

quality costs quality cost cost  Performing capability analyses

 Conducting design reviews







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 Test and inspection

Appraisal  Supplier acceptance sampling

cost  Auditing processes



 In-process scrap and rework

 Troubleshooting and repairing

 Design changes

Internal error  Additional inventory required to support poor

cost process yields and rejected lots

 Reinspection and retest of reworked items

Resultant poor-  Downgrading

quality cost

 Sales returns and allowances

 Service level agreement penalties

External  Complaint handling

error cost  Field service labor and parts costs incurred due

to warranty obligations



Micrometers, voltmeters, automated test equipment

Equipment poor-quality cost

(but not equipment used to make the product)

 Loss of productivity due to product or service

downtime

 Travel costs and time spent to return defective

product

Customer-incurred cost

Indirect poor-  Repair costs after warranty period

quality costs  Backup product or service to cover failure

periods



Customer-dissatisfaction cost Dissatisfaction shared by word of mouth

Loss-of-reputation cost Customer perception of firm



White collar COPQ

Harrington noted that expanding cost analyses to management and clerical workers could also

make a significant dent in waste. He defined the following costs by functional area:



Functional area Controllable COPQ Resultant COPQ



 Timecard reviews  Billing errors

 Capital equipment reviews  Incorrect accounting entries

Controller COPQ

 Invoicing reviews  Payroll errors



 Design reviews

 Crashes

Software COPQ  Code reviews

 Deadlocks







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 Incorrect outputs



 Security

 Disclosure of trade secrets

 Facility inspection and

 Facilities redesign

Plant administration testing

 Overstaffing/understaffing

COPQ  Machine maintenance

 Equipment downtime/idle time

training



 Vendor reviews

 Periodic vendor surveys  Line-down cost

 Follow-up on delivery  Excessive inventory due to suppliers

Purchasing COPQ

dates  Premium freight cost

 Strike built-in costs



 Sales material review

 Overstock

 Marketing forecast

 Loss of market share

Marketing COPQ  Customer surveys

 Incorrect order entry

 Sales training



 Prescreening applications

 Absenteeism

 Appraisal reviews

 Turnover

Personnel COPQ  Exit interviews

 Grievances

 Attendance tracking



 Packaging evaluations

 OSHA fines

 Layout reviews

 Shipping damage

Industrial engineering  OSHA reports

 Redoing layout

COPQ  Inspection of contract

 Paying contractors for poor work

work







QUALITY COST ANALYSIS: BENEFITS AND RISKS

Quality Cost Analysis

Quality costs are the costs associated with preventing, finding, and correcting defective work.

These costs are huge, running at 20% - 40% of sales. Many of these costs can be significantly reduced or

completely avoided. One of the key functions of a Quality Engineer is the reduction of the total cost of

quality associated with a product.

Here are six useful definitions, as applied to software products. Figure 1 gives examples of the

types of cost. Most of Figure 1‘s examples are (hopefully) self-explanatory, but I‘ll provide some

additional notes on a few of the costs:



o Prevention Costs: Costs of activities that are specifically designed to prevent poor quality.

Examples of "poor quality" include coding errors, design errors, mistakes in the user

manuals, as well as badly documented or unmentionably complex code.





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Note that most of the prevention costs don‘t fit within the Testing Group‘s

budget. This money is spent by the programming, design, and marketing staffs.



o Appraisal Costs: Costs of activities designed to find quality problems, such as code

inspections and any type of testing.



Design reviews are part prevention and part appraisal. To the degree that you‘re

looking for errors in the proposed design itself when you do the review, you‘re

doing an appraisal. To the degree that you are looking for ways to strengthen the

design, you are doing prevention.



o Failure Costs: Costs that result from poor quality, such as the cost of fixing bugs and the

cost of dealing with customer complaints.



o Internal Failure Costs: Failure costs that arise before your company supplies its product

to the customer. Along with costs of finding and fixing bugs are many internal failure costs

borne by groups outside of Product Development. If a bug blocks someone in your

company from doing her job, the costs of the wasted time, the missed milestones, and the

overtime to get back onto schedule are all internal failure costs.



For example, if your company sells thousands of copies of the same program, you

will probably print several thousand copies of a multi-color box that contains and

describes the program. You (your company) will often be able to get a much better

deal by booking press time with the printer in advance. However, if you don‘t get

the artwork to the printer on time, you might have to pay for some or all of that

wasted press time anyway, and then you may have to pay additional printing fees

and rush charges to get the printing done on the new schedule. This can be an

added expense of many thousands of dollars.

Some programming groups treat user interface errors as low priority, leaving them

until the end to fix. This can be a mistake. Marketing staff need pictures of the

product‘s screen long before the program is finished, in order to get the artwork

for the box into the printer on time. User interface bugs — the ones that will be

fixed later — can make it hard for these staff members to take (or mock up)

accurate screen shots. Delays caused by these minor design flaws, or by bugs that

block a packaging staff member from creating or printing special reports, can

cause the company to miss its printer deadline.

Including costs like lost opportunity and cost of delays in numerical estimates of

the total cost of quality can be controversial. Campanella (1990) [5] doesn‘t include

these in a detailed listing of examples. Gryna (1988)[6] recommends against

including costs like these in the published totals because fallout from the

controversy over them can kill the entire quality cost accounting effort. I include

them here because I sometimes find them very useful, even if it might not make

sense to include them in a balance sheet.



o External Failure Costs: Failure costs that arise after your company supplies the product

to the customer, such as customer service costs, or the cost of patching a released product

and distributing the patch.







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External failure costs are huge. It is much cheaper to fix problems before shipping

the defective product to customers.

Some of these costs must be treated with care. For example, the cost of public

relations efforts to soften the publicity effects of bugs is probably not a huge

percentage of your company‘s PR budget. You can‘t charge the entire PR budget

as a quality-related cost. But any money that the PR group has to spend to

specifically cope with potentially bad publicity due to bugs is a failure cost.

I‘ve omitted from Figure 1 several additional costs that I don‘t know how to

estimate, and that I suspect are too often too controversial to use. Of these, my

two strongest themes are cost of high turnover (people quit over quality-related

frustration — this definitely includes sales staff, not just development and support)

and cost of lost pride (many people will work less hard, with less care, if they

believe that the final product will be low quality no matter what they do.)



o Total Cost of Quality: The sum of costs: Prevention + Appraisal + Internal Failure +

External Failure.







Examples of Quality Costs Associated with Software Products



Prevention Appraisal





 Staff training

 Requirements analysis

 Early prototyping  Design review

 Fault-tolerant design  Code inspection

 Defensive programming  Glass box testing

 Usability analysis  Black box testing

 Clear specification  Training testers

 Accurate internal documentation  Beta testing

 Evaluation of the reliability of  Test automation

development tools (before buying  Usability testing

them) or of other potential  Pre-release out-of-box testing by

components of the product customer service staff





Internal Failure External Failure





 Bug fixes

 Regression testing

 Wasted in-house user time  Technical support calls[9]

 Wasted tester time  Preparation of support answer

 Wasted writer time books

 Wasted marketer time  Investigation of customer

 Wasted advertisements [7] complaints

 Direct cost of late shipment [8]



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 Opportunity cost of late shipment  Refunds and recalls

 Coding / testing of interim bug fix

releases

 Shipping of updated product

 Added expense of supporting

multiple versions of the product in

the field

 PR work to soften drafts of harsh

reviews

 Lost sales

 Lost customer goodwill

 Discounts to resellers to encourage

them to keep selling the product

 Warranty costs

 Liability costs

 Government investigations[10]

 Penalties[11]

 All other costs imposed by law







What Makes this Approach Powerful?

Over the long term, a project (or corporate) cost accounting system that tracks quality-related

costs can be a fundamentally important management tool. This is the path that Juran and Feigenbaum

will lead you down, and they and their followers have frequently and eloquently explained the path, the

system, and the goal. . There is significant benefit in using the language and insights of quality cost

analysis, on a project/product by project/product basis, even in a company that has no interest in Total

Quality Management or other formal quality management models.

Here‘s an example. Suppose that some feature has been designed in a way that you believe will

be awkward and annoying for the customer. You raise the issue and the project manager rejects your

report as subjective. It‘s "not a bug." Where do you go if you don‘t want to drop this issue? One

approach is to keep taking it to higher-level managers within product development (or within the

company as a whole). But without additional arguments, you‘ll often keep losing, without making any

friends in the process. Suppose that you change your emphasis instead. Rather than saying that, in your

opinion, customers won‘t be happy, collect some other data:



o Ask the writers: Is this design odd enough that it is causing extra effort to document?

Would a simpler design reduce writing time and the number of pages in the manual?



o Ask the training staff: Are they going to have to spend extra time in class, and to write

more supplementary materials because of this design?



o Ask Technical Support and Customer Service: Will this design increase support costs?

Will it take longer to train support staff? Will there be more calls for explanations or help?

More complaints? Have customers asked for refunds in previous versions of the product

because of features designed like this one?









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o Check for related problems: Is this design having other effects on the reliability of the

program? Has it caused other bugs? (Look in the database.) Made the code harder to

change? (Ask the programmers.)



o Ask the sales staff: If you think that this feature is very visible, and visibly wrong, ask

whether it will interfere with sales demonstrations, or add to customer resistance.



o What about magazine reviews? Is this problem likely to be visible enough to be

complained about by reviewers? If you think so, check your impression with someone in

Marketing or PR.



You won‘t get cost estimates from everyone, but you might be able to get ballpark estimates from

most, along with one or two carefully considered estimates. This is enough to give you a range to present

at the next project meeting or in a follow-up to your original bug report. Notice the difference in your

posture:



o You‘re no longer presenting your opinion that the feature is a problem. You‘re presenting

information collected from several parts of the company that demonstrates that this feature‘s

design is a problem.



o You‘re no longer arguing that the feature should be changed just to improve the quality. No

one else in the room can posture and say that you‘re being "idealistic" whereas a more

pragmatic, real-world businessperson wouldn‘t worry about problems like this one. Instead,

you‘re the one making the hard-nosed business argument, "This design is going to cost us $X

in failure costs. How much will it cost to fix it?"



o Your estimates are based on information from other stakeholders in this project. If you‘ve

fairly represented their views, you‘ll get support from them, at least to the extent of them

saying that you are honestly representing the data you‘ve collected.



Along with arguing about individual bugs, or groups of bugs, this approach opens up

opportunities for you (and other non-testers who come to realize the power of your approach) to make

business cases on several other types of issues. For example:



o The question of who should do unit testing (the programmers, the testers, or no one) can be

phrased and studied as a cost-of-quality issue. The programmers might be more efficient than

testers who don‘t know the code, but the testers might be less expensive per hour than the

programmers, and easier to recruit and train, and safer (unlike newly added programmers,

new testers can‘t write new bugs into the code) to add late in the project.



o The depth of the user manual‘s index is a cost-of-quality issue. An excellent index might cost

35 indexer-minutes per page of the manual (so a 200 page book would take over three

person-weeks to index). Trade this cost against the reduction in support calls because people

can find answers to their questions in the manual.



o The best investment to achieve better quality might be additional training and staffing of the

programming group (prevents the bugs rather than find and fix them).







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o You (in combination with the Documentation, Marketing, or Customer Service group) might

demonstrate that the user interface must be fixed and frozen sooner because of the impact of

late changes on the costs of developing documentation, packaging, marketing collaterals,

training materials, and support materials.



IMPLEMENTATION RISKS

Gryna (1988) and Juran & Gryna (1980) point out several problems that have caused cost-of-

quality approaches to fail. I‘ll mention two of the main ones here. First, it‘s unwise to try to achieve too

much, too fast. For example, don‘t try to apply a quality cost system to every project until you‘ve applied

it successfully to one project. And don‘t try to measure all of the costs, because you probably can‘t.

Second, beware of insisting on controversial costs. Gryna (1988) [17] points out several types of costs

that other managers might challenge as not being quality-related. If you include these costs in your totals

(such as total cost of quality), some readers will believe that you are padding these totals, to achieve a

more dramatic effect. Gryna‘s advice is to not include them. This is usually wise advice, but it can lead

you to underestimate your customer‘s probable dissatisfaction with your product. As we see in the next

section, down that road lays LawyerLand.



THE DARK SIDE OF QUALITY COST ANALYSIS

Quality Cost Analysis looks at the company‘s costs, not the customer‘s costs. The manufacturer

and seller are definitely not the only people who suffer quality-related costs. The customer suffers quality-

related costs too. If a manufacturer sells a bad product, the customer faces significant expenses in dealing

with that bad product. The Ford Pinto litigation provided the most famous example of a quality cost

analysis that evaluated company costs without considering customers‘ costs from the customers‘

viewpoint. Among the documents produced in these cases was the Grush-Saunby report, which looked at

costs associated with fuel tank integrity. In other words, it looked cheaper to pay an average of $200,000

per death in lawsuit costs than to pay $11 per car to prevent fuel tank explosions. Ultimately, the lawsuit

losses were much higher.

This kind of analysis didn‘t go away with the Pinto. For example, in the more recent case of

General Motors Corp. v. Johnston (1992), a PROM controlled the fuel injector in a pickup truck. The

truck stalled because of a defect in the PROM and in the ensuing accident, Johnston‘s seven-year old

grandchild was killed. The Alabama Supreme Court justified an award of $7.5 million in punitive damages

against GM by noting that GM "saved approximately $42,000,000 by not having a recall or otherwise

notifying its purchasers of the problem related to the PROM." Most software failures don‘t lead to

deaths. Most software projects involve conscious tradeoffs among several factors, including cost, time to

completion, richness of the feature set, and reliability. There is nothing wrong with doing this type of

business tradeoff, consciously and explicitly, unless you fail to take into account the fact that some of the

problems that you leave in the product might cost your customers much, much more than they cost your

company. Figure 2 lists some of the external failure costs that are borne by customers, rather than by the

company.



Figure 2. Comparison of External Failure Costs Borne by the Buyer and the Seller





Seller: external failure costs Customer: failure costs









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These are the types of costs absorbed by These are the types of costs absorbed by

the seller that releases a defective product. the customer who buys a defective

product.



 Technical support calls

 Preparation of support answer

books  Wasted time

 Investigation of customer  Lost data

complaints  Lost business

 Refunds and recalls  Embarrassment

 Coding / testing of interim bug fix  Frustrated employees quit

releases  Demos or presentations to potential

 Shipping of updated product customers fail because of the

 Added expense of supporting software

multiple versions of the product in  Failure when attempting other tasks

the field that can only be done once

 PR work to soften drafts of harsh  Cost of replacing product

reviews  Cost of reconfiguring the system

 Lost sales  Cost of recovery software

 Lost customer goodwill  Cost of tech support

 Discounts to resellers to encourage  Injury / death

them to keep selling the product

 Warranty costs

 Liability costs

 Government investigations

 Penalties

 All other costs imposed by law







The point of quality-related litigation is to transfer some of the costs borne by a cheated or injured

customer back to the maker or seller of the defective product. The well-publicized cases are for disastrous

personal injuries, but there are plenty of cases against computer companies and software companies for

breach of contract, breach of warranty, fraud, etc. The problem of cost-of-quality analysis is that it sets us

up to underestimate our litigation and customer dissatisfaction risks. We think when we have estimated

the total cost of quality associated with a project, that we have done a fairly complete analysis. But if we

don‘t take customers‘ external failure costs into account at some point, we can be surprised by huge

increased costs (lawsuits) over decisions that we thought, in our incomplete analyses, were safe and

reasonable.









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CHAPTER XI



Total Quality Audit



BASIC PRINCIPLES OF QUALITY AUDIT



Some basic principles of managing the auditing activities are:-

 The purpose of the audit must be clarified and approved

 The audit plan must be well prepared

 The audit plan and final report must be documented

 Appraisal against standards must be objectives and factual, Objective evidence must be

obtained for any non-conformities

 The audit should not unduly interfere with on-going operation

 The frequency of audit should be done according to actual needs

 Working paper and related documents should be kept in proper form and order

 The auditor must follow up or re-audit remedial action

The auditor must be qualified or trained and independent of the to be audited area (in the case of internal

quality audit).



The BS 5750/ISO 9000 schemes was introduced to rationalize all the quality system assessment

scheme as a third party audit scheme operated by an independent body which register companies as

complying with the BS 5750/ISO 9000 standards. In addition, adopting ISO 9000 Quality Standards as

an important role for the third party audit has the following benefits :-





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1. ISO 9000 is an international recognized quality standard.

2. A company comply with ISO 9000 entitles the firm to use a special symbol in their company

literature, packaging

3. Manufacturer can rely on the third party audit on the suppliers who are certified for ISO 9000

and need not conduct his own audit

4. Company saves time and money to audit their suppliers especially overseas suppliers. It is also

not practical audit all suppliers by themselves.



INTERNAL QUALITY AUDIT



Internal Quality Audit also called the First Party Audit is described as " an independent appraisal

function established within an organization to examine and evaluate its activities as a service to the

organization". Since internal quality auditors are employee of the organization, they are the best person to

know the "insight" of the operation of the and actual practice of the quality system. Therefore, they can

pin-point the inefficient and weakness of the quality systems of an organization.

Internal Quality system is also used as an important program in the company to arise the

awareness of the quality system during the preparation of the quality audit. It also can serve as a platform

to educate employees on the importance of the quality system and its compliance. From the result of the

quality audit, any non- conformance of the quality system will be used to an organization to for further

reinforcement and improvement of the quality system

Regular and effective Internal Quality Audit can help to achieve and maintain second or third party audit

registration. Internal quality audit can also help the auditors to understand an organization in a wider

perspective and provide suggestion for improvement.

QUALIFICATION CRITERIA FOR AUDITOR

Some of the qualification criteria for assessing a potential candidates are :- ( Reference

ISO 10011-2 Part 2 )

4.1) Auditor should have completed at least secondary education

4.2) Candidate can demonstrate competence in clearly and fluently expressing concepts

and ideas orally and in writing in their officially recognized language

4.3) Candidate should have undergone training to be competence in the skill and managing

an audit. ( or otherwise training will be provided )

4.4) Have knowledge and understanding of the standards

4.5) Candidate should have assessment techniques of examining questioning evaluating

and reporting

4.6) Candidates should have some experience in quality auditing

4.7) Candidate should be opening minded and mature

4.8) Candidate should posse‘s sound judgment, analytical skill and tenacity

4.9) Candidate should be able to perceive situation in a realistic way



OBJECTIVES OF EXTERNAL QUALITY AUDIT

The objective of an external quality audit such as supplier audit, are:-

1. Internal QA Standards requirement

2. Improving quality of a supplier for the vendor development program



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3. Selecting and evaluating new or existing suppliers

4. Increase quality awareness of suppliers

5. Help implementing quality system for suppliers

An external audit would comprise an overall audit program, individual company audit and

element to be assessed during each audit is to ensure a systematic and consistency in each audit. It

can be treated as a standards for an organization to be adopted to asses its suppliers to achieve the

desired quality standards



Audit Function Deployment to include typical functions according ISO 9001: 1994 are illustrated in the

table below:-



Quality System



Management Marketing Design Purchasing Production



 Policy  Contract Development  Vendor selection  Handlin

 Training Review & Planning  Purchasing Data  Storage

 Organization   Metrology  Identifi

 - Responsibility Organizational  Verification  Traceab

o -authority Technical  Incoming material  Process

interface  Non-conforming  Special

- verification  - segregation  Inspect

resources Design Input o - review  - in-pro

o - disposition o

- Design

management Output - corrective

representative action

Design

- Review

management

review Verification



Validation



Design

Changes



Document

Control



- Approval



- Issue



- Changes



OTHER PURPOSES OF THE QUALITY AUDIT



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Other purpose of the quality audit is:-

 To determine the effectiveness of the implemented quality system in meeting specified quality

objectives.

 To provide the auditee an opportunity to identify weakness and improve the quality system (

in the case of internal quality audit )

 To report to the management the effectiveness of the quality system

 To provide an opportunity to assess the suitability and effectiveness of a process in totality for

strength and weakness for continuous process improvement ( in the case of process audit )

 To "benchmark" good practices in one department for other department to follow ( in the

case of internal audit )



THE LEAD AUDITOR



The Lead auditor is responsible for the preparation of the audit report. However, each auditor is

responsible to provide a report of his audited area.

The audit report should faithfully reflect both the tone and content of the audit. It should be dated and

signed by the lead auditor. It should contain the following elements as applicable:-

 The scope and objective of the audit

 details of the audit plan, the identification of the audit team members and auditee's

representative, audit dates and identification of the specific organization audited

 identification of the reference documents against the audit conducted ( quality system

standards e.g. ISO 9000 , auditee's quality manual, procedures and work instructions etc

 audit team's judgment of the extent of the auditee's compliance with the applicable quality

system standard and related documentation

 the system's ability to achieve defined quality objectives

 the audit report distribution list

Audit report containing confidential or proprietary information shall be suitably safeguarded by the

auditing organization.









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CHAPTER XII

EMERGING TRENDS IN TOTAL QUALITY MANAGEMENT



QUALITY PLANNING

This chapter provides an overview of needs, approach, and methodology for quality

planning in healthcare laboratories. This overview should be useful as a preview, review, or quick

refresher of the quality-planning process and the training materials available to support your applications.

Because we all learn best from tests and methods that are of interest to ourselves, the remaining lessons

present a variety of example applications for routine chemistry, toxicology, hematology, endocrinology,

and immunology. Use these applications to master the steps of the quality-planning process and become

proficient in performing the process. Proficient means you should be able to select a QC procedure and

identify the TQC strategy in one minute or less. Even the one-minute manager can perform quality

planning with support of the tools and technology available today!









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Quality planning overview

A series of fifteen lessons are available to support your understanding and application of quality

planning for laboratory tests. A manual quality-planning process is available as part of these training

materials and can be readily implemented in any healthcare laboratory. Mini-courses, short courses, and

workshops may utilize only a subset of these lessons, so you may find it useful to know about all the

lessons that are available.

The first three lessons in this series [1-3] focus on the WHY or need for quality planning, the next

two [4-5] describe WHAT should be done to plan the quality of laboratory tests, and the following three

[6-8] provide the HOW or practical methodology for performing quality planning quickly and easily. This

lesson summarizes important points about the quality-planning process and provides additional

discussion about WHEN to do quality planning and WHO should be involved and responsible. The

remaining lessons provide example applications for different areas of the laboratory.

The WHY of quality planning









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1. The laboratory must provide a final and independent assurance of quality for the test results it

produces. This need for an independent quality management process is described in A Wake-up Call for

Laboratory Quality Management , which discusses the limitations of current laboratory management

practices that bundle many of the essential quality management activities into the services provided by

manufacturers of analytical systems. A current FDA action against a major manufacturer exposes several

fallacies in current management thinking:



o Analytical quality is not a given!

o Responsibility for quality can't be out-sourced!

o Compliance is not enough!

o Statistical QC can't be eliminated!

o Minimum personnel standards are skills are not sufficien









2. The principles of Total Quality

Management (TQM) point out the

importance of defining quality standards

and establishing a quality planning process that builds the desired quality into daily production processes.



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Assuring Quality through Total Quality Management describes a quality management framework

that is constructed from components - Quality Laboratory Processes, Quality Control, Quality

Assessment, Quality Improvement, and Quality Planning - and assembled in a loop or cycle that provides

continuous improvement of quality (as shown in the accompanying figure). This quality management

framework should be centered on, or guided by, quality goals or standards. In most laboratories,

implementation places a priority on the following:



o Defining quality goals or standards

o Establishing a quality planning process



3. Current regulatory and professional practice guidelines identify the need for quality planning and

provide guidance for implementing a quality-planning process. In Complying with Regulations,

Standards, and Practice Guidelines , the TQM framework is compared with JCAHO IOP guidelines

(Joint Commision for Accreditation of Healthcare Organizations, Improving Organizational

Performance) CLIA regulatory rules (Clinical Laboratory Improvement Amendments), and NCCLS QC

practice guidelines (National Committee for Clinical Laboratory Standards), which reveals that:



o JCAHO IOP strongly recommends quality assessment, quality improvement, and quality

planning.

o CLIA rules emphasize quality standards, quality laboratory processes, quality control, and

quality assessment.

o JCAHO and CLIA together support the complete framework identified earlier from

principles of TQM.

o NCCLS provides guidelines for planning QC procedures as shown in the accompanying

figure. The planning process includes steps for defining the quality required for a test,

determining method performance, identifying candidate statistical QC strategies, predicting

QC performance, setting goals for QC performance, and finally, selecting an appropriate

QC procedure.









The WHAT of quality

planning





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4. A detailed quality-planning process can be developed by building on the NCCLS QC planning

guidelines and introducing a quality-planning tool that provides information about the performance of

different QC procedures. Devising a Practical Process describes the steps of a quality-planning

process that utilizing a graphical quality-planning tool - the chart of operating specifications, or OPSpecs

chart. As shown in the accompanying figure, the two major areas of application are:



o Setting specifications for imprecision and bias when selecting a new analytical method, and

o Selecting control rules and numbers of control measurements when selecting a new QC

procedure.









5. The first and most important step

in the planning process is to define

the quality required for the test.

Unfortunately, this is not a trivial pursuit!

Defining Quality Requirements

describes the difficulties and the

solution:



o Concepts and terms are not consistent in the current literature on quality requirements,

therefore several different formats exist, such as the medically important change in test

results, the allowable total error, the maximum allowable standard deviation, and the

maximum allowable bias.

o Multiple factors affect test variability and different factors are included in different

concepts or formats of quality requirements.

o A system of quality standards must be recognized to understand the relationship between

the different types and formats of quality requirements. The accompanying figure

illustrates a system involving clinical outcome criteria, analytical outcome criteria, and

method performance criteria.

o All current quality standards assume that method performance is stable and do not

consider the need for QC to detect unstable performance.

o Quality standards must be converted into specifications for the imprecision and inaccuracy

that are allowable and the control rules and number of control measurements that are

necessary to detect medically important errors - the bottom line in a system of quality

standards.







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The HOW of quality planning









6. A practical tool for translating quality

requirements into operating

specifications is needed to make quality

planning quick and easy to perform. Adopting

the OPSpecs Chart as Your Planning Tool provides the basic details about a graphical tool called the

chart of operating specifications (or OPSpecs chart), including:



o How to read an OPSpecs chart and recognize the imprecision and inaccuracy that are

allowable for different QC procedures.

o How to determine method performance specifications from the x-intercept of the

operating limits of different QC procedures

o How to select a QC procedure by plotting the operating point of your method (observed

bias as y, observed imprecision as x).

o How to assess the need for quality improvement by identifying changes in imprecision and

inaccuracy that would lead to better and easier quality control.









7. Cost-effective operation of testing

processes depends on individualizing the QC

procedures for each test and method in the





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laboratory. QC is a technical management strategy and statistical QC is one component of the broad or

Total QC strategy. Formulating a Total Quality Control Strategy describes how to establish the

appropriate balance between statistical and non-statistical components (preventive maintenance,

instrument function checks, performance validation tests, patient data QC, in-service training) on the

basis of the error detection capability of the statistical QC procedure for the application of interest. Three

general strategies are pictured here:



o A HI-Ped strategy is appropriate when statistical QC provides 90% detection of medically

important errors. Costs are minimized by selecting QC procedures that have a low number

of control measurements and a low probability or chance of false rejections.

o A MOD-Ped strategy is appropriate when statistical QC provides at least 50% detection of

medically important errors. Costs increase to achieve the maximum detection possible with

the maximum amount of statistical QC that is practical. Preventive efforts and non-

statistical QC increase.

o A LO-Ped strategy is needed when statistical QC provides less than 50% detection of

medically important errors. The emphasis must be on prevention of errors through

operator training, preventive maintenance, verification of individual performance factors

through instrument function checks and method validation tests, and monitoring patient

test results by correlation with other diagnostic information or by patient data QC

procedures. Cost is high and efforts should be made to improve analytical performance.









Normalized OPSpecs charts provide a practical tool for doing quality planning by hand.

Implementing a Manual Process using Normalized OPSpecs Charts

Describes how OPSpecs charts can be scaled to be used with any quality requirement. As

shown here, the y-axis is scaled from 0 to 100 and the x-axis is scaled from 0 to 50. The operating

point is then calculated as a percent of the quality requirement and plotted on the normalized chart.

By using this approach, only six charts are needed to select a QC procedure for any test. Everything

you need to get started is available from the

Internet:









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9. The steps of the manual

planning process using

normalized OPSpecs charts are

summarized in the accompanying

diagram.









o Define the quality requirement at a medically important decision using the format of an

allowable total error, such as given by the CLIA criteria for acceptable performance in a

proficiency testing survey.

o Assess method inaccuracy as the %bias and method imprecision as the %CV at the

medically important decision level.

o Calculate the y-coordinate [(%bias/%TEa)*100] and x-coordinate [(%CV)/%TEa)*100]

for the normalized operating point.

o Plot the normalized operating point on the OPSpecs charts selected for the number of

control materials to be analyzed (2 materials or 3 materials).

o Inspect the normalized OPSpecs charts in the order low N 90% AQA, high N 90% AQA,

and high N 50% AQA.

o Select the control rules and total number of control measurements from the first chart that

provides a solution (i.e., provides a QC procedure whose operating limits are above the

operating point). Try to achieve 90% error detection with 5% or less false rejections. If no

selection is found, use your maximum QC procedure.

o Adopt a Total QC strategy to balance the efforts expended on statistical QC and other

non-statistical QC components. Adopt a Hi-Ped TQC strategy when the QC selection is

made from a 90% OPSpecs chart; adopt a Mod-Ped TQC strategy when the QC selection

is made from a 50% OPSpecs chart; adopt a Lo-Ped TQC strategy when a maximum QC

procedure must be used.



The WHEN of quality planning

In a healthcare laboratory, quality planning should be an ongoing activity. It's never too late or too early

to apply the quality planning process. You need to begin right away! Given that the quality-planning

process can be fast and easy to perform - one minute or less, the biggest demand on your time will be the

time needed to define the quality requirement for the test and the time needed to determine the





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imprecision and inaccuracy of your methods. Once you become proficient at using the quality-planning

process, you can apply the process to accomplish the following:



o establish method specifications for purchasing new analytical systems;

o select QC procedures once you have determined the imprecision and inaccuracy from your

initial method validation studies;

o adapt the QC procedures (if necessary) to accommodate the performance of your methods

under routine conditions, as characterized by estimates of imprecision from routine QC

data and estimates of inaccuracy or bias from monthly peer-comparison results and

periodic proficiency testing surveys;

o identify tests and methods that need improved analytical performance;

o prioritize purchases of new methods, instruments, and analytical systems on the basis of

needed improvements in analytical quality;

o assign experienced analysts to analytical systems that require technical expertise to assure

quality;

o determine which methods and instruments are best suited for cross-training and

widespread rotations of analysts;

o Update the QC guidelines during the annual review of your procedure manuals.



The WHO of quality planning

In workshops and seminars, I often tell participants that I can teach fifth and six grade students to

do quality planning and it would take less time than teaching adults. The point of that statement is to

emphasize that we have a well-defined quality-planning process and it's easy to do if you follow the

directions. If given a quality requirement, the OPSpecs charts for that requirement, and the figures for the

method bias and CV, any fifth or sixth grader could plot the operating point and identify the line or lines

above the point. The mechanics of using an OPSpecs chart are simple.

The things that are difficult in this process are

(a) Defining the quality requirement for the test and

(b) Determining the precision and accuracy of the method. Those steps require skilled and experienced

laboratory scientists.

In principle, the medical director of the laboratory has the responsibility for defining the quality

requirements for the tests. In practice, that activity may be delegated to managers, supervisors, lead

analysts, or quality specialists. In cases where CLIA has defined an allowable total error, that requirement

can be used as the starting point. Anyone familiar with the CLIA regulations could look up the

requirements.

Reliable estimates of method imprecision and inaccuracy depend on proper statistical analysis of

sets of experimental data, initially from method validation studies and later on from routine QC, monthly

peer-review comparisons, and periodic proficiency testing surveys. Here's where training and experience

with method validation studies and routine QC are important. Some statistical skills are needed - at least

the knowledge of which experiments and statistics provide reliable estimates of different types of

analytical errors. In many laboratories, these skills reside with QA or QC specialists, R&D technologists,

senior analysts, or experienced managers and supervisors.

Example applications for practice

Cholesterol is my favorite test! I use cholesterol as an example over and over and over because it

illustrates many of the problems and difficulties you can expect to encounter with other tests. As a clinical

chemist, I've followed the evolution of cholesterol methodology and quality standards for over thirty

years. In many ways, cholesterol provides a model system that illustrates the importance of analytical



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quality management in making test results commutable, i.e., interchangable from method to method and

lab to lab.

Commutability should be our goal for all laboratory tests, not just cholesterol. This will require

that each laboratory carefully plan it's testing processes to assure the quality required by national

standards and guidelines. The cholesterol story shows the importance of having methods that are highly

specific, excellent analytical performance (imprecision and inaccuracy), traceability of standards and

calibrators, comparability between field methods and a national reference method, and statistical QC for

assuring the quality of routine test results.

Few tests and methods are as well characterized and well studied as cholesterol. Therefore, we can

expect there will be difficulties in assuring the quality of many of the other tests being performed in our

laboratories. Even so, we will gain many useful insights into our quality management problems if we

apply the quality-planning process to all our tests. To help get started with tests you're familiar with, the

remaining lessons provide example applications in the following areas:



QUALITY STORYBOARD

A Quality storyboard is a visual method for displaying a Quality Control story (QC story). Some

enterprises have developed a storyboard format for telling the QC story, for example at Yokogawa-Hewlett-

Packard in Japan, the story is told using a flip chart which is 6 feet by 6 feet (2 x 2 meters). The project team uses

colored markers to show the PDSA cycle (Shewhart cycle) plus the SDSA cycle (SDSA = Standardize, Do, Study,

Act).

A QC story is an element of Policy Deployment (Hoshin Kanri). After each manager writes an

interpretation of the policy statement, the interpretation is discussed with the next manager above to reconcile

differences in understanding and direction. In this way they play ―catchball‖ with the policy and develop a

consensus.

Worker participation in managerial diagnosis

When the management attempts to make a managerial diagnosis, it is important that the people whose

work is being diagnosed be properly prepared to enter the discussion. For this purpose, it is very helpful if

everyone knows how to tell the QC story. Telling the story properly requires seven steps.

1. Plan and Problem definition:

This step includes an explanation of why the problem is important (which will tie it to the priority

statements of the top management or to a problem that is essential as seen at the lower levels). Normally this step

includes a discussion of the losses that occur because of the problem, the team that will work on it, and an estimate

of what might be done. A target is often specified though it is understood that reaching such a target cannot be

guaranteed. A schedule is proposed.

2. Data:

This step involves observing the time, place, type and symptoms of the problem. It involves data gathering

and display in an attempt to understand the important aspects of the problem.

3. Analysis:

In this step the various tools of quality analysis are used, such as Control charts, Pareto charts, cause-and-

effect diagrams, scatter diagrams, histograms, etc.

4. Action:

Based on the analysis, an action is taken.

5. Study:

The results are studied to see if they conform to what was expected and to learn from what was not

expected. Data are taken to confirm the action.

6. Act/Standardization:





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Appropriate steps are taken to see that the gains are secured. New standard procedures are introduced.

7. Plans for the future:

As a result of solving this problem, other problems will have been identified and other opportunities

recognized. These seven steps DO NOT describe how a problem is solved. Problem solving requires a great deal

of iteration and it is often necessary to go back to a previous step as new data are found and better analyses are

made. However, when it comes time to report on what was done, the above format provides the basis for telling

the story in a way that makes it comprehensible to the upper levels of management.

Questions to guide constructing a Quality storyboard



Definition of the problem:



 Does the Problem definition contain three parts: Direction, Measure and Reference.

 Did you avoid words like "improve" and "lack of"?

 Have you avoided using "and" to address more than one issue in the Problem definition?





Why Selected:



 Have you explained how you know this is the most important issue to work on?

 Have you shown how the issue relates to the customer or customer satisfaction, or how it will

benefit the customer?

 Have you explained the method used to select the issue?



Initial state:



 Have you described, in numerical terms, the status of the measure in the Problem definition?

 Have you collected time series data?

 Have you provided some historical information about the status of the measure?

 Are data displayed in a visual, graphical format?

 Is there a flowchart or other explanation of the status of the process at the beginning of the

project?

 Have you included other facts that would help the reader understand the initial situation?



Analysis of Causes:



 Is there a clear statement of the major cause(s) of the issue?

 Have you explained how the possible causes were theorized?

 Are data included showing how the main causes were identified?

 Are data displayed in such a way that the connection between the issue and the cause(s) is clear?

 Have you explained how the data were collected and over what time period they were collected?



Plans:



 Is there a complete Purpose Statement and objectives designed to move toward the purpose:

Direction, measure, reference, target, time frame, and owner?

 Is it clear how the target was derived from the analysis?

 Is it clear that the actions in the plan are aimed at correcting root cause(s)?







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 Have you indicated what alternative solutions were considered, and how they were evaluated to

select the best improvement theory?

 Have you included a copy of the planning documents?

 Have you indicated whether the plan was implemented on schedule?



Study:



 Is there a comparison of the target in the improvement theory and the actual results?

 Are the results displayed in the same graphical format as the information in "Initial state" or

"Analysis"?

 Have you indicated whether the results were achieved in the expected time frame?

 If the results did not match the objectives or were achieved outside the expected time, have you

provided an analysis of the differences?

 Have you included any other related results, good or bad?



Act/Standardization:



 Have you explained the actions taken to hold the gain and updated all related documentation,

training in the new process, skills training, physical reorganization, sharing, or process monitoring?



Future Plans:



 Have you included a list of possible next projects?

 Have you indicated which of the possible projects will be the next issue for improvement?



Believed to have been first developed by Japanese Tractor Company, Komatsu. Quality storyboards were

also used by Florida Power & Light as part of their quality drive during the 1980's to win the Deming

Prize.





1. A Wake-up call for Quality Management



FDA action against a manufacturer



 A sentinel event calling for independent analytical quality management

 Analytical quality is not a given!

 Responsibility cannot be out-sourced!

 Compliance is not enough!

 Statistical QC can't be eliminated!

 Minimum personnel standards and skills are not sufficient!

 HELP is available from the Internet

 Independent analytical quality management still depends on manufacturers



It's the year 2008 - the inception of a new millennium! Everyone is looking forward to the future and to a

new and better world. Better quality healthcare is on everyone's list for this new and better world. Better

quality laboratory testing should also be on the list!

Just when everyone thought the issue of quality in laboratory testing was dead and buried, a major

supplier of diagnostic testing materials and systems was cited for not following the Food and Drug





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Administration's (FDA) Good Manufacturing Practices (GMP) or Quality System Regulations. This left

laboratories scrambling to maintain testing services.









FDA action against a manufacturer

On November 2nd, 1999, the Food and

Drug Administration and Abbott

Laboratories entered into a legal agreement - a consent decree - to settle a dispute about manufacturing

practices. This agreement required Abbott to stop manufacturing certain diagnostic test kits within thirty

days (by December 2,1999) and to make corrective changes in the manufacturing processes in it's Lake

County IL facilities. On November 19, 1999, the Northern District Court of Illinois extended the

deadline until January 10, 2000.

In the FDA "Dear Colleague Letter", the consent decree is described as a permanent injunction

that requires Abbott to discontinue sales of certain tests within thirty days because of long standing

failure to comply with FDA's Good Manufacturing Practices (GMP) or Quality System Regulation (QSR)

and its failure to fulfill commitments to correct deficiencies in its manufacturing operations.

In a press release, Abbott pointed out that the consent decree did not require the recall of any

diagnostic products and that certain products would continue to be available.‖ The decree allows for the

continued manufacture and distribution of medically necessary diagnostic products made in Lake County,

Ill., such as assays for hepatitis, retrovirus, cardiovascular disease, cancer, thyroid disorders, fertility, drug

monitoring, and congenital and respiratory conditions. However, Abbott is prohibited from

manufacturing or distributing certain diagnostic products until Abbott ensure the processes in its Lake

County, Ill., diagnostics manufacturing operations conform with the current Quality System Regulation."

The November 3, 1999, Washington Post brought the issue to the public's attention with it's

article on "Medical Test Maker Fined $100 Million for Poor Quality Control," in which it stated:

"Abbott Laboratories, the nation's largest maker of medical laboratory tests, agreed yesterday to pay a

$100 million fine for failing to correct defects in its manufacturing processes despite six years of

warnings. The penalty was the largest ever levied by the Food and Drug Administration."

The LA Times also drew public attention with its article "Abbott Labs to Pay $100m to FDA."

"The issues have to do with manufacturing processes and not with known instances of patient

harm,' said FDA medical devices Chief Dr. David Feigal. Under the consent decree, Abbott is permitted

to continue selling only certain tests the FDA deems medically necessary… The FDA offered an

important warning for laboratories, which have certain quality-control to catch glitches with products.





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Labs that continue to use Abbott diagnostic products should 'increase their vigilance in these areas, to

make sure these tests are performing,' Feigal said."

Additional information for the laboratory community was also made available by professional

organizations. AACC delivered three MANAGEMENT ALERT bulletins via fax broadcasts in the days

following the consent decree and established a webpage with sources of information

(http://www.aacc.org/abbott.stm). In a fax broadcast on November 9, CLMA announced it was

petitioning the FDA to extend the notice before discontinuing sales of the tests. The petition would

extend the thirty day period by an additional fourteen days. CLMA also established a special page on its

website (http://www.clma.org/) and organized a special audio conference November 18, 1999 to focus

on practical strategies for transitioning products affected by the consent decree.

A sentinel event calling for independent analytical quality management

The FDA-Abbott consent decree sounded a warning that laboratories need to independently

guarantee the quality of the test results they produce. Virtually all US laboratories have been affected

because of Abbott's dominant role in supplying materials and systems for diagnostic testing. These

laboratories were faced with the immediate need to validate new methods and provide better quality

control of routine testing. For many laboratories, the personnel resources and skills needed to accomplish

this are now very scarce due to changes in laboratory management practices and priorities during the last

decade.

The need for independent laboratory quality control is fundamental! Many laboratories have

become dependent on manufacturers QC instructions and have forgotten the hard-learned lessons of the

past:



 Independent QC begins by having control materials that are independent of the calibration

materials.

 It is also good practice to have control materials that are made by a different manufacturer than

the test system, as confirmed by FDA's recommendation for "the use of quality control materials

made by other companies to increase the assurance that these devices are performing

successfully".

 Laboratories should establish their own means and standard deviations when implementing

statistical QC procedures, rather than use bottle values provided by a manufacturer.

 Laboratories should select appropriate control rules and numbers of control measurements on the

basis of the quality required by the patients and physicians they serve and the imprecision and

inaccuracy observed for their methods during routine operation in the laboratory.



It's important to recognize that the major problem that needs to be addressed is the poor quality

management practices that have evolved in laboratories during the last decade. Laboratories aren't ready

or able to exercise an independent responsibility for the quality of their own test results.

The consent decree should be a sentinel event that alerts laboratories to the serious limitations of

current quality management practices which often lack independence from the manufacturer.

Government agencies should reflect carefully on the state of laboratory quality management and the

susceptibility of laboratory testing on a national scale to a problem from a single manufacturer.

Accreditation and professional organizations also need to examine their priorities to be sure that the basic

requirement for correct test results hasn't been down-played in their efforts to focus laboratory attention

on utilization and outcome.









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Analytical quality is not given

The consent decree clearly exposes the fallacy of the current thinking that "analytical quality is a

given." I've been worried about the complacency that has been developing in the 90s - complacency in

laboratories, complacency in professional organizations, and complacency in regulatory agencies. This

issue has been addressed with a discussion of The Myths of Quality, which is the introductory lesson in

our course on basic method validation. The situation in many laboratories has deteriorated to the point

that educational efforts are needed to re-establish basic quality management practices, which is why we've

developed training materials for Basic QC Practices, Basic Method Validation, and Basic Quality

Planning. The fundamentals of quality management need to be re-established to support high quality

laboratory testing in the new millennium.



Responsibility for quality can't be out-sourced

In today's busy laboratories, managers and analysts are quite happy when they can find ways to

reduce their activities. A common strategy is to bundle quality management activities into contracts when

purchasing new analytical systems. For example, when a laboratory buys a new instrument, many

managers negotiate to have the manufacturer not only to install the system, but also perform the method

validation studies needed to satisfy CLIA validation requirements, provide the QC instructions, control

materials, method manual descriptions, and in-service training. Manufacturers provide these services to

satisfy the "wants" of their laboratory customers.

In effect, laboratories are out-sourcing many quality management activities by purchasing

complete systems from a manufacturer. That practice is clearly dangerous. The responsibility for the

quality of the test results can never be out-sourced, even if some of the activities are performed by others.

Quality can be guaranteed only when the laboratory accepts it's responsibility and properly manages its

own testing processes!



Compliance is not enough

"Compliance" has become the management focus as we enter this new millennium. Professional

organizations tout compliance training programs. Scientific meetings feature compliance workshops.

Laboratories are required to develop compliance policies and programs and may even have compliance

officers. Avoiding fraud and staying out of jail are getting more management time and attention than

quality. Does anyone think this inspires the laboratory staff to maintain and improve quality?



Compliance management got its start from CLIA. It wasn't mandated by the CLIA regulations, but

compliance became the main response to the regulations. It is evident that management regards the

minimums stated in regulations as all that need to be done, therefore these minimums have become

maximums in laboratory practice. For example, because the regulations say it's necessary to run two

controls, then bottom-line managers never run more than two. Some people seem to accept that the

government has become very smart in the area of quality control, even though they don't think the

government has a clue about anything else.

The CLIA rules might actually provide good guidance for quality management if the regulations

were ever completely implemented. It would be nice to have the "final-final" CLIA document published.

It would be even better if the FDA were to implement the process for clearance of a manufacturer's QC

instructions, as called for in the original regulations. The continual postponement of these regulations for

validation of manufacturers' QC is a serious problem and compromises the overall effectiveness of CLIA.



Statistical QC can't be eliminated!

Statistical QC has been a fundamental quality management technique in industry since the 1930s

and in laboratories since the 1960s because it provides an independent way of monitoring the quality of a



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production process. In spite of this, there have been efforts to do away with statistical QC for laboratory

testing. These efforts have been initially pushed by industry to expand point-of-care markets, even though

statistical QC still provides the most efficient technique for monitoring the many steps in a testing

process, as well as providing a quantitative measure of operator proficiency . Expanded quality

management systems should be built on statistical QC, rather than attempting to eliminate statistical QC.



Minimum personnel standards and skills are not sufficient!

CLIA's minimum requirements for personnel have now been achieved in many laboratories.

Again, because of the compliance mentality, the net result has been a lower level of the education for

laboratory analysts, a serious reduction in training programs for clinical laboratory scientists, and, of

course, some reduction in personnel costs, which justifies everything for bottom-line managers.

Even some laboratory professionals have capitulated to this trend of "dumping down" laboratory

medicine. One paper published in 1997 concluded that "QC is costly, and laboratories frequently do not

follow established QC practices, in part, because they are complex. To improve compliance, we believe

QC practices must be simplified." In short, if the job is too complex for lower skilled people, the solution

is to simplify the job. (Let's keep this little secret to ourselves -- I don't want my plumber taking over for

my surgeon!)

HELP is available from the Internet

Laboratories that want to upgrade their skills in analytical quality management can take advantage

of Internet training materials. Our course in Basic Method Validation provides a good starting point for

understanding analytical performance. Our course in Basic QC Practices provides the fundamentals of

statistical QC for laboratory application. This course in Basic Quality Planning builds on these

fundamentals and addresses the management issues of what quality is needed and how to assure that

quality is achieved in routine operation. Important elements of this course include:



 Why of quality planning. A review of principles of Total Quality Management and existing

regulatory, accreditation, and practice guidelines provide the background for understanding why

quality planning is an essential part of laboratory quality management.

 What of quality planning. A step-by-step process provides a quantitative way for setting method

performance specifications and selecting control rules and numbers of control measurements on

the basis of the quality required for a test and the imprecision and inaccuracy observed for a

method.

 How of quality planning. Quality-planning tools, such as the chart of operating specifications,

provide an easy way to perform quality planning.

 When of quality planning. Anytime, with the quick and effective quality-planning process and tools

provided here.

 Who of quality planning. That's you! You have a professional responsibility for the quality of your

work. Our objective is to help you do your job better and manage quality in an appropriate way.



Independent analytical quality management still depends on manufacturers

Laboratories are dependent on manufacturers to provide the test systems needed in today's

laboratories. There is a shared responsibility to deliver the test results that are needed for patient care. An

important part of the laboratory's responsibility is to assure the quality of the final test results before they

are released and reported. Manufacturers can help the laboratory carry out it's responsibility by supplying

QC materials and decision-support software that will allow the laboratory to easily implement and

efficiently operate the appropriate QC procedures. However, the laboratory is still responsible to select

and implement testing processes and QC procedures that will assure the quality needed for the patients it

serves.



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Assuring Quality through Total Quality Management



Quality assurance in healthcare is a modern MYTH - a Mighty Yearning (by the public),

Testimony (by healthcare providers), and Hope (by all of us) that things will work okay if and when we

need medical care. Current quality assurance practices mainly emphasize the assessment or measurement

of quality, assuming (maybe hoping is a better word) that this interest and attention will work some magic

to make quality happen. Unfortunately, quality doesn't just happen! Production processes have to be

carefully planned, monitored, and managed to assure quality is achieved.

Even the analytical quality of laboratory tests is being assumed today, rather than assured. [1] Laboratories

assume that manufacturer's have solved all the problems with their testing processes. Programs in quality

control and quality assurance are being reduced to the minimums needed to comply with regulatory and

accreditation guidelines. Today the trend is towards new management practices, such as utilization

control, outcome assessment, and compliance. But, have we really achieved the analytical quality that is

needed? Do we even know what quality is needed for each of the tests we perform? If we haven't defined

the quality that is needed, how can we assure that quality is being achieved by our testing processes?

Here's an assessment of what needs to be done if laboratories are to guarantee the quality of the test

results they produce.



ADOPT A TQM FRAMEWORK FOR QUALITY MANAGEMENT



Total Quality Management, or TQM, has been implemented in many healthcare organizations

during the last decade. The teachings of industrial quality gurus, such as Deming and Juran, have

established new principles and processes for managing quality. Personally, I have found that Deming

provides the principles for what needs to be done and that Juran describes the methodologies or

processes for getting it done.









Juran's quality trio logy is

particularly important because it

identifies quality planning as an integral

component of TQM . We have illustrated

how quality planning can be integrated with other quality management functions using the model shown

in the accompanying figure . This TQM framework involves quality laboratory processes, quality control,

quality assessment, quality improvement, quality planning, and quality goals.









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 Quality Laboratory Processes (QLP) refers to the policies, procedures, personnel standards,

and physical resources that determine how work gets done in the laboratory. Laboratory method

manuals describe the standard operating processes for producing test results.

 Quality Control (QC) refers to procedures for monitoring the work processes, detecting

problems, and making corrections prior to delivery of products or services. Statistical process

control, or statistical quality control, is the major procedure for monitoring the analytical

performance of laboratory methods.

 Quality Assessment (QA) refers to the broader monitoring of other dimensions or

characteristics of quality. Quality involves the totality of features and characteristics that bear on

the achievement and satisfaction of customer needs. Characteristics such as turnaround time,

patient preparation, specimen acquisition, etc., are monitored through QA activities. Proficiency

testing provides an external or outside measure of analytical performance.

 Quality Improvement (QI) is aimed at determining the causes or sources of problems identified

by QC and QA. The causes of some problems can be determined by individual analysts. Other

problems may require a team of people and a team problem-solving process and team problem-

solving tools (such as the flowchart, Pareto diagram, Ishikawa cause and effect diagram, force field

analysis, etc) .

 Quality Planning (QP) is concerned with establishing and validating processes that meet

customer needs. The selection and evaluation of new methods and instruments fits here, as well as

selection and design of QC procedures.

 Quality Goals represent the requirements that must be achieved to satisfy the needs of

customers. For analytical quality, the requirement is to provide test results that are correct within

stated limits.



As shown in the figure, these components work together to provide a quality management process

that functions like a feedback loop. QLP defines the best way to get the work done. QC and QA measure

how well the work is getting done. When problems are detected, QI determines the root causes, which

can then be eliminated through QP, in this case actually re-planning the testing processes and

implementing new and better ways of doing the work (which means making changes in QLP).

This quality management framework provides a process focus for management, in contrast to the

typical control structure that is usually used to organize management activities. Through this framework,

continuous improvement is built into the management process by cycling through the different quality

functions. Customer focus is achieved by centering the framework on quality goals, customer

requirements, and quality plans. It is important to recognize that quality assurance is the outcome of this

whole quality management process, rather than being a component in the process.

Another important insight is that quality planning is a prerequisite to quality assurance!

Unfortunately, quality planning is lacking in many laboratories. The highest priority for improving

laboratory quality management is to formalize a mechanism for designing or building quality into the

testing processes. The benefits will be the ability to provide objective specifications for the precision and

accuracy needed for the analytical methods and the quality control needed to monitor the performance of

those methods.



LEARN THE PRINCIPLES OF QUALITY PLANNING FROM INDUSTRY



Industry has learned that to guarantee quality it is necessary to specify what performance is

required and how to know if that performance is achieved. An operational definition is needed to tell

people what to do and how to know if they're doing it right. For example, "answer the telephone within

three rings" tells a person what to do and how to know if they're achieving the desired performance.



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The important message here is that bench level specifications are needed to assure the quality of

routine operations. For analytical quality, these operating specifications must describe the imprecision and

inaccuracy that are allowable for the method and the control rules and number of control measurements

that are necessary for routine QC. The laboratory cannot guarantee the quality of its product unless these

operating specifications are achieved.

To establish operating specifications, industry has developed a technique called Quality Function

Deployment (or QFD). This technique is used to translate the voice of the customer into the

performance characteristics of the process AND a procedure for knowing if the process is working okay.

For a laboratory test, the necessary steps include listening to the voice of the customer to understand how

the test is used and the changes that are medically important, interpreting these customer needs to define

quality requirements, such as the allowable total error for analytical quality or the clinical decision interval

for clinical quality, and then translating these quality requirements into the operating specifications for the

method, i.e., the imprecision and inaccuracy that are allowable and the QC that is necessary.

A simple example will help illustrates the key importance of the interpretation and translation

steps. Anyone working in a laboratory has experienced the situation where a physician has ordered a stat

or emergency test, then calls for the answer before the specimen has arrived in the laboratory. The

physician is unhappy because the test result is not available and tells you to get your act together and do

the test faster. Now, if you listen to the customer and do the test faster, you know that still won't satisfy

the customer's needs. Doing the test faster doesn't solve the problem, which most likely is due to the

acquisition of the specimen and transporting it to the laboratory. Listening to the voice of the customer

doesn't mean doing what the customer says. Interpretation and translation are required to understand the

problem and fix the process.



UTILIZE AVAILABLE QUALITY-PLANNING TOOLS AND TECHNOLOGY



It is easy to see how this planning approach can be used to achieve the desired turnaround time

for a laboratory test. A quality requirement can be defined in the form of an allowable turnaround time on

the basis of discussions with the users. The steps in the process can be identified from the point of

ordering the test, acquiring the specimen, transporting the specimen, processing the specimen, analyzing a

sample, reporting the result, and receiving the result. Portions of the allowable turnaround time can then

be allocated to different steps in the process, establishing specifications for each of the steps, and allowing

those steps to be monitored to be sure the desired performance can be achieved.









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For analytical quality, the translation is more difficult. Quality-planning models are used to convert

quality requirements into specifications for the imprecision and inaccuracy that are allowable and the QC

that is necessary. These models are developed by identifying the various factors or steps in the process,

allocating or budgeting a portion of the quality requirement to that factor or step, building in a QC check

to assure the desired quality or performance is achieved for that factor or step, then balancing the budget

for the whole process and monitoring (or controlling) that budget during routine operation.

The analytical factors that must be considered include the imprecision and inaccuracy of the

method and the sensitivity of the QC procedure. Pre-analytical factors may include within-subject

biological variation, sampling variation, and specimen stability. Portions of the allowable variation or

allowable error for a test need to be allocated to these different factors, as well as a allocating a margin of

safety to quality control the process . For an analytical quality requirement in the form of an allowable

total error, the allocations consider only analytical factors - the imprecision, inaccuracy, and QC. For a

clinical decision interval requirement, a more complicated model is needed to handle both preanalytical

and analytical factors. The application of these models can be simplified by providing a graphical

presentation, e.g, a chart of operating specifications (or OPSpecs chart) can be prepared for a stated

quality requirement to show the inaccuracy that is allowable versus the imprecision that is allowable for

different control rules and numbers of control measurements.



IMPLEMENT AN EFFICIENT PROCESS



We need to make quality planning quick and easy. The time to be saved is often the time of the

managers, supervisors, and lead scientists who have the responsibilities for selecting laboratory methods

and QC procedures. The essential process, tools, and technology will be described in later lessons, and

here's a quick preview of what's to come in the next several lessons:



 Guidelines from regulatory, accreditation, and consensus standards organizations provide a

starting point for developing a quality-planning process.

 A practical quality-planning process depends on utilizing available tools and technology. Step-by-

step procedures are described that can be used both to select analytical methods (by establishing

purchase specifications for imprecision and bias) and to select QC procedures, i.e., specific control

rules and the number of control measurements needed.



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 The quality required for a test is the first step in the quality planning process. Two types of quality

requirements are practical today - allowable total error and clinical decision intervals - because

quality planning models are available to derive operating specifications.

 A graphic tool - the OPSpecs chart - is the best and most efficient way to present operating

specifications. The tool is easy to use, even though the theory is complicated. For a stated quality

requirement, the OPSpecs chart provides a visual display of the imprecision and inaccuracy that

allowable for a method and the control rules and N needed for a QC procedure.

 Internet calculators are available to support simple quality-planning applications. They provide

OPSpecs charts for use with analytical quality requirements in the form of allowable total errors.

 A computer program - QC Validator - is available to prepare OPSpecs charts for both analytical

and clinical quality requirements and to document the quality planning process. Computer support

is essential when using the clinical decision interval type of quality requirement.

 Internet resources are available to support the quality planning process, including updates on

quality requirements, interactive training tools, and detailed quality planning applications.



ACCEPT RESPONSIBILITY FOR QUALITY



The physician can give us information about how the test is used and interpreted. This

information will most likely describe the change in a test result that would cause a change the diagnosis,

treatment, or management of the patient - a medically important change. We in the laboratory have to

translate the medically important change into specifications for operating our testing processes, i.e., the

precision and accuracy that are allowable and the QC that is necessary. We shouldn't expect the physician

to understand precision, accuracy, and QC and to be able to give us those specifications directly. We have

to listen, interpret, and translate - that's our job in the laboratory.

Implementation of a quality planning process requires that laboratories build on a solid

foundation of QC, QA, and TQM, rather than replacing these management practices with new practices,

such as utilization review and outcome measurements. That's a real danger with trends in management

practices - we may throw out some old but useful practices because we don't have the time to do both the

old and the new. It's still important to improve the technical management of analytical processes to

provide a solid baseline for utilization review and outcomes assessment.





Complying with Regulations, Standards, and Practice Guidelines

Implementation of a quality planning process should be a high priority in laboratories today to

assure or guarantee the quality of laboratory tests and services. Guidelines for quality planning can be

found in government regulations, accreditation standards, and national practice standards. The

widespread application of quality planning is recommended in the latest inspection manual from the Joint

Commission for Accreditation of Healthcare Organizations (JCAHO). A focus on analytical quality, the

assessment of method performance, and the selection of appropriate quality control procedures is

provided by the Clinical Laboratory Improvement Amendments (CLIA-88). A general planning

methodology for quality control is outlined by the National Committee for Clinical Laboratory Standards

(NCCLS). Together, these guidelines provide the basis for implementing a quality planning process in

laboratories. Here's a summary of the standards, rules, and recommendations that bear on quality

planning.

JCAHO Guidelines for Improving Organizational Effectiveness

Improving Organizational Performance, or IOP, is JCAHO's latest terminology and represents an

evolution of their TQM philosophy and concepts. The 2000-2001 Comprehensive Accreditation Manual





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for Pathology and Clinical Laboratory Services [1] states that the goal of IOP is "for the laboratory to

design processes well and systematically monitor, analyze, and improve its services that affect patient

health outcomes." The following are identified as essential management activities:



 Designing processes;

 Monitoring performance through data collection;

 Analyzing current performance; and

 Improving and sustaining improved performance.



REVIEW OF SPECIFIC STANDARDS





More guidance is provided by specific standards. In the list that follows, I have also identified how

these standards fit into the total quality management framework described earlier, which is composed of

quality laboratory practices (QLP), quality control (QC), quality assessment (QA), quality improvement

(QI), quality planning (QP), and quality goals (QG). This additional information will be helpful for

assessing the relationship between IOP and TQM.



1. The leaders establish a planned, systematic, organization-wide approach to process design and

performance measurement, analysis, and improvement. [QP to establish QLP]

2. The activities are planned in a collaborative and interdisciplinary manner. [QP across functions or

departments to establish QLP]

3. New or modified processes are designed well. [QP or re-planning to establish QLP]

4. Performance expectations are established for new and modified processes. [QG or requirements

for QLP]

5. The performance of new and modified processes is measured. [QA of QLP]

6. Data are collected to monitor the stability of existing processes, identify opportunities for

improvement, identify changes that will lead to improvement, and sustain improvement. [QC and

QA of QLP]

7. The organization collects data to monitor its performance. [QA]

8. The organization collects data to monitor the performance of processes that involve risks or may

result in sentinel events. [QA of QLP]

9. The organization collects data to monitor performance in areas targeted for further study. [QA]

10. The organization collects data to monitor improvements in performance. [Q A]

11. Data are systematically aggregated and analyzed on an ongoing basis. [QA]

12. Appropriate statistical techniques are used to analyze and display data. [QA and QI]

13. The organization compares it performance over time and with other sources of information. [QA

and QI]

14. Undesirable patterns or trends in performance and sentinel events are intensively analyzed. [QA

and QI]

15. The organization identifies changes that will lead to improved performance and reduce the risk of

sentinel events. [QI and QP or re-planning]

16. Improved performance is achieved and sustained. [QP, QLP, QC, QA, QI]



IOP VERSUS TQM





From the review of these standards and comparison to our TQM framework, the relationship

between IOP and TQM is described in the accompanying figure, where theemphasis on each component





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is shown by the darkness of each Q. Quality Planning is strongly recommended by the JCAHO standards

on the "design" of processes. Quality Laboratory Processes is pervasive throughout the standards and

JCAHO's frequent reference to "processes." Quality Control and Quality Assessment provide the

measure and monitor aspects that are mentioned in a large number of the standards. Quality

Improvement is the focus of the whole IOP approach and is specifically mentioned in several standards.

Quality goals are found in the standard that recommends establishing performance expectations for new

and modified processes.

In summary, JCAHO provides a broad recommendation for quality planning that applies to all

laboratory processes. Given that the fundamental laboratory process is to perform tests, carefully planned

testing processes should be the heart of quality laboratory processes. Performing tests is the main event in

the laboratory. Other activities are important in support of testing and patient services, but none of them

will matter if the laboratory can't produce correct test results.



CLIA RULES AND REGULATIONS





The strongest guidelines for analytical quality management are found in the CLIA-88 rules and

regulations. CLIA provides a broad focus on quality management of the "total testing process", which

includes the pre-analytical, analytical, and post-analytical phases. Rules for the analytical phase provide

very detailed guidelines for method validation and quality control, i.e., for establishing valid testing

processes and assuring the quality of the test results on an ongoing basis. CLIA also establishes specific

criteria for acceptable performance in proficiency testing surveys, thereby defining the minimum quality

standards that must be achieved by US laboratories.





REVIEW OF SPECIFIC RULES





Subpart K of the CLIA rules begins with a statement that "the laboratory must establish and

follow written QC procedures for monitoring and evaluating the quality of the analytical testing process

of each method to assure the accuracy and reliability of patient test results and reports." This is followed

by detailed rules that can be ascribed to different components in our TQM framework, as follows:



 Quality Goals are defined by the proficiency testing criteria for acceptable performance which are

given for immunology [493.927], routine chemistry [493.931], endocrinology [493.933], toxicology

[493.937], and hematology [493.941].

 Quality Laboratory Processes are defined through the facilities [493.1204], test methods,

equipment, instrumentation, reagents, materials, and supplies [493.1205], and the procedure

manual [493.1211], as well as detailed personnel standards [Subpart M].

 Quality Control is described by control procedures [493.1218], remedial actions [493.1219], and

quality control records [493.1221].

 Quality Assessment requires the establishment and verification of method performance

specifications [493.1213], calibration verification procedures [493.1217], and participation in

external proficiency testing [Subpart H].

 Quality Improvement is not mentioned.

 Quality planning for analytical methods is implied through the requirement for verification of QC.

o "For each method that is developed in-house, is a modification of the manufacturer's test

procedure, or is a method that has not been cleared by FDA as meeting the CLIA





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requirements for general quality control, the laboratory must evaluate the instrument and

reagent stability and operator variance in determining the number, type, and frequency of

testing calibration or control materials and establish criteria for acceptability used to

monitor test performance during a run of patient specimen(s)."

The accompanying figure summarizes the relationship of the CLIA rules to the TQM

framework. CLIA provides a strong emphasis on Quality Laboratory Processes,

particularly analytical testing processes, through the detailed rules that govern facilities,

equipment and materials, procedure manual, and personnel. The rules for Quality Control

and Quality Assessment are also much more detailed, as applied to analytical methods.

There is no mention of Quality Improvement, but Quality Planning for analytical

processes is implied in the rules for establishing QC procedures. Specific Quality Goals are

defined for approximately 80 different tests that include some from immunology,

hematology, coagulation, chemistry, endocrinology, and toxicology.



NCCLS QC PRACTICE GUIDELINES





Given that JCAHO provides a strong recommendation for Quality Planning and that CLIA

specifies Quality Goals and requires that QC procedures account for the laboratory's own variability,

laboratories must carefully plan their analytical testing processes. NCCLS provides the methodology for

accomplishing this in its 1999 QC practice guideline which describes a step-by-step process for selecting

a statistical QC procedures on the basis of the quality required by a test and the performance observed

for a method.

Review of NCCLS QC planning methodology









The NCCLS methodology appears in section 5 of the C24-A2

document. The steps recommended are shown in the figure

and explained below:







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1. Define the quality requirement. A quality requirement may be defined in terms of an allowable

total analytical error, such as often provided by proficiency testing criteria for acceptable

performance. The allowable analytical error is the magnitude of analytical error that if exceeded

would cause a test result to be of unacceptable quality [4]. It encompasses both random and

systematic errors, i.e., both method imprecision and bias. There also are recommendations for

medically important changes in test results that similarly include both method imprecision and

bias, as well as subject biological variation. Biologic variation itself provides another basis for

defining the allowable imprecision and allowable bias for a test . Clinical treatment models can also

be a source of information about the analytical quality required to assure that test results are

medically useful.

2. Determine method performance. The performance characteristics of an analytical process that

are critical for the proper planning of QC procedures are imprecision and bias. Estimates of these

parameters should represent the stable performance of an analytical process. In addition to

imprecision and bias, it would be useful to have information about unstable performance, such as

the expected type, magnitude, and frequency of analytical errors, but this information is not

generally available.

3. Identify candidate statistical QC strategies. A quality control strategy is defined by the control

materials used, the number of control samples analyzed, the location of these control samples in

an analytical run, the quality control rules applied to the control sample measurements, and the

time when the quality control rules are evaluated. The appropriateness of the QC strategy depends

on the quality required, as well as the expected instability of the analytical method (e.g., type,

magnitude, and frequency of errors). Several QC strategies may be defined and evaluated.

4. Predict QC performance. The performance of a quality control strategy can be predicted from

probability calculations or from computer simulation studies. The most direct indicator of the

performance of a quality control procedure is the expected number of unacceptable patient test

results that are produced (or reported) when an out-of-control error condition exists [8]. This will

depend on the type and magnitude of the out-of-control error condition, when the error condition

occurs and how long it lasts, which in turn depends on how frequently quality control testing

occurs and the probability that the quality control rules detect the error condition. These

predictions generally assume the shape of the error distribution is gaussian, which may not account

for some periodic and irregular effects observed with real laboratory systems, therefore the

complexity of the prediction model needs to match the complexity of the potential error sources

of the method and system.







5. Set goals for QC performance. Quality control performance goals set desirable targets for

quality control performance. The goal will depend on the chosen quality control performance

measure. Thus, one goal could be specified as a maximum allowable number of unacceptable

results due to an out-of-control error condition, or a maximum allowable probability or reporting

unacceptable results (maximum defect rate), or a minimum acceptable probability of detecting an

out-of-control error condition. Another goal could specify a maximum allowable probability of

false rejections.







6. Select appropriate QC. When more than one quality control strategy meets the quality control

performance goals, other characteristics such as cost and ease of implementation can be used to

select the best approach. Practical approaches for selecting appropriate QC procedures have been



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described based on power function graphs, critical-error graphs, and charts of operating

specifications. Illustrative applications of QC planning are available in the literature to provide

guidance in selecting appropriate QC strategies.









Other

guidelines

The College

of American

Pathologists (CAP)

also provides

detailed guidelines

that focus on

laboratory testing processes. CAP has "deemed status," meaning the CAP guidelines are at least as

stringent as the CLIA rules. Likewise, the Commission of Office Laboratory Accreditation (COLA) has

deemed status and it's guidelines must comply with the CLIA rules. Therefore, the CLIA rules, JACHO

IOP standards, and NCCLS QC planning process provide the a comprehensive synthesis of guidelines

for managing quality in healthcare laboratories. These guidelines are also seen to be consistent with the

principles from TQM.





DIFFICULTIES WITH QUALITY REQUIREMENTS



These are not intended to be trick questions! Quality-planning begins with the definition of quality

requirements. The selection of methods and QC procedures should follow in a logical manner. Regardless

of your background and experience, my guess is that you will find it difficult to answer these questions. A

major reason is that quality requirements themselves are confusing and therefore difficult to define.





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Lack of consistent concepts and terms

Part of the difficulty comes from the different types of quality goals, criteria for acceptable

performance, and performance specifications that are being recommended. From the beginning, we've

been plagued by conflicting concepts and terms. The first recommendations for establishing standards of

quality were published by Tonks in 1963 and presented in the form of allowable total errors. In 1968,

Barnett described medically important changes in test results and related them to allowable SDs or CVs

for laboratory methods. In 1970, Cot love et al utilized within-subject biological variation to derive

standards for allowable SDs . The quality standards for cholesterol demonstrate the difficulties with

inconsistent concepts and terms. Some of these are analytical outcome criteria, such as the CLIA

allowable total error in proficiency testing; some are clinical outcome criteria, such as the NCEP

medically important change for test interpretation. Others are method performance specifications, such as

allowable CV and allowable bias defined by NCEP, and still others are quality goals, such as the

recommendations for allowable imprecision and inaccuracy by the European working group.

Trying to understand and compare these different terms is like trying to compare apples, oranges,

grapefruit, and bananas - they're all fruit, but they're different kinds of fruit. The allowable total error

encompasses both the allowable CV and SD for method imprecision and allowable bias for method

inaccuracy. A medically important change encompasses preanalytical factors, such as the within-subject

biological variation, as well as analytical factors such as imprecision and inaccuracy. You will find that

certain people, organizations, or agencies have a particular taste. Each selects what they like, which leaves

the laboratory with a fruit-bowl of choices, rather than a coherent system of recommendations that guide

the management of testing processes.



Multiple factors affecting test variability

To make sense of the different concepts and terms, you need to understand how the variability of

a test result depends on pre-analytic and analytic factors. The accompanying figure illustrates the

cholesterol situation for a patient whose true homeostatic set point is 200 mg/dL, i.e., this is the patient

mean value if the patient were sampled repeatedly over a long period of time. The patient's own biologic

variation is shown as BV, which in this case is illustrated as a variation equivalent to approximately 1*BV

or 200*6.5% or 13 mg/dL. Note that if the test were to be repeated say a week later, the patient's true

value will most likely change due to the biologic variation - and could be considerably higher or lower

than illustrated here.

Method bias, or inaccuracy, is shown by the difference between the true test value and the mean

that would be observed if the patient's sample were measured several times. In this illustration, the bias is

approximately 3% of the true test value, which would add a systematic error of about 6 or 7 mg/d to the

true test value of 213 mg/dL, giving an observed mean of about 220 mg/dL. The distribution of repeated

measurements is shown by the histogram and represents the effect of method imprecision. Method

imprecision would add a random error of about another 12 or 13 mg/dL (2*3%*213 mg/dL), thus values

as high as 233 mg/dL can be expected for a single measurement made on this patient.

The total error describes the net effect of method inaccuracy and imprecision. It is commonly

estimated as bias + 2*CV. Note that the total error does not include the biologic variation - it only

considers analytical components of error. However, biologic variation will be an important component

when interpreting the cholesterol result of an individual patient. The NCEP patient treatment guidelines

define a clinical decision interval from 200 to 240 mg/dL, which is a medically important change that

includes both pre-analytic and analytic factors. Biologic variation is a large pre-analytical factor for

cholesterol - 6.5% compared to NCEP's recommended method CV of 3.0%.









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An unstated assumption

From my perspective, most of the recommendations are actually lemons because they provide

little practical guidance for the laboratory. They don't work because they are recommendations only for

the stable performance of a method, i.e., they assume everything works perfectly, no problems will occur,

therefore no QC is needed. If this assumption of stable performance is not correct, then it follows that

these recommendations are not correct for real laboratories where problems really do occur.

The analytical quality achieved in the daily operation of a testing process will depend on the both

the stable performance of the measurement procedure (i.e., its observed imprecision and inaccuracy) and

the capability of the quality control procedure to detect unstable method performance (i.e., changes in

imprecision and inaccuracy). Specifications for stable imprecision and inaccuracy are incomplete and

inadequate if they fail to consider QC. If you really believe in this assumption of stable performance, it

should follow that you don't do any QC! If you perform QC, that's evidence you expect some method

problems, therefore, the assumption of perfect method stability is wrong.



Misunderstanding performance as quality

Imprecision and inaccuracy are performance characteristics, not quality requirements.

Performance certainly contributes to quality, but it's not the same thing. A given level of quality can be

achieved by different combinations of imprecision and inaccuracy. Therefore, setting separate goals for

imprecision and inaccuracy in the form of allowable CVs and allowable biases might conceal, rather than

reveal, the total error that will be experienced by the user and consumer. Calculations can be performed

to combine the maximum allowable bias with a multiple of the maximum allowable imprecision to

describe the expected total error, such as done by NCEP for lipid tests, but these estimates of overall

quality again are flawed because they assume stable performance and don't allow for the performance of

the QC procedure.



Relevance to

customers









One of the things TQM teaches is that quality is related to customer needs. To determine

customer needs for laboratory tests requires communication with physicians and nurses, none of

whom really think in terms of precision and accuracy. The cartoon here characterizes the reaction of

customers to a laboratory scientist's description of quality and performance. They hear our technical





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words, but those words don't mean anything to them. For customer communication to work, we have to

listen to their words, understand their needs, and translate those needs into our technical terms. This is

the process of Quality Function Deployment where the key ideas are listening to customers and

translating customer needs into process specifications.

Our customers are concerned with the total change that might occur in a test result, not

components of errors such as imprecision and inaccuracy. Furthermore, their application of test results is

related to certain critical changes from reference values, decision limits, or previous test results. Our

customers think about medically important changes in test results; they don't think about test results with

reference to imprecision and inaccuracy. The information available from our customers and relevant to

their use of results from laboratory testing processes is in the form of medically important changes and

total errors, not specifications for allowable imprecision and allowable inaccuracy.



Applicability for laboratory use

The practical purposes of these recommendations for quality goals, criteria for acceptable

performance, and performance specifications are to help the laboratory establish, manage, and monitor a

testing process to assure the analytical quality of the test results. That means these recommendations

should be useful for characterizing the clinical needs of the test, setting purchase specifications for the

method, evaluating method performance, establishing internal quality control, and monitoring method

performance via external quality assessment or proficiency testing. A source of our difficulties is that

different types or formats of quality requirements are needed at different times and for different purposes

in the overall process of managing the analytical quality of laboratory tests. Each type of recommendation

has its place in this system, but the system itself is not well understood.

A SYSTEM OF QUALITY STANDARDS

The debate about the "best" type of quality requirement has overshadowed the use and

application of quality requirements. Finally, in 1999 at an international conference in Stockholm, a

recommendation was made to recognize a system of quality standards. This system includes different

sources of information and different formats for requirements, such as the allowable total error (analytical

outcome criterion), the clinical decision interval (clinical outcome criterion), or the maximum allowable

standard deviation and the maximum allowable bias (analytical performance criteria).

The accompanying figure shows my view of the relationships between these different sources of

information, different types of quality requirements, and the operating specifications needed for

managing routine testing processes. Starting at the top of the figure, medically important changes in test

results can be defined by standard treatment guidelines (clinical pathways, clinical practice guidelines, etc.)

to establish clinical outcome criteria (or decision intervals, Dint). Such clinical criteria can be converted to

laboratory operating specifications for imprecision (smeas), inaccuracy (biasmeas), and QC (control rules, N)

by a clinical quality-planning model that takes into account pre-analytical factors, such as individual or

within-subject biologic variation.

The left side of the figure shows how performance criteria for imprecision and inaccuracy can be

defined as separate analytical goals for the maximum imprecision and bias that would be allowable for the

stable performance of the method. Specifications for maximum imprecision and bias can be derived on

the basis of within-subject biological variation . The maximum allowable bias can also be derived from

diagnostic classification models. Laboratories can utilize these separate performance criteria by relating

observed method performance to the maximum allowable value, calculating the critical-size error that

needs to be detected to maintain satisfactory performance, and then selecting appropriate QC procedures

by use of power function graphs.

The right side of the figure shows how proficiency testing criteria define analytical outcome

criteria in the form of allowable total errors (TEa), which can be translated into operating specifications



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(smeas, biasmeas, control rules, N) via an analytical quality-planning model . Note that the allowable total

error can also be set on the basis of total biologic goals that are population based or individual based ,

therefore the extensive data-bank of individual biologic variation can be utilized in this situation The

bottom line is operating specifications. The laboratory must know the imprecision and inaccuracy that are

allowable for the method and the control rules and number of control measurements that are necessary to

monitor and assure the quality of the testing process. Thus, all these different forms of quality standards

have some use in the context of a system for analytical quality management. However, until this system is

recognized, understood, and applied, the different recommendations in the literature will continue to be

incoherent, rather than useful and practical for analytical quality management. In the absence of defined

quality requirements, manufacturers set performance specifications on the basis of "state of the art";

laboratories apply arbitrary control, not quality control.



Clinical quality requirements

Practical information can be provided in the form of a medically important change, medically

significant change, or clinical decision limit, which are the commonly used terms for this type of quality

requirement. One source of information about medically important changes in test values is a paper by

Skendzel, Barnett, and Platt. Note that the important information is found in this paper, which provides a

summary of physicians' opinions of a significant change in test results. This paper is sometimes criticized

for the rather large values recommended for medically useful CVs, which appear in Table 2 and were

derived without accounting for within-subject biological variation. When Fraser's figures for within-

subject biological variation are used in a clinical quality-planning model that accounts for biological

variation, the allowable CVs are much smaller. The original recommendations for allowable CVs were

limited by an over-simplified quality-planning model that attributed the total variation to analytical

variation, rather than first deducting the known biological variation.

One major advantage of this type of quality requirement is that information is directly available

from the customers, either through their description of how they use and interpret a laboratory test,

through clinical pathways that detail the expected use and interpretation of tests, or through audits of

clinical practices. When this information is properly translated to operating specification via a quality-

planning model that accounts for pre-analytical factors, it provides a useful and valid approach for

defining and managing the quality of the testing process.



Analytical quality requirements

The most useful form for these requirements is a statement of an allowable total error that

encompasses both imprecision and inaccuracy. This corresponds to the industrial "tolerance

specification" for process production that considers both the centering of the process on a target value

and the distribution of individual products around that target. The most common sources of these type of

requirements are the proficiency testing or external quality assessment programs that specify acceptability

limits in the form of a target value plus/minus certain tolerances. In the US, CLIA defines such limits for

approximately 80 different tests. In other countries, such as Australia and Canada, the lists and criteria

may be even more extensive.

These PT limits define minimum levels of quality that must be achieved, therefore, it is always

important to plan testing processes to assure PT criteria are achieved in routine operation. This can be

accomplished by using an analytical quality-planning model that translates these requirements into the

imprecision and inaccuracy that are allowable and the QC that is necessary.



Operating specifications

Both clinical and analytical quality requirements in the forms of decision intervals and allowable

total errors can be translated into the practical specifications that are needed to manage routine



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operations. These

operating specifications

consist of the

imprecision and

inaccuracy that are

allowable for the

method and the QC

that is necessary to detect

unstable performance,

i.e., detect analytical

problems and errors

that occur with the method.

The exact values for the

CV, bias, control rules and N are interdependent, permitting many different combinations that will still

assure the desired quality will be achieved. The many possible combinations can be shown graphically by

OPSpecs charts to help analysts and managers determine how to properly manage the analytical quality of

a testing process.



Summaries of available recommendations

For initial guidance, see the following summaries of recommendations for different types of quality

requirements:



Analytical quality requirements in the form of allowable total errors are provided by the

proficiency testing criteria for acceptable performance that have been defined by the US Clinical

Laboratory Improvement Amendments (CLIA).. Similar information is often available from the external

quality assessment programs in other countries.



European recommendations for biologic goals for imprecision and inacuracy, as well as calculated

allowable total error criteria, are based on individual biologic variability. An extensive databank is available

that summarizes the results of studies on biologic variation. These results can be used to calculate biologic

goals for imprecision and inaccuracy, which in turn can be used to calculate total error criteria.



Some information on medically important changes in test results is also available and provides a

starting point for defining clinical decision interval criteria. Note, however, that clinical quality

requirements in this form require a more complicated quality-planning model to properly consider both

pre-analytical and analytical components of errors. Answers to self-assessment exercise.



For our cholesterol example, where the NCEP clinical quality requirement is 20% and within-

subject biological variation is 6.5%, a clinical OPSpecs chart shows that if method bias were zero, then

method CVs of 2.7% or less are needed (see the x-intercept of the bold line) if common QC procedures

with N=2 are to be used and the false rejection probabilities are to be kept below 0.05 (i.e., a false

rejection rate lower than 5%). A method that satisfies the NCEP 3.0% precision and 3.0% accuracy

specifications will not assure the desired clinical quality, as can be seen by plotting an operating point of

x=3% and y=3%, which exceeds the allowable limits of imprecision and inaccuracy for all the QC

procedures with N=2.









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Given the CLIA

analytical quality

requirement of 10% for

cholesterol, an analytical

OPSpecs chart shows that if method bias were zero, then method CVs of 2.2% or less are needed (see

the x-intercept of the boldline) if common QC procedures with N=2 are to be used and the false

rejection rate is to be kept at 5% or less. Note that this OPSpecs chart also shows the operating point for

a method that satisfies the NCEP 3% imprecision and 3% inaccuracy specifications and that this

performance would be judged acceptable in a method evaluation study that used a bias + 2s criterion for

stable performance (as shown by the line above the operating point). However, such a method cannot be

adequately controlled by commonly used QC procedures with Ns of 2. Note that these operating

specifications are more demanding that the European quality goals for imprecision of 2.7% and

inaccuracy of 4.1%. If bias were as large as 4.1%, then the method CV would need to be as low as 1.0 to

1.5% (as can be seen by finding 4.1% on the y-axis of the OPSpecs chart, drawing a horizontal line

across, dropping a vertical line from the point of intersection with the operating limits, and reading the

allowable imprecision from the x-axis).



In summary, a method CV from 2.0 to 2.5% would generally be required if method bias were zero

and simple control procedures with N's of 2 were to be applied. If a method with this performance were

purchased, the laboratory could then implement a single-rule procedure using the 12.5s rule with N=2 or a

multi-rule procedure such as 13s/22s/R4s with N=2.









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5. Formulating a Total Quality Control Strategy

Optimal management of analytical quality depends on individualizing the QC procedures for each

test and method in the laboratory. The quality-planning process provides the methodology for (a) setting

method performance specifications that are appropriate for the analytical or clinical quality required for a

test and (b) selecting statistical QC procedures appropriate for the actual imprecision and inaccuracy

observed for methods in routine operation in a laboratory. Ideally, the QC procedure should provide at

least a 90% chance of rejecting an analytical run having medically important errors. At the same time, the

QC procedure should have less than a 5% chance of falsely rejecting a run that contains only the random

errors due to the inherent (stable) imprecision of the method. And, for practicality and low cost, the QC

procedure should require only 2 to 6 control measurements per run - the lower the better.

REVIEW OF CLIA REGULATIONS

US government regulations (CLIA-88) define a set of standards for quality control that include

method performance specifications, statistical quality control, preventive maintenance, instrument

function checks, and method performance tests .These CLIA rules may be viewed as separate

requirements for individual components of Quality Control (QC), or as a requirement for developing a

Total Quality Control (TQC) strategy that incorporates these components in a manner appropriate for

controlling individual testing processes. The latter view would seem to be more desirable for assuring the

quality of laboratory testing because of the need to individualize the QC designs for the many different

analytical methods for performing those tests.

The responsibility for establishing a TQC strategy initially belongs to the manufacturers of

medical testing systems, devices, or kits. When a manufacturer's QC instructions have by cleared by FDA

as meeting CLIA requirements for quality control, the CLIA rules require that a laboratory does the

following:

"demonstrate that, prior to reporting patient test results, it can obtain the performance

specifications for accuracy, precision, and reportable range of patient test results, comparable to those

established by the manufacturer, "perform maintenance as defined by the manufacturer and with at least

the frequency specified by the manufacturer,", "perform function checks as defined by the manufacturer

and with at least the frequency specified by the manufacturer", "follow the manufacturer's instructions for

calibration and calibration verification procedures using calibration materials specified by the

manufacturer", and "follow the manufacturer's instructions for control procedures". For a test method

whose QC instructions have not been cleared by the FDA, the laboratory itself assumes responsibility for

formulating an appropriate strategy for quality control that includes these same components. Note that as

of the year 2000, a QC clearance process has NOT yet been implemented in accordance with the CLIA

regulations, thus the laboratory is primarily responsible for selecting appropriate QC procedures and for

implementing appropriate TQC strategies.

GENERAL TQC GUIDELINES

The starting point for formulating a TQC strategy is the quality-planning process and the error

detection available from the selected statistical QC procedure. Testing processes will be classified into one

of three categories: high error detection when a QC procedure can be selected from an OPSpecs chart

with 90% AQA; moderate error detection when a QC procedure is selected from an OPSpecs chart with

50% AQA; low error detection when 50% AQA is not obtainable, in which case a maximum QC

procedure should be defined as the default selection. The recommendations for maximum QC selections

here are to use a multirule procedure such as 13s/22s/R4s/41s/8x with the maximum of N=4 (for 2 control

materials) and 13s/2of32s/R4s/31s/6x with N=6 (for 3 control materials).

The general TQC strategies for these three classes are shown in the accompanying table, where SQC

refers to statistical QC, Other QC includes preventive maintenance (PM), instrument function checks





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(FC), performance validation tests (PV), and patient data quality control (PD). QI means quality

improvement and refers primarily to improving the precision, accuracy, and stability of the measurement

procedure.



 HI-Ped strategy. When SQC is able to provide high error detection, then the TQC strategy is to

depend primarily on SQC and perform the minimum requirements for other QC components.

 MOD-Ped strategy. When SQC provides moderate error detection, the TQC strategy is to

balance the emphasis on SQC, Other QC, and QI.

 LOW-Ped strategy. When SQC provides low error detection, the TQC strategy cannot rely on

SQC, but must emphasize Other QC and QI.









Step-by-step

guidelines

The flowchart

shows a more detailed

process for

developing a TQC

strategy for an

individual method.

When SQC provides high error detection (90% AQA), the emphasis is on minimizing the costs of

statistical and non-statistical QC. When SQC provides moderate error detection (at least 50% AQA), then



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the emphasis is on maximizing statistical and non-statistical components, as well as improving

measurement performance. If SQC provide low error detection (less than 50% AQA), the efforts also

include optimizing QC for process stability, improving the skills of the analysts, and adding patient data

QC. In all cases, the final step is to document the QC system.









HI-Ped Strategy



 Minimize the cost of

statistical QC. Use as few control measurements as needed. Reduce N to a minimum of 2

whenever possible. Use single-rule rather than multi-rule procedures. Use control limits as wide as

3.5s when possible to minimize false rejections. Aim for 1% or lower false rejection. Increase run

length to maximize test yield, i.e., the ratio of patient samples to control and calibration samples.

 Minimize cost of non-statistical QC. Recognize the limitations of control materials and their

matrices when minimizing non-statistical QC. Weigh the clinical needs and risks carefully. Then

identify the minimum frequency of system function checks, performance validation tests, and

preventive maintenance, as required by regulations, manufacturer's instructions, and good

laboratory practice.

 Document the TQC strategy. The last step for any of the TQC strategies is documentation,

including the QC acceptability criteria (control rules, N) for assessing the control status of an

analytical run. Document the expected rejection characteristics of the statistical QC procedure.

Establish the schedule for performing non-statistical QC checks and document the procedures for

performing those checks.



MOD-Ped and LO-Ped Strategies



 Maximize error detection. Increase N from a minimum of 2 up to at least 4 control

measurements per run when using two control materials and increase N from 3 to 6 when using

three control materials. Increase runs length as a way of increasing N, being careful to satisfy the

turnaround time requirements for the test. Narrow the control limits and tolerate higher false

rejections, up to 6-7% for the N=6 procedures. Change from single-rule to multirule QC

procedures. Use look-back rules to effectively increase N by inspecting control data from the





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previous run. Implement multi-stage QC procedures that have startup designs with higher N's

and/or more sensitive control rules to maximize error detection, then switch to a monitor design

having lower N and/or less sensitive control rules that minimize false rejections. Switch back and

forth between the startup and monitor designs as necessary.

 Maximize non-statistical QC. Perform preventive maintenance, calibration, instrument checks,

and performance verification tests that are required by CLIA, recommended by the manufacturer,

and appropriate for the susceptibility of the method and the clinical application of the test. Adopt

a more aggressive schedule to minimize problems.

 Improve method performance. Reduce analytical bias by selecting appropriate standards,

calibrating properly, and by selecting proper comparison groups in proficiency testing surveys.

Reduce imprecision by identifying and minimizing the major component of variance, standardizing

operator techniques, and mechanizing manual steps in the process. Reduce frequency of errors by

identifying and eliminating sources of problems, increasing the preventive maintenance schedule,

increasing function checks and performance verification tests, reducing operator variables, and

increasing operating training and expertise. When necessary, change measurement procedures or

analytic systems to obtain better accuracy, precision, and stability.



Additional steps for LO-Ped strategy



 Optimize QC for process stability. Document the frequency of errors by careful study of the

analytical process. In general, a 50% error detection rate will be satisfactory for stable processes

that have <2% frequency of errors and even a 25% detection rate may be sufficient for extremely

stable processes that have <1% frequency of errors.

 Deploy skilled analysts. Assign highly skilled analysts to testing processes that are problematic

and difficult to control. Provide thorough in-service training. Increase technical skills and

experience. Improve problem-solving capabilities. Improve statistical skills for method validation

and quality control. Reduce the rotation schedule for personnel to maintain operator experience

and continuity.

 Add patient data QC. Perform between system comparisons on patient samples. Check patient

data with consistency algorithms, such as delta checks, anion gap, etc. Utilize









 Population statistics, such as





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mean of normal, Bull's algorithm, etc. Perform clinical correlations to check test results with

patient diagnosis and condition.



Cost of quality control vs cost of quality improvement

The costs of quality control are ongoing and never-ending, month after month, year after year. The

accompanying figure illustrates the relative costs for different TQC strategies.



 HI-Ped TQC costs. When 90% detection of medically important errors can be achieved with

statistical QC, cost of ongoing quality management is lowest. The better the analytical

performance, the lower number of control measurements needed, the wider the control limits, the

lower the false rejection rate, and the less non-statistical QC is needed. This demonstrates

Deming's principle that improved quality leads to lower costs.

 MOD-Ped TQC costs. When moderate error detection is achieved - greater than 50% detection

of medically important errors, all the costs go up, including the cost of statistical QC. There is a

need for more control measurements, more preventive maintenance, more frequent instrument

checks, etc., which means more time and effort by laboratory personnel.

 LO-Ped TQC costs. When error detection is low - less than 50% detection of medically

important errors, costs become even greater. There is a need for more training, more operator

experience, and more review and correlation of patient data.



At some point, the costs for MOD-Ped TQC and LO-Ped TQC should justify efforts to improve

the performance of an analytical method. Begin your quality improvement efforts by reducing the

inaccuracy or bias of the method. Next try to reduce the imprecision or CV of the method. Try to gain

enough improvement to change the TQC strategy, moving from LO-Ped to MOD-Ped to HI-Ped

strategies. If this is not successful, consider replacing the method with one that has better analytical

performance.



6. Adopting the OPSpecs Chart as Your Planning Tool

A step-by-step quality-planning process has been described drawing on the NCCLS QC practice

guidelines. The first step of this process requires definition of the quality required for a test, which can be

stated in several different formats - all of which are important in a system of quality standards. The

bottom line in the laboratory is knowledge of the operating specifications, which are the imprecision and

inaccuracy allowable for a method and the QC needed to monitor method performance and assure the

desired quality is achieved.

Operating specifications are different from quality goals or quality standards because they consider

QC as an integral part for monitoring the instability of a method, whereas most quality standards

generally assume that method performance is stable and therefore don't consider QC. In the lingo of

today, this is a paradigm shift - a new perspective or a different view of the situation. QC needs to be

designed into the testing process, in addition to analytical imprecision and inaccuracy.

This new perspective provides a more complete and comprehensive view of what is necessary to

manage the analytical quality of a laboratory testing process. It's also more complicated because of the

interdependence of imprecision, inaccuracy, and QC. The better the imprecision and inaccuracy, the

easier it is to QC the process. The worse the imprecision or inaccuracy, the more difficult it becomes to

adequately QC the process. The interactions of these three critical factors must be considered.

The best tool for showing these interactions is the chart of operating specifications, or OPSpecs

chart. The concept of the OPSpecs chart has been introduced, using the idea of a map that will help you

get to solid ground. Example applications of OPSpecs charts have been illustrated for a cholesterol test

that has well-defined standards of quality in US national regulations (CLIA-88) and clinical practice



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guidelines (NCEP). Now is the time to learn the details of how to use OPSpecs charts. For the theory of

why it works, see references 1 and 2.

How to read an OPSpecs Chart

An OPSpecs chart contains information about the type of quality requirement, actual quality

desired, the imprecision and inaccuracy allowable for different QC procedures, details about the control

rules and number of control measurements, and information about the error detection and false rejection

characteristics of the QC procedures. See the figure on the "ABCs of reading an OPSpecs Chart" for

guidance on finding all this information.









A. Start with the title that appears at

the top of the chart. The title identifies the following:



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 Type of quality requirement, TEa for an allowable total error or Dint for a clinical decision

interval. In this example, the OPSpecs chart was prepared for an analytical total error requirement.

To prepare a chart for a clinical decision interval requirement, you will need an electronic

spreadsheet or computer program, such as QC Validator.

 Desired quality, in % of a medically important decision level, e.g., 10% for cholesterol in this

example.



 Error detection, in %, such as 90% in this example, which is the same as a probability of error

detection (Ped) of 0.90. The AQA(SE) stands for Analytical Quality Assurance for Systematic

Error. Charts are also available for 50% AQA(SE). With the aid of the QC Validator computer

program, charts c

 B. Look at the axes of the chart.The y-axis shows allowable inaccuracy, or the method bias in units

of %, i.e., relative to the medical decision level of interest.



 The x-axis shows allowable imprecision, or the standard deviation (s) in units of %, which is the

same as the coefficient of variation or CV. an also be prepared for 25% AQA and also for random

error (RE).









C. Inspect the lines that describe the limits of allowable bias and allowable imprecision, i.e., define the

solid ground.







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 The partially hidden line shows the limits of stable performance

that correspond to a total error criterion of bias + 2s, which is

commonly used as a criterion for acceptable performance in the

initial evaluation or validation of a method. Remember that this

criterion assumes stable method performance and does not consider the need for QC - that's why this

line is the highest.

 The other lines correspond to certain control rules and numbers of control measurements, which are

identified in the key at the right side of the chart. Follow the arrows across to find the details of

individual QC procedures. Note that the order of the lines - top to bottom on the chart - corresponds

to the order of the lines top to bottom in the key at the right.



D. Match the lines in the key of the graph to get the details about each QC procedure.



 The dots and dashes in the lines on the graph match the dots and dashes in the lines in the key.

For example, the solid line on the graph corresponds to the solid line in the key. Often it is easier

to match up the lines based on their top-to-bottom order on the graph and in the key.

 The control rule or rules are shown in the first column of the key. The abbreviations are of the

form AL, where A is the symbol for a rule or the number of control measurements and L is the

control limit. For example, the top line is for a 12s rule and indicates a run is to be rejected when 1

control measurement exceeds a 2s control limit. This corresponds to a Levey-Jennings control

chart having control limits set as the mean plus/minus 2 standard deviations.

 The probability of false rejection, Pfr, is listed in the second column. Ideally, this figure should be

less than 0.05, or 5%, to minimize the false alarms from the QC procedure. Note that for the 1 2s

rule with N=2, the probability for false rejection (Pfr) is 0.10, which means a 10% chance of falsely

rejecting each run. The use of 2s control limits with N=2 should be generally discouraged because







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they cause a 10% waste in the production of a testing process, even when the process is working

perfectly. It gets even worse for higher Ns (14% for N=3, 18% for N=4).

 N is the total number of control measurements per analytical run. An N of 2 could refer to 2

measurements on a single control material or 1 measure ment on each of two different materials.

Remember that N is the "total number" of control measurements that can be spread over different

control materials being used.

 R is the number of runs over which the control rules are applied. In this example, all the rules are

applied only in the 1st run. However, if a 41s rule were added to the multirule procedure, the rule

could only be applied if there were 2 runs each having 2 control measurements to give the total of

4 needed to apply the rule. Likewise, if a 10x rule were added, there would need to be 5 runs to

accumulate the 10 measurements.



How to determine method performance specifications









Recall the steps in the planning process when the intent is to determine the imprecision and inaccuracy

that is needed by a method:



 Define the quality required for the test, e.g., a 20% clinical decision interval for cholesterol on

basis of NCEP treatment guidelines.

 Obtain the OPSpecs chart for a 20% Dint, as shown in the accompanying figure. Note the title of

the OPSpecs chart states Dint 20% with 90% AQA(SE), indicating it has been prepared for a

clinical quality requirement of 20% and 90% detection of medically important systematic errors.

 Specify the laboratory's preferred QC procedure, e.g., 1 2.5s with N=2 as shown by the solid line.

Note that the 12s procedure with N=2 is being avoided here because of its high false rejection rate

of 10%.

 Read the x-intercept for the solid line that corresponds to the preferred QC procedure, in this case

the line for 12.5s with N=2. In this example, it looks like a method CV of 2.7-2.8% is needed if





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method bias is zero. If method bias were 1.0%, then a CV of about 2.5% would be needed; if

method bias were 3.0%, then a CV of 1.9% would be needed.



HOW TO SELECT A QC PROCEDURE









Again, let's go through this application step-by-step to illustrate how OPSpecs charts are used:



 Define the quality required for the test, in this case, an allowable total error of 10% according to

the CLIA criterion for acceptable performance for a cholesterol test.

 Obtain the OPSpecs chart for 10% TEa, as shown in the accompanying figure. Again, start by

reading the title of the chart which indicates TEa of 10% for 90% AQA(SE). Note that this chart

is for N=2 QC procedures which are preferred to keep the cost of QC low.

 Plot the operating point for the method, i.e., the observed standard deviation or CV (in %) is the

x-coordinate and the observed bias (in %) is the y-coordinate.

 Inspect the lines showing the allowable limits of imprecision and inaccuracy for different QC

procedures. Select a line above the operating point. If none is available, as in this example, it will

be necessary to consider other QC procedures having higher Ns or to settle for lower error

detection, perhaps 50% AQA(SE).

 Utilize other OPSpecs charts if necessary. If selecting a QC procedure to work with 2 control

materials, the strategy is to inspect OPSpecs charts in the following order:

o N=2 with 90% AQA

o N=4 with 90% AQA

o N=4 with 50% AQA

 If using 3 control materials, the strategy is to inspect OPSpecs charts in the following order:

o N=3 with 90% AQA

o N=6 with 90% AQA

o N=6 with 50% AQA





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 The objective is to select a QC procedure having 90% error detection with 5% or less false

rejection and the lowest number of control measurements possible. If necessary, settle for 50%

error detection and compensate by beefing up other non-statistical control procedures as part of

the overall or Total QC strategy. In the example here, it's not possible to achieve 50% AQA even

with multirule QC procedures and N up to 6, as shown by the OPSpecs chart titled "50%

AQA(SE)". The operating point for a 3% CV and 3% bias is still above all the lines for the QC

procedures given in the key here, which now include single-rule and multi-rule procedures with Ns

of 2, 4, and 6.

 Formulate a Total QC strategy that provides an appropriate balance of statistical and non-

statistical procedures (such as preventive maintenance, instrument function checks, method

validation tests, patient data QC, in-service training, staffing with operators who have maximum

experience and minimal rotations). This TQC strategy can depend primarily on statistical QC

when a solution is obtained from a 90% AQA chart. A balanced strategy is needed when the QC

selection is made using a 50% AQA chart. If less than 50% AQA, the maximum statistical QC that

is practical should be selected, but in addition, efforts should be made to maximize other non-

statistical components and to improve the performance of the method. It may even to advisable to

acquire a new method having better imprecision and lower bias, rather than expend so much

effort trying to control a method that does not provide the necessary analytical performance.









How to assess need for quality improvement

Another useful application of the OPSpecs chart is to assess the improvements in analytical quality that

are needed to simplify QC and reduce the number of control measurements. For example, the cholesterol

application considered above is shown on the accompanying OPSpecs chart for a TEa of 10%, 90%

AQA(SE), and QC procedures with Ns from 2 to 6.







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The effect



of

reducing

method

bias is

shown by

the

vertical arrow located at 3.0% on the x-axis. If method bias were reduced to zero, then a method

with a CV of 3.0% could be controlled using a multirule QC procedure with an N of 6. That's a

costly QC procedure, but at least the necessary analytical quality would be assured.

 The additional benefit of improving method imprecision is shown by the dotted horizontal arrow.

If the method CV were improved to 2.5%, adequate control is possible with single-rule or

multirule QC procedures having Ns of 4. If the method CV were improved to 2.0%, QC

procedures with Ns of 2 would be adequate.



The ability to assess the benefits of improvements in method performance is one of OPSpecs

chart's real advantages. Of course, this cycles back to its earlier application for setting performance

specifications. If the necessary analytical performance were achieved initially through careful selection and

evaluation of the method, then QC will turn out to be simple and easy to perform. However, when the

QC selection application demonstrates costly QC with high N, you have the information to help you

assess the benefits form any improvements in method performance.

Note also that the demands of different quality requirements can be compared by having OPSpecs charts

for both the clinical requirement (1st figure, NCEP clinical decision interval of 20%) and the analytical

quality requirement (last figure, CLIA allowable total error of 10%). As shown earlier, to achieve the

quality needed for a cholesterol test that will be interpreted according to the NCEP treatment guidelines,

a method should be selected that has a CV of 2.7% or less (when bias is 0.0 and the laboratory intends to

monitor performance with only 2 control measurements per run). Compare this with the demands of the

CLIA proficiency testing requirement where the method needs a CV of 2.0% or better (when bias is 0.0).

The clinical requirement for patient treatment is less demanding than the regulatory requirement for

method performance.

It makes no sense to have a more demanding requirement for analyzing proficiency testing

samples than for analyzing patient samples! This inconsistency is due to a lack of understanding of the

nature of these quality requirements and the inherent difficulty of comparing apples and oranges.

However, the OPSpecs methodology can translate both requirements into equivalent terms (operating

specifications) that can be compared - another advantage of the OPSpecs tool.









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DEVISING A PRACTICAL PROCESS

The importance of implementing a quality-planning process is evident from the general

principles of Total Quality Management (TQM) as embodied in the accreditation requirements of

JCAHO's IOP standards. CLIA provides a focus on laboratory testing processes and provides specific

rules for validating the performance of methods and planning QC procedures. NCCLS addresses the

issue of how to perform quality-planning for QC procedures in general terms. However, none of these

describe a detailed process that can easily be implemented. Our objective in this lesson is to devise a

detailed step-by-step process that can be supported by available tools or technology.

Review your intended applications

An understanding of your own uses of a quality-planning process is important, particularly for

assessing the practicality of different tools and technology. If you are a laboratory quality coordinator,

your first interest may be in satisfying regulatory and accreditation guidelines for planning your testing

processes. A rigorous and well-documented process will be important. If you are a manufacturer, your

interest may be in establishing performance specifications for the precision and accuracy of new methods.

If you are a laboratory inspector or a technical field specialist for a manufacturer, you may be visiting

different laboratories and will need a "portable" process that you can take with you - most likely installed

on your computer.









 Laboratory applications.

Selecting control rules

and numbers of control

measurements

is, of course, an important application in a service laboratory. In addition, the performance needed

by the method can also be determined if the QC procedures are given, which should be useful in

establishing purchase specifications for methods, instruments, and systems. QC recommendations

from manufacturers and QC guidelines given in the literature can be evaluated to be sure they are

adequate for the quality required for the test and the analytical performance claimed for the

method. It is also possible to compare allowable total errors, clinical decision intervals, and

biologic goals to determine which is most demanding and should take priority in managing a

testing process.







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 Manufacturers' applications. The design of new methods and systems should be greatly aided

by a quantitative approach for setting performance specifications for imprecision and inaccuracy.

QC recommendations can objectively develop and validated based on regulatory quality

requirements, the design specifications for imprecision and inaccuracy, and the common QC

practices in the marketplace. Customers can be supported and assisted in the proper management

of analytical systems by having a better understanding of the relationships between the quality

required for a test, the imprecision and inaccuracy expected from a method, and the QC

procedures to be implemented.

 Regulatory and accreditation applications. It is interesting that there is little documentation of

the source and origin of proficiency testing (PT) criteria, such as those allowable total errors

specified by CLIA. Regulatory agencies and PT providers should evaluate the practicality of

proposed PT criteria by comparison with clinical decision intervals, taking into account the

common QC practices and expected method performance. Manufacturer's QC product labeling

should be reviewed to assess whether those QC instructions are valid for the intended users.

Laboratory QC practices should be reviewed to assess whether they are valid to assure the quality

needed for the patient populations and clinical applications of a healthcare organization.

Laboratories and manufacturer's should be educated and supported to provide more optimal

management of the analytical quality of their tests and systems.



Recognize two categories of applications

The main applications involve either the

(a) Selection of the method of analysis or establishment of performance specifications for imprecision

and inaccuracy, or

(b) The selection of a QC procedure for a method in routine service. In both cases, the first step will be

to define the quality requirement for the diagnostic test of interest. Then, as shown in the accompanying

figure, there are two variations of the planning process, depending on whether the purpose is to select the

method of analysis or to select a QC procedure:



 To select a method of analysis or set performance specifications for a method, the quality-planning

process involves specifying the QC procedure (statistical control rules, number of control

measurements or N) that will be employed and then setting the developmental or purchase

specifications for the imprecision and inaccuracy of the method.

 To select a QC procedure for a method, the process involves assessing method performance

(imprecision and inaccuracy) and then selecting the statistical control rules and number of control

measurements to be used.



The key step in both applications is the use of an appropriate quality-planning tool that will translate the

defined quality requirement into specifications for the imprecision and inaccuracy that are allowable and

the QC that is necessary.

Identify a practical quality-planning tool

A chart of operating specifications (or OPSpecs chart) is the most practical tool because it

provides all of the necessary information on a single graph. It is easy to use and easy to prepare using a

computer program, but it is complicated to understand. An analytical quality-planning model is available

to translate an allowable total error requirement into the imprecision and inaccuracy that are allowable

and the QC that is necessary. A clinical model is available that accounts for pre-analytical factors, such as

within-subject biologic variation, as well as the analytical factors - imprecision, inaccuracy, and QC. The







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theory will be considered later; our interest now is to demonstrate the practicality of the OPSpecs chart

for quality-planning applications.

An OPSpecs chart is like a map. You use it to find where you are and to provide directions to

where you are going. First you need to get the right map, which requires defining the city, county, or state

of interest. This is analogous to defining the quality requirement for a test. There are different maps for

different quality requirements, but all the maps are similar in form. As illustrated in the accompanying

figure, the map identifies areas of deep water, shallow water, and solid ground. From a quality-planning

point of view, the objective is to be on solid ground rather than ending up "in the drink." Given the

correct map, you can find your location by knowing the coordinates on the x-axis and the y-axis, or you

can look up the coordinates to get to a location of interest.









An actual OPSpecs chart

will give the y- coordinate as

the allowable inaccuracy and

the x-coordinate as the allowable

imprecision, as illustrated in the

next figure. The area of "deep water" is defined by a line that corresponds to a criterion for stable method

performance, most commonly a total error criterion composed of the bias (method inaccuracy) plus two

standard deviations (method imprecision). This is a hazardous area and must be avoided if you want to

assure the quality required for a test. The solid ground is delineated by a QC procedure, i.e., the limits of

bias and imprecision that are allowable for a specific control rule or combination of rules and a given

number of control measurements (N). Several lines may be provided corresponding to several different

QC procedures. In the figure here, three different QC procedures are depicted by the green, orange, and

red lines.



To select an appropriate QC procedure, find the location that corresponds to the imprecision and

inaccuracy observed for your method. This is the "operating point" of the method in your laboratory.

Then, look to see if you're on solid ground for any of the candidate QC procedures. In this example, only

the QC procedure corresponding to the green line could be used to assure the quality of your test results.

All that remains is to look up the control rules and N that correspond to the green line (usually be given

by a "key" to the lines).









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The next figure shows how an

OPSpecs chart could be used to establish

performance specifications for the

imprecision and inaccuracy of a method.

In this application, you select the QC procedure to be used in routine operation and then determine the

imprecision and inaccuracy that are allowable. For example, if you pick the orange line as illustrated here,

then x-intercept of that line will specify the maximum imprecision that would be allowable if bias were

zero. Note that the specifications for allowable imprecision and inaccuracy are interdependent. Any

allowable bias above zero will reduce the imprecision that is allowable.









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Adapt the NCCLS QC planning process

Now that the general approach has been outlined and the application of the OPSpecs chart has been

illustrated, we need to develop a more detailed step-by-step process. Given that the NCCLS guidelines

for planning QC procedures are the best developed, they provide a good starting point for devising a

practical planning process. Note that the NCCLS term "statistical QC strategy" is replaced here with the

term "QC procedure" to avoid confusion with the "Total QC strategy" term used here to describe an

overall QC system that may include non-statistical components (such as instrument function checks,

patient data QC, instrument maintenance, etc). The second important point is that the NCCLS guidelines

do not provide any "specifics" for how to predict (step 4) and set goals for QC performance (step 5). In

devising a step-by-step planning process here, QC performance will be characterized by the probabilities

of rejecting runs having different sizes of errors, therefore there are two probabilities that are of particular

interest:



 Probability of false rejection, i.e., the chance of rejecting a run when there are no errors except for

the inherent random error of the method;

 Probability for error detection, i.e., the probability or chance of rejecting a run whether there is an

error present in addition to the inherent random error of the method.



Therefore, in devising a practical quality-planning process, we will add some specifics to the NCCLS

process, particularly steps 3, 4, and 5.

Define a detailed step-by-step process

An eight-step quality-planning process is shown in the accompanying flowchart. Here's a description of

each of the steps:



1. Define the quality required for the test. For practical purposes, it is easiest to get started with

requirements in the form of an allowable total error, such as specified by proficiency testing or

external quality assessment programs.

2. Assess method performance in terms of imprecision and inaccuracy. Here's where method

validation experiments are important to provide the initial estimates of imprecision (from a

replication experiment) and inaccuracy or bias (from a comparison of methods experiment). Later

on, the estimates of imprecision can be obtained from routine QC data and estimates of bias can

be obtained from monthly peer comparison data and proficiency testing results.

3. Assess QC performance of candidate procedures in terms of the rejection characteristics

or power curves. This information is available in the scientific literature for most of the

commonly used QC procedures] and can be incorporated in quality-planning tools and technology

to facilitate the application.

4. Utilize QC planning tools. The available tools include QC simulation programs , power function

graphs , critical-error graphs , QC Selection Grids (QCSGs) , and OPSpecs charts. The OPSpecs

chart is recommended here because of it is a quantitative tool that is easy to use and readily

available.

5. Evaluate the probabilities of rejection for the operating conditions in the laboratory. In the

quality-planning process recommended here, the probabilities for false rejection will be minimized

(below 0.05 or 5%) and error detection will be maximized (0.90 or 90% and greater).

6. Select appropriate control rules and the total number of control measurements. A wide

variety of control rules are available. The rejection characteristics of each QC procedure must be

known if it is to be a candidate for implementation. Candidate QC procedures include single-rules

such as 12s, 12.5s, 13s, and 13.5s with Ns of 2, 3, 4, and 6; multirules such as 13s/22s/R4s/41s/8x with Ns

of 2 and 4 and 13s/2of32s/R4s/31s/6x with Ns of 3 and 6.



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7. Adopt a Total QC strategy that provides an appropriate balance of statistical and non-

statistical components. This TQC strategy defines the relative amount of effort expended for

statistical QC, instrument function checks, method validation tests, patient data QC, preventive

maintenance, and operator training. When HIGH error detection is obtained by statistical QC (i.e.,

Ped of 0.90 or 90% detection of medically important errors), the HIGH TQC strategy is to

depend on statistical QC and perform the minimum other QC required by regulations,

accreditation, and good practice guidelines. When MODERATE error detection is obtained (Ped

between 0.50 and 0.90), the MODERATE TQC strategy is to balance the efforts over all the QC

components. When LOW error detection is available (i.e., Ped less than 0.50), the LOW TQC

strategy emphasizes preventive measures because problems can not be detected by statistical QC.

8. Reassess the control rules, N, and TQC strategy when method performance or quality

requirements change. Given a quality-planning process that is quick and easy to perform, it can

be repeated whenever changes occur or when methods are periodically reviewed.



Obtain the necessary tools or technology

A laboratory's ability to do anything efficiently often depends on utilizing tools and technology to

facilitate a process. Most laboratory procedures have evolved from an initial qualitative manual method

(1st generation) that has then been systematized and made more quantitative with tools such as diluters

and photometers, then automated through succeeding generations of technology until complete systems

are available that are highly efficient and productive (such as todays 4th and 5th generation chemistry and

hematology analyzers). Quality planning, likewise, must evolve from a qualitative manual method to a

systematic process that utilizes standard tools to a quantitative automated process that is quick and

effective.

Concerning OPSpecs charts - the quality-planning tool recommended here, different "generations" are

available, as follows:



 Manual from scratch: using theoretical models available in the scientific literature with

implementation via electronic spreadsheets;

 Kit form: using preprinted charts in workbook form (an atlas of maps), such as the OPSpecs

Manual, or a standard set of Normalized OPSpecs charts, which will be provided along with these

lessons;

 Semi-automated: using Internet calculation tools, such as the "normalized" OPSpecs calculator;

Automated: using a PC computer program - QC Validator - that prepares OPSpecs charts for

both analytical total error requirements and clinical decision interval requirements (version 1.1)

and fully automates the selection of QC procedures (version 2.0).

 Highly automated: using a QC rule selection engine that can be embedded in QC software to

support the automatic selection and design of QC procedures



Applying Quality Management Methods and Measures to Server Management



Quality management techniques that can be used in support of system administration tasks are X-

Bar and RADAR charts. X-Bar charts will be used to monitor and measure the following network

statistics:

METRIC MEASURE PERIOD AVG

Available Volume Space Percent Remaining Daily 75

Available Cache Percent Remaining Daily 80





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Redirected Blocks Quantity Weekly 0

Cache Hits Peak Percentage (Only Daily 85

available in Netware 4.x)

Bad Frame Count Quantity (cumulative) Daily 25

Physical LAN Peak Percentage Utilization Weekly 14

Bandwidth





The resulting charts will be constructed with baseline averages that reflect Novell

recommendations and/or acceptable parameters based on empirical experience in designing, installing

and supporting LANs.

RADAR charts will be used to monitor file server performance. The approach to developing RADAR

charts for server performance analysis is to create a chart template with radial vectors alternating between

Higher is Better and Lower is Better. The following illustration shows how the chart is laid out:









Examples of Higher is Better vectors include: CPU utilization (for servers providing file and print

services), percent cache hits, percent available cache and LAN controller I/O. Lower is Better vectors

include: disk I/Os pending, dirty cache, bad frame count and physical LAN bandwidth utilization.

The following illustration depicts a RADAR chart that has been designed specifically for Netware file

server analysis.









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All data required to plot points on the RADAR chart are available from the following Netware facilities:



VECTOR DERIVED

SIGNIFICANCE

METRIC FROM



CPU MONITOR.NLM For file servers providing file and print services higher

Utilization CPU utilization is desirable for load balancing. CPU

utilization is normally 10-15% under Netware, but can be

driven to higher levels by segmenting the LAN by placing

multiple network cards in the file server. This taps

underutilized CPU resources while increasing available

physical LAN bandwidth by segmenting the LAN into

subnetworks, using the file server as a router.



Disk I/O MONITOR.NLM High or steady numbers indicate insufficient cache

Pending and/or bottlenecks in SCSI channels or disk drives.



Dirty Cache MONITOR.NLM This metric represents data that has not been flushed to

disk storage. Netware employs cache to provide fast data

access, so a certain amount of dirty cache will always be

present on an active file server. A high number,

combined with a steady number for disk I/Os pending,





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indicates SCSI or disk bottlenecks.



Percent MONITOR.NLM Percentage of file server RAM allocated to cache. This

Cache metric is a ratio of the amount of RAM assigned by

Netware to the total amount of RAM in the server. It is

not configurable. Novell recommends maintaining 80%

available cache.



Cache Hits MONITOR.NLM Percentage of time that data is retrieved from cache, as

opposed to retrieved from disk. Cache speed is measured

in nanoseconds, which is an order of magnitude faster

than millisecond speeds associated with disk access. If

cache hits drop below 85% Novell recommends adding

more RAM to the file server. This metric is only available

in Netware 4.x.



Bad Frame MONITOR.NLM Primary causes are problems with physical cable plant,

Count including hub ports, or failing network interface card.

Can also be caused by heavily loaded network segments.



LAN Card MONITOR.NLM High I/O count indicates adequate file server CPU

I/O power, efficient network drivers and high performance

network interface card.



Physical LANALYZER Bandwidth utilization in excess of 20% average indicates

Bandwidth a segment that is nearing saturation because peaks from a

20+ percent baseline will probably be in the 50-70%

range, causing intermittent slowdowns and impacting of

end user productivity. Remedies include: adding network

interface cards to the file server to further segment the

LAN, employing cross-point matrix switches or

collapsing the backbone with a router. Each of the

remedies presented are in increasing order of cost and

management complexity.





From the foregoing the value of RADAR charts for server capacity and performance analysis is

apparent. The following illustration shows various server states that can be discerned from a RADAR

chart:









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The combined use of X-Bar and RADAR charts as TQM tools for system administration tasks will result

in the following benefits:

 Consistent measurement process for key technical parameters related to file server and network performance



 Historical data that will support life cycle management decisions for capacity and technology

upgrades

 The ability to apply proactive management techniques to system administration by spotting and

acting on trends that point to impending problems. This approach provides higher quality service

than waiting for the problems themselves to surface, causing reactive corrective measures.



SELECT CASE STUDIES



QUALITY ASSURANCE OF ASSESSMENTS: A CASE STUDY



Higher education institutions find themselves on the brink of a completely transformed educational scene.

However higher education institutions are also influence by international trends in higher education.



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These influences impact on the roles of the educators and students. In pursuit of quality, academics and

students must be continuously engaged in a process of finding opportunities for improving the teaching

and learning process, the quality of the learning experience and the way it is delivered and assess. In this

paper the focus will be on quality assurance of student learning, but with a special focus, on quality

assessments used in the Public Human Resource Management III module at the Central University of

Technology Free State. The purpose of this article is to investigate to what extent a group of 87 third year

students according to their own perceptions attach value to the (self-assessments, assignment and formal

test) assessment methods associated with the critical outcomes of the Public Human Resource

Management III Module.



INTRODUCTION



The adoption of an outcomes-based education approach posed important challenges for the higher

education institutions provision of learning. With implementation of such an approach the higher

education institution have to deal not only with the requirements indicated by the South African

Qualifications Authority (SAQA) 1995, (Act 58 of 1995) but also with the norms and standards laid down

by the Higher Education Quality Committee (HEQC) (Council on Higher Education 2003:1). Being

quality and service minded in higher education means that academics have to ensure that the goals needs

desires and interests of the students are met (Steyn 2000:174). In the light of this it is important to ensure

that all education process contribute directly or indirectly to quality as the clients (students) describe it

Applying principles of outcomes based education means also that the learning and assessment processes

needs to be assessed to determine the quality as defined by the students. This article discusses the findings

of an investigation on the use of outcomes-based assessment methods at the School of Government

Management, Central University of Technology, Free State. Many of these findings have a direct bearing

on the development of quality assessment methods in a changed educational environment.



DEFINITIONS

For the purpose of this article, the following concepts need to be defined



Assessment. Assessment is the process by which academics make judgments how well the learning has

occurred. The assessment of student learning is generally understood to mean the practice of designing

formal tasks for students to complete and then of making inferences from and estimating the worth of

their performances on these tasks.



Formative assessments. This is used to improve learning through the provision of feedback to students

on their progress, serves needs intrinsic to the educational process.



Outcomes-based assessment. Outcomes-based assessment refers to the process to assess student‘s

ability to demonstrate the outcomes that they have to achieve. With this as the prime purpose of

assessment, it is easy to maintain a strong-link between the outcomes that learners have to achieve, the

education strategies that academics use to facilitate learning and the assessment of learning .



Self assessments. Self-assessments refer to the activities where students judging their work (test,

presentation or assignments) by providing written feedback and a grade, by using a criteria sheet and

model answers (Brown & Knight 1994:10 and Ellery and Shutherland 2004:102). Lucket and Sutherland

(2000: 112) also indicated that with self-assessments students are invited to assess themselves against a set

of given or negotiated criteria, usually for the formative purposes. For the purpose of this article self

assessments refer to the range of learning assessments in the prescribe learning material that each student

have to do at the end of each learning unit. With the self assessment s students are able to assess whether



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they achieve the learning objectives stated at the beginning of each learning unit. With the self

assessments students takes responsibility for their own learning, to consolidate their learning and to

become more reflective and effective learners.



Summative assessment. This is used to certificate the attainment of a certain level of education and to

make educational decisions, formalised assessment used to serve needs extrinsic to the educational process

(Council on Higher Education 2003:9). Summative assessments provide judgment on student‘s

achievements in order to establish student‘s level of achievement at the end of the course/programme .

Outcomes. Outcomes refer to the knowledge, skills and values within particular contexts .

Quality Assurance. This in turn involves measuring and evaluating performance to these standards,

reporting results and taking appropriate action to deal with deviations .



HOW IS STUDENT ASSESSMENT CHANGING



While academics have favoured traditional assessment methods for years the current shift in educational

practice towards outcomes-based education emphasise the adoption of a new approach of assessment.

Also with the implementation of the South African Qualifications Authority (SAQA) Act 1995 (Act 58 of

1995) higher education institutions is moving away from the traditional examination driven approach to an

outcomes-based assessment approach that is seen to have greater educational value in terms of the kinds

of education and learning it encourages (Ellery &Sutherland 2004 100).



In the new approach of assessment students will be asses in relation to the learning outcomes of the unit

standards they are to achieve. Outcomes based assessment encourages careful reflection on education

strategies and the provision of learning opportunities that ensure that students are enabled to attain a set of

learning outcomes and to demonstrate them in assessments (Killen:2000:79). With this as the prime purpose

of assessment it is easy to maintain a strong link between the outcomes that students have to achieve and the

assessment of learning.



When academics consider methods of assessment they will find that there are no method unique to

outcomes-based education. In fact there are no methods that can never be used in outcomes-based

education (Killen 2000: 79). However care needs to be taken by academics not to be overburdened with

too many assessments. The key to outcomes based assessment is to specify learning outcomes and to

assess student performance. This involves making explicit the learning outcomes that the students have to

achieve and then designing assessment instruments that will effectively assess student attainment of these

outcomes (Council on Higher Education 2003:3). Explicit assessment criteria are derived from the

learning outcomes in order to assess a particular performance. Outcomes-based assessment approach pose

enormous challenges for assessment practice in higher education. In the light of this assessments should

contribute to:



 Improve the quality of education and training



 The process of assessment should be based on outcomes, unit standards and moderation



 The basic assessment principles (criteria) are validity, reliability, flexibility, fairness, a holistic approach

to assessments



 Increase the emphasis on the learning enhancement purpose of assessment







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 Increase attention to formative assessment rather than only on summative assessments



 Assess students abilities to integrate knowledge and skills to achieve specific outcomes



 A move away from one main method of assessment that take place at the end of the course to the

implementation of a wide range of continuous assessment methods (Council of Higher Education

2003:2-3 and Guidelines for Education and Training Quality Assurance Bodies 2001).



With the above in mind it is clear that the development of fair, effective and efficient assessment

provision in higher education requires a more comprehensive role for assessment than has traditionally

been the norm. The real challenge is how should academics go about to create the conditions for

outcomes-based assessment. One means of attempting to do so is the development and implementation

of teaching and learning policies and strategies that emphasize outcomes-base education and assessment.



THE ROLE OF QUALITY ASSURANCE OF ASSESSMENTS IN TEACHING AND

LEARNING.



It is widely recognized that assessment is a critical component in higher education and influences the type

of learning that take place (Ellery & Sutherland 2004: 99). Assessment is also the educational event that

holds the highest stakes for students in terms of their achievement. In fact according to Gibbs 1996:5-6)

assessment can be used strategically to change the way students learn. Assessment procedures and

methods influence not only the learning styles and strategies, but also their attitudes, motivation, sense of

ownership and even their self esteem. For these reasons measures to ensure the quality of assessment

practices are critical. With this in mind the role of quality assurance used to provide judgment on the

education system in order to



 provide feedback to academics on the effectiveness of their teaching and assessment methods

 assess the extent to which the learning outcomes of a module, course or programme have been

achieved

 evaluate the effectiveness of the learning environment

 monitor the quality of assessment methods used



With quality assurance it is necessary to reaffirms the higher education institutions commitment to quality

learning, education and assessments. This outcome will only be attained by identifying and implementing

measures of quality and performance in order to facilitate the quality process. The development of a

reliable and valid instrument for assessing high quality teaching, learning and assessments is crucial. By

using the feedback from such measures can contribute to the improvement of quality assessments.



AN EMPIRICAL INVESTIGATION INTO LEARNERS PERCEPTIONS



Overview of the Public Human Resource Management Module



The Public Human Resource Management III module is part of the Public Management Programme. In

2002 the School of Government management at the Central University of Technology adopted the

outcomes based education approach. The New Public Management programme consist of 24 modules

that are presented from the first year up to the BTech (fourth year) level. Each module is indented as a

unique contribution to the field of public management and to provide students with the necessary skills

and competencies to be able to manage public institutions effectively and efficiently. The Public Human



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Resource Management III module consists of six learning units. In each learning unit specific learning

objectives are provided. The current assignment, self-assessment and the formal test for this module was

introduced in 2002 to assess whether a student achieve the specific learning objectives. At the end of the

semester students have to write a formal written evaluation (summative assessment) to make a judgment

of the standard of achievement of the learning outcomes.



Purpose of the investigation



This empirical investigation attempts to determine whether the third year Public Management student‘s

benefits from the assessment methods (self assessments, the assignment and the formal test) used to

achieve the critical learning outcomes of the Public Human Resource Management IV module. It does so

by investigating whether the students according to their own perceptions



 Benefits from the assessment methods (Assignment, Self-assessments and the formal test) used to

achieve the outcomes of the different unit standards

 Benefits from the formal written feedback and the feedback lecture about their progress with the

assignment



Research methodology



The investigation was conducted at the Central University of Technology Free State, a higher education

institution in Bloemfontein, South Africa with the third year Public Management (full time students).

Approximately 107 full time third year students enrolled in January 2004 in the Public Management

programme. A total of 87 questionnaires, which constituted 81.3% of the total population of the third

year Public Management students enrolled for the module, were analysed. This can be regarded as valid

and useful (representing 81, 3% of the population).



The research instrument



The questionnaire posed thirteen questions to the respondents. Respondents had to answer these

questions by plotting their answer based on their perception on a four point scale.

(1) Strongly disagree

(2) disagree

(3) agree

(4) Strongly agree.



Research findings



The research findings are presented in table 1. The table is a combined analysis of the responses to the

items of the assessment (assignment, self-assessment and the formal test)



TABLE 1 COMBINED ANALYSIS OF THE RESPONSES TO THE ITEMS OF THE

ASSESSMENT USED IN THE PUBLIC HUMAN RESOURCE MANAGEMENT III MODULE



ITEM RATING FREQUENCY PERCENT

SCALE (AMOUNT of %

students)

A. ASSIGNMENT





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1. Did you attend the lecture 4 53 60.9%

about how to compile an 3 33 37.9

effective assignment 2 1 1.14%

1 0 -

2 Is the topic of the assignment 4 63 72.4%

relevant to the module 3 24 27.5%

2 0 -

1 0 -

3. Did the assignment assist you 4 52 59.7%

to achieve the outcomes stated 3 34 39.0%

in the learning unit 2 0 -

1 1 1.14%

4. Did you buy the guidelines 4 9 10.3%

how to compile a good 3 0 -

assignment from the Library 2 0 -

and Information Centre 1 78 89.6%





5. Did you benefit from the 4 53 60.9%

memorandum to improve your 3 32 36.7%

writing and research skills 2 1 1.14%

1 0 -

6. Did you benefit from the 4 53 60.9%

feedback lecture 3 34 39.0%

2 0 -

1 0 -

7. Did you benefit from both 4 70 80.4%

the memorandum and the 3 17 19.5%

feedback lecture to improve 2 0 -

your research skills 1 0 -

8. Did the assignment assist 4 57 65.5%

you to enhance your knowledge, 3 30 34.4%

and skills about the specific 2 0 -

module 1 0 -



B. FORMAL TEST

1. Where the questions in the 4 55 63.2%

test in line with the learning 3 31 35.6%

outcomes of the specific units 2 1 1.14%

in the module 1 0 -

C.SELF-ASSESSMENTS

1. Did the self-assessments at 4 59 67.8%

the end of each unit help you to 3 28 32.18%

prepare yourself for the 2 0 -

summative assessment 1 0 -

2. Did the self assessments 4 50 57.4%

assist you to achieve the 3 37 42.5%

outcomes of the different units 2 0 -



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1 0 -



3. Did you benefit from doing 4 59 67.8%

the self-assessments to gain 3 27 31.0%

extra knowledge in this specific 2 1 1.14%

module 1 0 -





Responses to all the activities were very positive, expect for the item dealing with the purchasing of the

guidelines how to compile a good assignment. Only 10.3% indicated that they make use of this

opportunity where as 89.6% of the respondents indicated that they did not purchase (at a very low cost)

the guidelines from the Library and Information Centre as requested. These guidelines assist students

how to compile a good assignment. The reason why students did not make use of this opportunity could

be that they are not serious enough about the assignment as an effective assessment method. However in

future these guidelines will be distributed to all the third year students in class. The main findings of this

empirical investigation can be summarised as follows:



 Responses to all the activities about the assignment as an assessment method are very positive except

for item number four that were discussed above. For instance 72.4% strongly agreed and 27.5%

agreed that the topic of the assignment is relevant to the module Public Human Resource

Management III. Only 1 respondent disagreed on item 3 that was about whether the assignment

assists them to achieve the outcomes stated in the learning unit about performance management.

Whereas 59.7% strongly agreed and 39% agreed that the assignment assist them to achieve the

outcomes stated in the performance management unit. 60.9% of the respondents indicated that they

strongly agreed that they benefited from the memorandum and the feedback lecture to improve their

writing and research skills. In fact 65% of the respondents strongly agreed and 34.4% agreed that the

assignment contributed to improve their knowledge and skills about performance management

systems. None of the respondents disagreed on this item.



 Moreover 63.2% strongly agreed and 35.6% of the respondents indicated that the questions in the

formal test were in line with the learning outcomes of the specific learning units in the Human

Resource Management III module. Only one respondent of the total of eighty seven respondents

disagreed on this item.



 The majority of the respondents indicated that the self-assessments assist them to prepare for the

main summative assessment. 67.8% strongly agreed and 32.18% agreed with this item. 57.4 %

strongly agreed and 42.5% agreed that the self-assessments assisted them to achieve the outcomes of

the different learning units. None of the respondents disagreed on the abovementioned items.

However only one respondent disagreed on item 3 of the self-assessments. Item 3 was about whether

they benefit form the self-assessments to gain extra knowledge in the specific module.



CONCLUSION



No matter how formal education is organized, it will involve the continuous planning, instruction,

assessment and reporting of student achievement. With the adoption of the outcomes based approach to

education it becomes critical important to measure whether the current assessment used contributed to

the learning process of achieving specific learning objectives.







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To promote learning, assessments must incorporate genuine feedback and quality assurance. In other

words, assessment information must reveal to students an understanding of their work compares to

specific standards and learning outcomes, as well as information about how to improve their skills and

knowledge if improvement is needed. This article demonstrated how the School of Government

Management at the Central University of technology, Free State has embarked on the quality assurance

journey to ensure that the adopted assessment approach assist with the achieving of specific standards

and learning outcomes. It seems that the assignment, the self-assessments and the formal test used has

succeeded in assisting students to achieve the specific learning objectives in the module. Evidence was

given that the students benefit from the assessment methods used towards a quality learning process.









TOTAL QUALITY MANAGEMENT IN THE HOSPITALITY INDUSTRY



1. Introduction

Visualize the lobby of a hotel that is renowned for its quality service. The General Manager is discretely

observing the activity in the foyer.

Nearby is the front desk and guests are being checked in, and from his vantage point the Manager can

hear what is being said.

The front desk clerk is confirming the arrangements of the booking with the guest and the following

discussion occurs:

"Sir, you will be charging your accommodation to the company and paying your other expenses."

"No, all expenses will be paid by the company."

"I am sorry sir, but according to this we have only authorized charge of the accommodation."

"Last time I stayed here I had the same problem and last week I personally rang to sort this out. All

expenses are to be charged."

The clerk goes to get authorization on the account and the now disgruntled guest turns to his companion

and says in exasperation:

". . . you see it's exactly as I said it would happen. I stay here every month and yet every time I have this

same problem."

The General Manager considers the exchange with concern. That guest had not received the quality

service the hotel was aiming to provide and if the guest continually had this experience it would simply be

a matter of time before he decided to try one of the competitors. Not only could that one guest's custom

be lost, but he could be the manager of a company who frequently stay at the hotel and hold functions

there.

The difficulty for the Hotel Manager is to determine how to react to this situation. Is it a problem that

only this particular guest faces or is it a common problem experienced by many? Whose fault is it that the

problem arises initially? What is the appropriate action to be taken?



2. TQM and service quality

Total quality management (TQM) is an approach to management that focuses on quality as the key to

success. The 'Quality Triangle' summarizes the components of Quality Manager (see Fig. 1):





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The focus on the customer in defining Quality.

The importance of teamwork in unifying goals.

The need for a scientific approach and decisions based on data.

Following the publication of the 'Foley Report' (Report of the Committee of Review of Standards,

Accreditation and Quality Control and Assurance, Australian Government Printing Service, 1987), which

concluded that

"Few, if any issues are more important than quality in meeting the need to improve the competitiveness

of Australian Industry". (Foley Report, p.43)

there has been heightened interest in Australia in the implementation and effective use of Quality

Management.

The acceptance of W. Edwards Deming's ideas in Japan, followed by the rapid success of Japanese

industry, goes some way to explaining the current interest in TQM in Western countries. Japan has to a

great extent replaced the USA in providing models of good management practice. In the immediate

post-war period, Japanese management practices were often characterized by Western writers as

irrational hangovers from a feudal past (e.g. Abegglen, 1958). Japanese management practices now find

a place in the curricula of most management courses. TQM holds a significant place in Japanese

management practice and is claimed by its proponents (Deming, 1986 and many others) to be the

fundamental reason for Japan's success.

TQM originated in a manufacturing environment and its terminology and techniques have largely been

developed in that environment. Its application in a service environment thus requires adaptation of the

ideas to a different set of circumstances.

How is service industry different? According to Enrick (1986):

Modern methods of quality control were developed and matured in manufacturing industries. These

involve the processing and fabrication of materials into finished durable and nondurable goods.... Service,

however, is a relatively distinct non manufacturing activity. Work is performed for someone else.

The major distinctions between service and manufacturing organizations are that the product:

is intangible and ephemeral;

is perishable;

frequently involves the customer in the delivery of the product;

is not perceived as a product by employees.

The intangible nature of the service as a product means that it could be very difficult to place quantifiable

terms on the features that contribute to the quality of the product. This could make measurement of the

quality of the product a problem for TQM.

As service products are perishable, they cannot be stockpiled and must be produced 'on demand'. The

result is that the process for delivering a service may be highly complex involving the co-ordination of

primary and support systems in what is usually a very time sensitive relationship with the customer. This

is in contrast to manufacturing organizations where although time may be an important aspect in the

delivery of the goods it is rarely regarded as a feature of the goods which will affect its quality.

In the case of a service organization time is regarded as an assessable quality or feature of the product.

For example people usually book aeroplane flights based on the departure and arrival times that are most

convenient. If a traveler is expecting to arrive at a destination at a specified time, and the aeroplane is 2

hours late the product will most likely have failed to meet the person's satisfaction. This is irrespective of

how comfortable the aeroplane was, how good the inflight service was, or the fact that the flight had been

made safely.







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The customer is frequently directly involved in the delivery of the service and as such introduces an

unknown and unpredictable influence on the process. The customer also adds uncertainty to the process

because it is often difficult to determine the exact requirements of the customer and what they regard as

an acceptable standard of service. This problem is magnified by the fact that, standards are often

judgmental, based on personal preferences or even mood, rather than on technical performance that can

be measured (King, 1985).

This has the result that while a service completely satisfied a customer yesterday exactly the same service

may not do so today because of the mood of the customer. Therefore there is a problem of the fickle

customer!

Deming (1986) suggests a further difference:

An important difference [between manufacturing and service organisations] is that a production worker

in manufacturing not only has a job: he is aware that he is doing his part to make something that

somebody will see, feel and use in some way.....In contrast, in many service organisations, the people that

work there only have a job. They are not aware that they have a product and that this product is service.

In manufacturing industries the product is highly visible and therefore identifiable whereas in service

organizations the 'product' is frequently 'invisible' and the customer cannot easily be identified. Often a

person in a service industry has no perception of their work being a product and that the way in which

his job is performed has an impact on the success of the organization as a whole.

How do these differences impact on the implementation of TQM in a service organization? Looking

again at the Quality Triangle, it is clear that the 'Focus on the Customer' is very much a part of the

provision of a service. The further development of identifying internal customers and building the

concepts of 'Teamwork' is less immediate. The intangible nature of the product may make it harder for

each individual to see that they are contributing to a common goal: Whereas a person making a physical

object can usually readily identify the next step in the process, and identify their contribution to the final

product and its quality, a clerk in the accounts receivable section of a hospital may find it difficult to

identify their customers and see how the quality of their work will affect the final product. However, the

difference is one of degree and simply requires, as in manufacturing, that each person be made aware of

the value of their role in producing a quality product and be allowed to contribute to continuous

improvement in the product.

A more fundamental difference lies in the third corner of the triangle: the 'Scientific Method'. This

involves the use of measurements and a scientific approach to problem solving in the search for ongoing

improvement in quality.

Measuring the length of a steel rod or the weight of a packet of biscuits is a simple matter. It can be

carried out on line, or the objects to be measured can be stored for later measurement. If the

measurements are taken in a timely manner, any defects can be detected before the shipment leaves the

factory, so that the high costs of a failure reaching the customer are avoided. Thus the use of 'Scientific

Method' is (relatively) straightforward.

In service provision the situation is very different. The involvement of the customer makes the definition

of quality varying from moment to moment. 'Service' cannot be stored, so the measurement must be

immediate. Finally, the service is delivered at the moment it is produced. Any measurement taken is thus

too late to avoid a failure in contact with the customer. The critical difference in the application of TAM

to service industries thus lies in the area of quality measurement and it is this issue that we shall address

in the remainder of this paper.



3. Quality in the hospitality industry

Quality is considered to be of very great importance in the hospitality industry. Mill (1986) identifies the

aim of service quality as being able to ensure a satisfied customer. However, the focus of quality initiatives



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has been primarily on selection and training of front line staff (see, for example, Gober & Tannehill,

1984; Mill, 1986; Cathcart 1988). The issues of measurement and process improvement have been largely

neglected.

The Mayfair Crest Hotel in Brisbane, Queensland, has adopted an approach to service quality which

resembles TQM. Kerr et al. (1988) describe this approach. It is based on an overall mission for the hotel:

"The Spirit of the Mayfair Crest is Serving You". This mission was cascaded through the hotel by each

department and subsequently each employee being asked to define the meaning of this mission in their

own context. Thus the overall direction of the staff of the hotel was brought together to develop the

teamwork that is vital to TQM.

However, the issue of measurement still remained a problem. Only feedback from 'How do you rate us?'

forms and indirect measures of employee satisfaction were used to measure their performance. Like all

such measures, they are received too late to prevent a problem affecting a customer.

How can appropriate measurements be developed for a hotel that can complete the quality triangle and

fully implement the TQM ideal?



4. Internal and external service quality measures

Service quality, which always involves the customer as part of a transaction, will therefore always be a

balance: the balance between the expectations that the customer had and their perceptions of the service

received. A 'high quality' service is one where the customer's perceptions meet or exceed their

expectations.

The components of perceived service quality have been identified (Parasuraman et al., 1988) as



1. Reliability: the ability to provide a service as expected by the customer.

2. Assurance: the degree to which the customer can feel confident that the service will be correctly

provided.

3. Tangibles: the quality of the physical environment and materials used in providing the service.

4. Responsiveness: the ability of the service provider to respond to the individual needs of a

particular customer.

5. Empathy: the courtesy, understanding and friendliness shown by the service provider.



Note that these are external measures: they can be obtained only after the service is delivered. They thus

suffer from the problems noted above for service quality measures: a failure can be detected only when it

is too late to respond.

Such measures have great value, but not in the ongoing business of monitoring and improving quality.

Rather they can indicate the targets that must be aimed for. They define what the customer is expecting

and so what we must aim to deliver. In order to deliver these expectations, we need internal measures:

measures that will tell us how we can deliver what the customer expects. More importantly, how we can

know before delivery that the service will exceed the customer's expectations?

Zimmerman & Enell (1988) advise that careful consultation with the customer and an appraisal of the

performance of competitors is needed in order to create any scales or measurements of quality which they

place in a narrowed down framework of four quality standards. The four service quality categories are:

timeliness;

integrity;

predictability;

customer satisfaction.







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Timeliness of service has been referred to by a number of authors as an important component in the

quality of a service. It is a reasonable feature of service to be given high priority because the service has to

be produced on demand and the interval in provision is an element of the actual product.

Timeliness may be separated into three types: access time (the time taken to gain attention from the

company); queuing time (this can be influenced by the length of the queue, or its integrity); and action

time (the time taken to provide the required service).

Integrity deals with the completeness of service and must set out what elements are to be included in the

service in order for the customer to regard it as a satisfactory product. This standard will set out precisely

what features are essential to the service.

Predictability refers to the consistency of the service and also the persistence, or the frequency of the

demand. "Standards for predictability identify the proper processes and procedures that need to be

followed . . . (and) may include standards for availability of people, materials and equipment, and

schedules of operation" (Zimmerman & Enell, 1988).

Finally customer satisfaction is designed to provide the targets of success, which may be based on relative

market position for the provision of a specific service. These are the external measures noted above.

Once these service standards have been determined the next step is to develop measurement techniques

to monitor how well the standards are being achieved.

The measurement step is the second vital component of TQM, without which the supporting

philosophies lack coherence. Once measurement methods have been developed and results derived the

process being studied can be placed in this measured context and decisions made accordingly. The

remaining aspects of TQM present no greater difficulties than in a manufacturing organization.



5. Case study

The concepts developed above were implemented in a study of processes at the Sheraton Brisbane Hotel

and Towers. Sheraton have implemented for some years the Sheraton Guest Satisfaction Scheme which

has focused the attention of Sherton staff on the importance of service quality. However, prior to the

study they had made limited use of internal quality measurement and the main aim of the project was to

develop such measures for some of the processes within the hotel.

The processes chosen for study were identified at a meeting with the hotel's Executive Committee. They

were chosen to be of interest to the Committee and also to be likely to give reasonable results in the time

available for the study.

The processes chosen were:



1. The reservation process, from the time a guest makes a booking until they arrive at their room.

2. The function process, from the time the organizers book the function room to the completion of

the function.



The first step in studying the process was the preparation of detailed flow charts of the processes.



1. Meetings were held with the managers of divisions directly involved in the processes. This ensured

that these key managers understood the aims of the project and would give their support.

2. Interviews were conducted with staff at all stages of each process to identify:



their roles and activities;

other staff with whom they interacted;

their sources of information;





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their customers.



 3. Sections of the processes were observed in action to ensure that the information gained in the

interviews was correctly understood.

 4. Flowcharts were drawn up: where necessary, additional information was obtained to allow them

to be completed. These were then checked with staff involved in the processes.



Having thus clearly defined the steps involved in these processes, measurement points were identified

that would allow assessment of timeliness, integrity, predictability and satisfaction.

Some of the measures that were identified are shown in Table 1. A number of these were studied. Here

we shall concentrate on three of them:

The Towers' Check-in;

The Luggage Survey;

The Event Order.

5.1 Check-in at the Towers

The Towers part of the Sheraton Brisbane Hotel and Towers offers a very high standard of

accommodation and service. In order to speed check-in for Towers' guests, a separate check-in desk had

been established on the 27th floor. However, the Hotel's management were concerned that too many

Towers' guests were unaware of this and were waiting in line at the Front Desk before being redirected to

the 27th floor. In an attempt to rectify this a sign indicating the separate Towers' reception area had

recently been placed in the Lobby, however management had no information regarding the

effectiveness of the sign in providing directions to the Towers' guests.

The aim of this study was therefore to determine how Towers' guests knew that check-in was on the 27th

floor.

In order to collect the necessary data a study was constructed that involved three different measurements:

(a) a study of the process of the arrival of Towers' guests;

(b) a measure of the number of Towers' guests approaching Hotel reception;

(c) an informal questioning of the Towers' guests on their arrival at Towers' reception regarding how they

were aware that reception was separate from the Hotel reception and where it was.

The process study indicated that once the guest is at the Hotel it is possible for them to gain information

about the Towers' check-in from three sources: the Doorman or Bellman; the Front Desk; or the Towers'

direction sign in the Lobby. It is important to know this when constructing a measurement such as a

record sheet for use in the Towers' reception relating to how the guest knew where the Towers' reception

was.

The objective of the study was to determine how guests knew how to locate the Towers' Reception.

From the process study it was possible to isolate the Front Desk and the Towers' Reception as the two

points of the guests' journey where it would be possible to gather useful data. So it was decided to take

measurements at both of these points.

Data from the Front Desk was collected on a form and the Front Desk staffs were asked to mark the

appropriate box for each Towers' guest they encountered (see Fig. 2).

At the Towers' Check-in, a similar form was used (see Fig. 3).

These surveys were carried out over a period of 2 weeks and the results collated. The results of the Front

Desk Survey were as follows:

Type of enquiry Number Percentage







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Asked to check in, did not mention Towers 58 81

Asked to check in to Towers 8 11

Asked for directions to Towers' reception 3 4

Checked-in--Towers reception closed 2 3

Total 71 100

Those who checked in at the Towers gave the following sources of information for the location of

Towers' reception:

Response Number Percentage



Stayed in Towers before 32 47

Went to Front Desk 31 46

Other 5 7

Total 68 100

A total of 38% of guests had seen the sign in the lobby, but none gave this as the source of their

information. None had received information from a travel agent or other external source.

The study thus clearly indicated a need for better communication with new Towers' guests about the

location of the Towers' check-in.

5.2. The luggage Survey

The objective of this measurement was to determine the time delay if any between the guest's arrival in

the room and the delivery of the luggage to the room.

This is of interest to the Hotel because they have a policy that an individual checking in to a room should

have their luggage delivered no longer than 10 minutes after they arrive in their room. The measurement

was selected primarily so that the Executive Committee could have some definite figures on the delivery

of luggage rather than depending on their 'feelings' about what was occurring with the timeliness of

luggage delivery.

A number of possible methodologies of measuring this were discussed and the alternatives will be

mentioned later.

The approach decided upon to measure the timeliness of luggage delivery was to involve the guest in

recording what time he reached the room and the time the luggage was delivered.

A guest questionnaire card was designed to be handed out to guests. The card (Fig. 4) asked the guest to

record the time of arrival in the room and the time of luggage delivery. The completed card was then to

be given to the bellman delivering the luggage or returned to the Front Desk at a convenient time.

In order to maintain a check on how many cards were handed out to guests each day a colour coding

system was used. Each day corresponded to a particular colour, and coloured dots were placed on the

back of the cards. A count of the cards at the beginning and the end of the days indicated how many

cards were given out for that period. As cards were returned it would be possible to note the day they had

been handed out so that a control could be maintained on the return of cards.

This provided information on how many guests were given the cards but also made it possible to monitor

what proportion of those handed out were returned. Had the result been that a large percentage of the

cards were not being returned, it would be necessary to seek explanations for this.

The results obtained were of value less in determining the distribution of delays to luggage than in giving

a clearer picture of the process of luggage delivery.

Only 16 cards were handed out over the 10 day period. Of these 16 guests, 10 experienced no delay and

the longest delay was 8 minutes. This is however a very small percentage of the total number of guests

who checked into the Hotel. In fact, the vast majority were businessmen or women who brought very

little luggage with them and carried it themselves. In addition, since the Hotel was running close to 100%



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capacity, guests often arrived before their room was ready and left their luggage for later delivery. It

would then generally reach the room before them and they would experience no delay.

The only conclusion that can be drawn is that, by Sheraton's standard for delivery, there is no evidence of

a problem in the time taken to delivery luggage to rooms.

5.3. The Event Order

The Event Order is a form containing full details of customer requirements for a function (see Fig. 5). It

performs a vital role in the provision of information to the operational areas in the function area.

The aim of this study was to determine how effectively the Event Order conveyed information to the

users of the form.

As the objective was to see how well the Event Order was meeting the needs of the people using it, the

measurement had to gather information from the internal customers of the form.

The Event Order is produced by the Catering Office Staff after consultation with the clients. It contains

information relating to the details of the function, specifying details such as the number of guests, the

time of the function, the menu and its costing, the method of payment, and any other special

requirements of the function.

The Event Order is distributed to a large number of sections of the hotel ranging from the General

Manager' to public relations, the kitchens and house keeping. However, not all of the recipients require

the Event Order to provide information for the completion of their primary activities. Therefore the

study was narrowed down to apply to those sections of the organization who had a strong dependence on

the Event Order.

The group to be used in the study was determined in consultation with the Food and Beverage Manager

and the Catering Manager.

The purpose of this measurement was to ask the 'customers' to record occasions where the Event Order

had not provided the required information for them to satisfactorily perform their job. This lack of

information may have been in the form of incorrect information; unclear information which required

validation; or insufficient information leading the individual to seek more details.

In addition to filling out the Record Sheets, the staff were asked to attach these to the relevant Event

Order, and to also return all Event Orders, regardless of whether there had been a problem with it or not.

The aim of this was that it could be easily recognized when there had not been a problem with the Event

Order.

Meetings were held with all those who would be involved in the study and it was agreed to trial the

procedure for a period of four weeks.

Over the 4 week measurement period, a total of 27 Record Sheets were completed and submitted. Not all

of the people submitted all of the Event Orders they had received, however there were approximately 150

Event Orders distributed to each department during this period.

Six of the 27 Record Sheets made comments relating to the set up of rooms (the furniture requirements

and their arrangement in the room): the amount of information provided with regard to this; how

accurately the instructions reflected the needs of the client; and whether the prescribed set up was

appropriate for the room being used.

There were two occasions where there was an error with the name of the function. In one case the

function name was incorrect and in the other it was not actually provided. Again this was not a problem

with the design of the Event Order but rather an error in completing the information on it.

The Banquet Kitchen did not report any problems as a result of the information contained on the Event

Order. However the Banquet Chef did note that there were problems experienced quite often, but that

they were not directly related to the Event Order form.





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The most interesting consequence of the study was the effect of the recording on the Catering Office

staff who are responsible for completing the Event Orders.

Some members of the Catering staff commented that as the Record Sheets were being deposited in their

office and could be perused by them, it provided them with a feedback line of communication with the

people they gave the Event Orders to. This gave them the opportunity to focus on any problems that the

other sections were facing and to take these into consideration. They thus had an increased awareness of

some of the problems encountered by the other departments.

The Banquet Beverage staff also mentioned that they believed that the sheets provided a useful method

of feedback to the Catering Office.

This is not to suggest that there is not normally any communication between the departments--they do

meet on a regular basis to discuss the forthcoming events and sometimes comment on aspects of the past

functions-however often many details are missed on these occasions. The point made was that although

in general there is a good flow of information between the different sections, every one is very busy and

they simply forget to mention things that could have been valuable had they been passed on to the other

department. Therefore the members of the Catering department and the Banquet Beverage section

considered the opportunity to gain some feedback as a result of this formal channel to be most useful.

This study suggests that the Event Orders are well designed and except for some relatively minor errors

in the completion of the forms, they cater well for the needs of their users. It has also shown that the use

of rapid written feedback has the potential to further improve the Function process.



6. Conclusions

The major difficulty for service organizations in implementing TQM is determining measurements that

provide quantifiable data. This study has shown how, by focusing on processes and identifying

appropriate quality measures, it is possible to obtain such data.

Once a service organization identifies measurement techniques they should not experience any difficulties

other than those faced in the manufacturing sector.

While the techniques described here will require further development and adaptation to different service

environments, it is clear that the 'Scientific Method' corner of the Quality Triangle is as applicable to the

Hospitality industry as to other industries.



Acknowledgements

This work formed part of MAG's Masters of Commerce thesis at Bond University. The generous support

of Sheraton International and the Total Quality Management Institute of Australasia, and the willing

cooperation of staff at the Sheraton Brisbane Hotel and Towers is gratefully acknowledged.

[*] New position: Professor of Quality Manager, Key Centre in Strategic Management, Queensland

University of Technology, 2 George Street, G.P.O. Box 2434, Brisbane Q4001, Australia.



Table 1. Some possible measurements

Reservation Function

process process



Timeliness Luggage delivery Maintenance to

Waiting time pre-arranged

schedule

Quick response to

enquiries





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Integrity Towers' Check-in Function plans

Account details correspond

to customer's

perceptions

All necessary

information

on event order



Predictability Correctness of Level of service will be

information repeated at other

Efficient check-in functions



Satisfaction Reply forms Function questionnaires

Guest comments Guest comments

Comments from staff









LEADERSHIP AND TOTAL QUALITY MANAGEMENT



An Empirical Investigation of ISO Certified Companies in Sri Lanka

INTRODUCTION

Oakland (1989) argues that after the industrial revolution of the nineteenth century and the computing

revolution of the early 1980,s ―we are now without doubt in the midst of quality revolution‖ (Wilkinson,

et al, 1998). Development in the product markets; technology, and legislation have led employers to

search for new strategies and structures. Accordingly product and service quality are high on the agenda of

both private and public sector organizations with quality certification and Total Quality Management

emerging as key concerns. (Wilkinson,1996). TQM is a management approach of an organization

centered on quality based on the participation of all of its members aiming at long term success through

customer satisfaction and benefits to all members of the organization and to the society (ISO 8402).

Accordingly TQM is an organization wide approach to continuously improving the quality of all the

organizations, processes, products and services (Kotler, 2000)



Having discussed what is meant by TQM, we can look in at the two aspects of the TQM namely Hard and

Soft aspects.(Wilkinson,1998).The hard aspect reflecting the production orientation of the quality ‗gurus‘

emphasizes systems, data collection and measurement. It involves a range of production techniques

including statistical process control, changes in the layout, design processes and procedures of the





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organization, and use of the seven basic TQM tools used to interpret data. (Process Flow Charting, Tally

Charts, Pareto Analysis, Scatter Diagrams, Histograms, Control Charts and Cause and Effect

Analysis).The soft side focuses on the management of human resources in the organization and lays

particular emphasis on the need to change the culture. TQM thus emphasizes both production oriented

and employee relations oriented elements.

Accordingly the management in TQM implies that TQM is a management approach, not just a narrow

quality control or quality assurance function .It should be remembered that every one in the organization

is involved in TQM not just project head. In other words TQM is a very people oriented and has many

implications for the study and application of Organizational Behavior. An extensive review of literature

indicates that the leadership, conducive work culture and positive attitudes of employees as the major

factors that affect the excellence of TQM.

Figure: 1- Major factors which affect the excellence of TQM



Human Resource Mgt



Leadership Job attitudes Excellence of Total

Organizational Culture Quality Management

Source: Author developed



Some principles and practices of TQM may differ among firms and industries, but there is unanimous

agreement as to the importance of leadership by top management in implementing TQM. Such leadership

is a pre-requisite to all strategy and action plans .According to Juran (1989) it cannot be delegated. Those

firms that have succeeded in making total quality work for them have been able to do so because of strong

leadership (Juran, 1989) If moral integrity is fundamental to TQM, the TQM is the means by which it is

expressed. Leadership is defined in the context of TQM as providing and driving the vision.

(Mittal,1999,p:200)

Dimensions

According to Schmidt and Finnigan (1992), there are twelve behaviors that successful quality leaders

demonstrate. They were considered as the dimensions of the leadership behavior. These dimensions are:

D1 - Giving priority attention to the needs of external and internal customers

D2 - They empower rather than control

D3 - They emphasize improvement rather than Maintenance

D4 - They emphasize prevention rather than correction

D5 - They encourage collaboration rather than Competition

D6 - They train and coach, rather than direct and Supervise

D7 - They learn from problems

D8 - They continually try to improve communications

D9 - They continually demonstrate their commitment to quality

D10 - They establish organizational systems to support quality effort

D11 - They encourage and recognize team effort.



According to the theory TQM success is measured in five main areas of effectiveness, efficiency,

productivity, quality, and non quality related measures such defects, error rates, cost of poor quality and

deliveries not on time etc(Oakland, 1995, Pp.173-187) For the purpose of the research the researcher has

given his own operational definition for the success of TQM. That is TQM success is measured in terms

of employee perception of the quality. Accordingly TQM success is the perception of a person to see in

the production of the final product that, he should be educated in the process and should be participated

with full authority and self control with the intention of being innovative, so that the ultimate product or





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service to be free of errors in accordance with the current prevailing quality concepts. There are seven

dimensions identified for the measurement of success of TQM. They are:

D1 - Educative Process

D2 - Participative structures (Quality Circles, Action Teams etc.)

D3 - Greater autonomy and self control

D4 - Decreasing trend of errors towards zero defects

D5 - Adherence to quality concepts

D6 - Creativeness or innovativeness

D7 - Perception of customer satisfaction.

OBJECTIVE OF THE STUDY

The objective of this paper is to empirically investigate the impact of leadership behavior on the success of

Total Quality Management .Accordingly this paper examined the following research question.

Does the leadership behavior have an impact on the success of TQM?

METHODOLOGY

The sample

The sample of study consisted 180 executives and managers who are working under functional heads of

operations, marketing, human resources, and finance departments. The researcher used convenience

sampling in selecting the subjects. The subject community has all the characteristics of the type needed for

in-depth study of this topic.

Instrumentation

The instrument used in the study was a survey questionnaire which consists 49 questions. The leadership

behavior has been measured by a 27 item questionnaire which has been originally devised by the Xerox

for its management performance survey. (Besterfield et al ,2005,P.51) The success of TQM has been

measured by a 15 item questionnaire originally devised by the researcher for this specific study. Of 49

questions, seven were designed to gather background information of the respondents.

Data collection and analysis

Two hundred questionnaires along with a covering letter were distributed among the selected sample of

managers and executives. .It explained the purpose of the study and the importance of the participation of

the employees in responding to the questionnaires.

The first stage of data analysis involved computing descriptive statistics as frequencies and percentages for

analyzing characteristics of the subjects. Second a reliability analysis was done to check whether the

questionnaires measure the variables reliably .The Alpha values were calculated for the same purpose. If

the Alpha values are greater than 0.5, the questionnaires measure the variables reliably.(leadership

questionnaire and success of TQM questionnaire) Third a factor analysis was performed to find out the

dimensions of each variable ,how questions are grouped to dimensions, to find out whether any unwanted

questions can be eliminated from the questionnaire. Finally the simple correlation analysis was performed

to identify the relationship between leadership and success of TQM.

RESULTS

Questionnaire responses and the profile of employees

There were 185 responses from the 200 questionnaires. It is a response rate of 93%, which is at a

satisfactory level. However 180 questionnaires were selected for this analysis. It shows that 80% of the

respondents were male employees while the rest were female employees. The majority of the employees

appear to be within the age group of 36-50(60%).The highest number of respondents have been

employees with G C E (A/L) qualification (48%) while rest 30% and 22% of employees were degree

qualifications and GCE (O/L) respectively. The respondents have been from majority group having job

experience of 6-10 years. There were 85% of married and 15% unmarried employees in the sample.



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Reliability analysis and factor analysis

A reliability analysis was done to check whether each Questionnaire measure the variables reliably. The

Chronbach‘s Alpha value was measured for this purpose .The results reveal that the questionnaires

measure the variables reliably.

Table 1: Summary of Reliability Analysis

Questionnaire Alpha Value Comment

01 Leadership Behavior Questionnaire 0.7647 acceptable

02 Success of TQM Questionnaire 0.6832 acceptable

Source: Survey data

Factor analysis is a statistical procedure to take a large number of constructs and reduce them

to a smaller number of factors that describe this measure. A ‗factor‘ is a combination of questions where

shared correlation explains a certain amount of total variance. After rotation, factors are designed to

demonstrate underlying similarities between groups of variables.

. Three measures were considered for the analysis.

(1) Kaiser – Meyer – Olkin Measure of Sampling adequacy

KMO measure is acceptable, (KMO = 0.756) since it is higher than 0.5. There fore the distribution of

data is acceptable for performing the factor analysis.

(2) Bartlett‘s test of sphericity

Bartlett‘s test of sphericity : Significance = 0.000. This result is acceptable since data do

not differ significantly from multivariate normal. That is the chance to differ occurs at p =

0.000 < 0.05.

(3) Component Matrix

At the beginning there were two components. But, after extracting, two variables come

under one component. Therefore the entire set of questionnaire is unidimensional. It

means that the questionnaire has equally measured all of the variables.

Descriptives

The Standard Error of Mean (SEM) is less than 3.5% for all variables and the highest standard Error of

mean is for leadership behavior (2.6%). The success of TQM has the highest average scores. Overall

averages are above 03, and it implies that successfulness of all factors. The following table shows a

summary of the descriptives. Table 2: Summary of descriptives

Mean Standard

Deviation Remarks

Leadership 4.08 0.343 Highest dispersion: Points scattered away from mean

Success of 4.15 0.248 Points scattered some what close to the mean.

TQM

Source: Survey Data

Correlation analysis

Scatter plots were taken to identity relationship of success of TQM with the leadership. The correlation

between leadership and success of TQM is positive and significant at 1% significance level since r = 0.530

and P = 0.000.

The results of the scatter plots are commented as follows.



Table 3: Comment on scatter plots

Variables Correlation

Involved Co- Comments

efficient(r).

Leadership- 0.530 A positive correlation

Success of The points are much scattered



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TQM around a straight line

Source: Survey Data



Regression between leadership Behaviour – success of TQM.

The analysis gives s the following information.

1. R = 0.531, which means a moderate gradient regression line.

2. R2 = 0.282, means 28% of the variance of success of TQM was accounted for by

leadership.

3. Sum of squares figures explain a larger proportion of unexplained variance than explained

variance.

4. Sag F = 0.000, which shows that a particular ―F‖ value could occur by a chance of less

than 1 in 1000.



Test of Hypothesis

The hypothesis states as follows

―Leadership behavior of an organization is positively related to the success of TQM.‖

Correlation analysis explained a positive relationship between leadership and success of TQM (r = 0.531,

P = 0.000). Regression analysis also supports this by giving a value, (B3 = 0.236). Hence leadership is a

predictor of success of TQM (F = 35.189, 0.000). Hence the decision is, leadership behavior is a predictor

of success of Total Quality Management.



DISCUSSION

The study reveals a moderate relationship between leadership and success of TQM (r = 0.531, P = 0.000).

This is significant at 1% significance level. Hence leadership is reflected on success of TQM. The simple

regression analysis describes that leadership has a positive impact on success of TQM with the strength of

B = 0.384 (F = 69.423, P = 0.000). The leadership behavior gives a measure of success of TQM and it

has a 28.2% accuracy of predicting. That is 28.2% of success of TQM is accounted for, by leadership

behavior.



The distribution of the leadership shows that the Mean and standard Deviation are at favorable levels.

(Mean = 4.0789, Standard Deviation = 0.3425). The Standard Deviation shows that all senior managers‘

leadership behavior is committed on the quality at plus or minus 0.3425 Standard Deviation level.

CONCLUSIONS



As far as the role of TQM leader is concerned, every manager is responsible for quality; especially senior

management and the CEO, however, only latter can provide the leadership system to achieve results.

Senior management has numerous responsibilities .They must practice the philosophy of management by

wondering around. Management should get out of the office and visit the customers, suppliers and

departments within the organization, so that managers learn what is happening with a particular customer,

supplier or project. The idea is is to let employees think for themselves. Senior management‘s role is no

longer to make the final decision, but to make sure the team‘s decision is aligned with the quality

statements of the organization. Push problem solving and decision making to the lowest appropriate level

by delegating authority and responsibility. The needed resources must be provided to train employees in

the TQM tools and techniques, the technical requirements of the job, appropriate equipment and security.



Senior mangers must find time to celebrate the success of their organization‘s quality efforts by personally

participating in award and recognition ceremonies. One of the duties of the managers is to establish or

revise the reorganization and reward system. Senior managers must be visibly and actively engaged in the



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quality effort by serving on teams, coaching teams, and teaching seminars. They should lead by

demonstrating, communicating, and reinforcing the quality statements. As a rule of thumb, they should

spend about one third of their time on quality (Besterfield, 2003, P.32). A very important role of senior

managers if listening to internal and external customers and suppliers through visits, focus groups and

surveys. This information is translated in to core values and process improvement projects. Another very

important role is the communication. The objective is to create awareness of the importance of TQM and

provide TQM results in an ongoing manner. The TQM implementation process beings with senior

management. Leadership is essential during every phase of the implementation process and particularly at

the start.



QUALITY MANAGEMENT GLOSSARY



Affinity Diagram - A way to organize idea data into coherent patterns or themes. A large number of

ideas are generated and then organized into groupings to reveal major themes.



Authoritarian Culture - An organizational culture characterized by the holding of all power (decision

making and information) at the top of the organization. The authoritarian organization seeks to maintain

the status quo and forces workers to conform, never question or give feedback, play politics, and wait for

orders.



Benchmarking - A systematic process of continuously measuring an organization's critical business

processes against business leaders anywhere in the world to gain information which will help the

organization take action to improve its performance. Steps include Planning the Study, Collecting

Information, Analyzing Results, Implementing Improvements.



Benefit - See Outcome



Boundary - The beginning or end point in the portion of a process from a Supplier to a Customer that

will be the focus of the process improvement effort.



Brainstorming - A group decision-making technique designed to generate a large number of creative

ideas through an interactive process. Brainstorming is used to generate alternative ideas to be considered

in making decisions.



Cause and Effect Diagram - See Ishikawa Diagram.



Center Line - The line on a control chart that represents the average (mean or median) value of the items

being plotted.



Check Sheet - A data collection form consisting of multiple categories. Each category has an operational

definition and can be checked off as it occurs. Properly designed, the Check Sheet helps to summarize the

data, which is often displayed in a Pareto Chart.



Coach - A key resource person from within the organization who will support the CEO's leadership of

the CQI. A respected peer from the hospital work force who is enthusiastic and knowledgeable about

CQI, eager to learn and eager to help others learn.









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Collaborative Culture - An organizational culture characterized by a shared vision, shared leadership,

empowered workers, cooperation among organizational units as they work to improve processes, a high

degree of openness to feedback and data, and optimization of the organizational whole versus its many

parts.



Common Cause System of Variation - The collection of variables that produce common cause

variation and the interaction of those variables.



Continuous Quality Improvement (CQI) - The culture, strategies and methods necessary for continual

improvement in meeting and exceeding customers' expectations.



Control Chart - A display of data in the order that they occur with statistically determined upper and

lower limits of expected common cause variation. It is used to indicate special causes of process variation,

to monitor a process for maintenance, and to determine if process changes have had the desired effect.

One of the basic tools of the New Quality Technology.



Control limits - Expected limits of common cause variation. Sometimes they are referred to as upper and

lower control limits. They are not specification or tolerance limits.



Customer - The receiver of an output of a process, either internal or external to an organization or

corporate unit. A customer could be a person, a department, a company, etc.



Customer Data Table - A tool for translating customers' words into requirements, quality indicators and

features of the product or service.



Data Collection - Gathering facts on how a process works and/or how a process is working from the

customer's point of view. All data collection is driven by knowledge of the process and guided by

statistical principles.



Deming Cycle for Continuous Improvement - A visualization of the CQI process usually consisting of

four points - Plan, Do, Check, Act -- linked by quarter circles. The cycle was first developed by Dr. Walter

A. Shewhart but was popularized in Japan in the 1950 by Dr. W. Edwards Deming.



Deming's 14 Principles - The foundation of Deming's philosophy. The points are a blend of leadership,

management theory, and statistical concepts which highlight the responsibilities of management while

enhancing the capacities of employees.



Facilitator/Advisor - A person who has developed special expertise in the CQI process. In a CQI team,

the facilitator/advisor is not a team member but a person outside the group who serves as a process guide,

teacher of CQI methods, and consultant to the team leader, and who helps connect the work of the team

to the organization's overall CQI effort.



Fishbone Chart - See Cause and Effect Chart.



Flowchart - A graphical representation of the flow of a process. A useful way to examine how various

steps in a process relate to each other, to define the boundaries of the process, to identify

customer/supplier relationships in a process, to verify or form the appropriate team, to create common







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understanding of the process flow, to determine the current "best method" of performing the process,

and to identify redundancy, unnecessary complexity and inefficiency in a process.



FOCUS-PDCA - A strategy that provides a roadmap for continuous process improvement when linked

to a quality definition. It is an acronym meaning: Find a process to improve, Organize a team that knows

the process, Clarify current knowledge of the process, Understand sources of process variation, Select the

process improvement, Plan the improvement and continued data collection, Do the improvement, data

collection, and analysis, Check and study the results, Act to hold the gain and to continue to improve the

process.



Force Field Analysis - A systematic method of understanding competing forces that increase or decrease

the likelihood of successfully implementing change.



Future State - In an organizational transformation, the vision of where the organization will be after it is

transformed. For the transformation to CQI, the future state includes constancy of purpose, leaders who

model the new way, collaboration, customer mindedness, and a process focus.



Immediate customer - The person or unit that directly receives the output of the process.



Input - The service or product a supplier provides to a process. Inputs to one process are the outputs

from preceding processes.



Interrelationship Digraph - A way to display cause-and-effect relationships among all the elements in a

system. The relationship arrows indicate the issues/causes that are the most fundamental among all the

related items.



Ishikawa Diagram - A graphic tool used to explore and display all the factors that may influence or

cause a given outcome. (Also known as a cause and effect or fishbone diagram.)



Key Process Variable - A component of the process that has a cause and effect relationship of sufficient

magnitude with the Key Quality Characteristic such that manipulation and control of the KPV will reduce

variation of the KQC and/or change its level.



Key Quality Characteristic - The most important quality characteristics. The KQCs must be

operationally defined by combining knowledge of the customer with knowledge of the process. KQCs are

measured to understand the actual performance of the process.



Median - In a series of numbers, the median is a number which has at least half the values greater than or

equal to it and at least half of them less than or equal to it.



Meeting Process - A defined method for conducting meetings that includes specific roles and

responsibilities for a team leader, a recorder, a timekeeper, team members, and a facilitator or advisor. The

steps are 1) Clarify the objective, 2) Review roles, 3) Review the agenda, 4) Work through agenda items, 5)

Review the meeting record, 6) Plan next steps and next meeting agenda and 7) Evaluate.



Mentor - A highly skilled CQI professional with extensive training and experience in the initiation and

operation of CQI. A resource person from outside the organization or department who visits periodically







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to counsel the CEO, Coach and Quality Improvement Council in the development, implementation and

evaluation of CQI.



Multiple Voting - A group decision-making technique designed to reduce a long list to a few ideas.



Nominal Group Brainstorming - A group process technique designed to efficiently generate a large

number of ideas through input from individual group members.



Operational Definition - A description in quantifiable terms of what to measure and the steps to follow

to measure it consistently. Deming has suggested that a good operational definition includes: 1) a criterion

to be applied, 2) a way to determine whether the criterion is satisfied, and 3) a way to interpret the results

of the test. An operational definition is developed for each KQC or process variable before data is

collected.



Opportunity Statement - A concise description of a process in need of improvement, its boundaries, and

the general area of concern where a CQI Team should begin its efforts.



Outcome (Benefit) - The degree to which Outputs meet the needs and expectations of the Customer.



Output - The service or product that a customer receives from a process. The output of one process can

be the input to a succeeding process.



Owner - The person who has or is given the responsibility and authority to lead the continuing

improvement of a process. Process ownership is a designation made by leaders of organizations and

depends on the boundaries of the process.



Paradigm Shift - A point in time when the knowledge or structure which underlies a science or discipline

changes in such a fundamental way that the beliefs and behavior of the people involved in the science or

discipline are changed.



Pareto Chart - A bar graph used to arrange information in such a way that priorities for process

improvement can be established. It displays the relative importance of data and is used to direct efforts to

the biggest improvement opportunity by highlighting the vital few in contrast to the many others.



Penny Matrix - A way to prioritize a list of options by pooling the opinions of raters. Raters "spend"

their pennies across several options with the sums of "money spent" indicating a priority weighting and

ranking to the options.



Present State - In a force field analysis, the description of an organization as it currently exists. It includes

what happens in the organization, both formally and informally.



Prioritization Matrix - A way to prioritize options by requiring the raters to work to consensus on

priorities. Also known as a paired-comparison technique. The tool pairs each option with each other, with

row totals indicating weighting and ranking.



Process - A series of actions which repeatedly come together to transform Inputs provided by a Supplier

into Outputs received by a Customer. A process can be used to develop products and services.







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Process Decision Program Chart (PDPC) - A tool for improving implementation through contingency

planning. By considering "what could go wrong?", plans for prevention of problems are generated.



Process Improvement - The continuous endeavor to learn about all aspects of a process and to use this

knowledge to change the process to reduce variation and complexity and to improve customer judgments

of quality. CFI begins by understanding how customers judge quality, how processes work, and how

understanding the variation in those processes can lead to wise management action.



Process Variation - The spread of process output over time. There is variation in every process, and all

variation is caused. The causes are of two types - special or common. A process can have both types of

variation at the same time or only common cause variation. The management action necessary to improve

the process is very different in each situation.



Quality Characteristics - Characteristics of the output of a process that are important to the customer.

The identification of quality characteristics requires knowledge of the customer needs and expectations.



Quality Improvement Council (QIC) - A group composed of the Coach and the senior leadership of

an organization which is primarily responsible for planning, strategy development, deployment,

monitoring, educating, and promoting CQI.



Quality Inspection - Usually consists of three stages - sampling, measuring, and sorting. While many

organizations rely on inspection to improve quality, the better way is to design quality into the product or

service - to improve the process. This may include some inspection as a means of data gathering.



Quality Planning/Redesign - Creating new or redesigned products/services/processes to meet

customer requirements. The steps of this method are Organize the project, Identify key customers,

Determine requirements, Establish quality indicators, Design, Strengthen the design, Test the design,

Implement and improve.



Red Bead Experiment - A simple exercise to demonstrate, among other things, that many managers

hold workers to standards beyond their control, variation is part of any process, and workers work within

a system beyond their control. The game also shows that some workers will always be above average,

some average, and some below average, that the system, not the skills of individual workers, determines to

a large extent how workers in repeating processes perform, and that only management can change the

system or empower others to change it.



Refreezing - Recognizing, reinforcing, and rewarding new organizational attitudes and behaviors so they

become the norm. Making processes, systems, and methods throughout the organization support CQI.



Requirement-Indicator Matrix - A matrix that shows the presence of all possible relationships between

customer requirements and quality indicators.



Rework - The act of doing something again because it was not done right the first time. It can occur for a

variety of reasons, including insufficient planning, failure of a customer to specify the needed input, and

failure of a supplier to provide a consistently high quality output.









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Run - A point or a consecutive number of points that are above or below the central line in a run chart.

Too long a run or too many or too few runs can be evidence of the existence of special causes of

variation.



Run Chart - A display of data in the order that they occur. Run charts display process variation and can

be used to indicate special causes of process variation in the form of trends, shifts, or other non-random

patterns.



Special Cause Variation - Variation in the process that is assignable to a specific cause or causes. It

arises because of special circumstances.



Special and Common Cause System of Variation - The collection of variables that produce both

common cause variation and special cause variation and the interaction of those variables.



Spider Diagram - A visual report card for the performance of a number of indicators on a single chart.

Also know as a "radar chart" and a "gap analysis" tool, this diagram makes visible the gaps between the

current and desired performance.



Sponsor - A member of the organizational leadership who serves as an advocate or champion for a

process improvement, assists in securing resources, and gives guidance to the effort.



Storytelling - A major accelerator of the process of organization wide CQI that uses Storybooks to follow

steps in the QI or QP strategy. Storybooks and Storyboards help teams organize their work and their

presentations so others can more readily learn from them. Use of Storyboards and Storybooks reduces

variation in the process of Storytelling so the focus of learning is on content, not the method of telling.

Storybooks form a permanent record of a team's actions and achievements and all the data generated, and

Storyboards can function as the working minutes of a team.



Supplier - The party or entity responsible for an input to a process. A supplier could be a person, a

department, a company, a nursing school, etc.



Tampering - Taking action without taking into account the difference between special and common

cause variation.



Team Leader - A person designated to lead the CQI Team. An individual who has team leadership skills

and basic quality improvement skills.



Teams



 Cross-functional - A group of usually five to eight people from two or more areas of the

organization who are addressing an issue which impacts the operations of each area. For example,

the processes of meeting information requests might be addressed by a team involving PI,,

managed care and marketing staff.

 Functional - A group of five to eight people addressing an issue where any recommended changes

would not be likely to affect people outside the specific area. For example, a Functional Team

concerned with filing and retrieving data in the laboratory might consist just of people who work

in the lab.







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Transformation - A major organizational change from the present state to a new/preferred state in

which CQI flourishes. The primary steps involved in moving an organization through a transformation is

present state, unfreezing, transition period, refreezing, and new/preferred state.



Transition Period - A description of the time when an organization is visibly moving away from the old

way toward the new way. During this time, employee attitudes and behaviors range from being excited

and busy to being confused and resistant. The support for change is building. New leaders emerge,

champions of the change come forward and confusion over roles begins to clear.



Tree Diagram - A tool to expand a proposed change from a general idea to a specific series of concepts

or actions. Used to systematically map out in increasing detail the full range of paths and tasks that need to

be accomplished to achieve a primary goal and related sub goals.



Ultimate Customer - The person or unit who receives the output from a series of processes and for who

these processes are designed. Without the ultimate customer, there would be no need for the intermediate

processes to exist.



Unfreezing - Reassessing old values and behaviors and becoming open to the acceptance of a new

culture.









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