Integrating SG Centurion Into a Six Sigma Program

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Integrating SG Centurion Into a Six Sigma Program Powered By Docstoc
					INTEGRATING STATGRAPHICS CENTURION XVI
INTO A SIX SIGMA PROGRAM




STATPOINT TECHNOLOGIES, INC.

Warrenton, Virginia
Phone: 1-800-232-7828 or 1-540-428-0084
Fax: 1-540-428-0089
E-mail: info@statgraphics.com
Web site: www.statgraphics.com
Introduction
This white paper discusses how integrating the STATGRAPHICS Centurion statistical tools
into a Six Sigma program can provide ongoing benefits across an enterprise. Starting with a
brief overview of the Six Sigma philosophy, the discussion then focuses on how the
analytical and reporting tools within the STATGRAPHICS Centurion software can be used
to implement key components of the Six Sigma approach.


What is the Six Sigma Philosophy?

The "Six Sigma" quality approach was pioneered in the 1980s by Motorola in response to a
rising tide of global competition. Following a top-to-bottom analysis of its business
operations, Motorola adopted a set of processes geared toward reducing the time required to
take a product from concept to manufacture while simultaneously reducing the defects in
products distributed to its customers.

Since then the Six Sigma approach has become a widely used management strategy for
initiating a comprehensive review of all the processes a company undertakes to create,
market, and support its products and services. A rule-of-thumb is that average processes
operate at a three-sigma level; best-in-class at six sigma. Generally, the fundamental
objective is that products and processes experience only 3.4 defects per million
opportunities.

The benefits of a successful Six Sigma program are many: cost savings, productivity gains,
improved production cycle times, reduction in errors, and elimination of unnecessary work.
Better processes drive top-line growth, increase operating margins, and reduce working
capital and spending.


Implementing Six Sigma

From the STATGRAPHICS Centurion perspective, implementing a Six Sigma program
means giving a company's employees the most powerful statistical tools available to
achieve their goals. These tools should serve both as a guide for improving processes by
identifying trends away from set quality standards and as an archive for storing process
information for future reference.

A company committing itself to a Six Sigma quality program must put in place an
intensive training program for key executives and staff. In turn, these people learn to (1)
organize and effectively lead the deployment of the program, and (2) implement and use
statistical tools in their business-improvement efforts.




                                                                                              1
The STATGRAPHICS Centurion Statistical Toolkit
An important goal of this white paper is to illustrate the impact that using
STATGRAPHICS can have within an organization. Six Sigma training involves teaching
employees how key statistical tools are combined and sequenced to form a methodical and
repeatable process for solving vital manufacturing, engineering and administrative
problems. These tools should help users gain a better understanding of descriptive
statistics and the relationship between variables.

Since its development in the early 1980s, STATGRAPHICS has concentrated on providing
statistical tools that can be used both to (1) design quality into products, and (2) ensure
that acceptable quality is maintained throughout the production process. This approach
fits well with the broad Six Sigma mandate to develop an in-depth understanding of the
philosophy as well as the theory, tactics, strategy, and application tools.

STATGRAPHICS Centurion is one of the few available statistical software applications that
is flexible enough to provide entry-level access for virtually everyone within a company,
while still ensuring that high-level statistical algorithms are available for addressing
complex issues and calculations. It is a statistical software package that will serve a range
of users from machine operators and shop floor supervisors to design and process engineers.




                                                                                              2
To provide this wide latitude of functionality, STATGRAPHICS Centurion incorporates
a number of unique features:

StatWizard -- A tool to guide novice or casual users through the creation of an analysis
from the selection of data to the choice of analytical options.

StatAdvisor -- A feature that provides short and easy-to-understand interpretations of
all the reports and graphs in a statistical analysis.

StatFolios -- The main mechanism within STATGRAPHICS Centurion for saving
information about analyses and their related data. An alternative to a macro language,
this dialog-box driven approach allows users to save a single analysis or a combination of
analyses that can encompass the most complex calculations.

StatGallery -- A special tool for archival and report-generation purposes. Up to nine
graphics panes can be arranged on a single page. An overlay feature allows users to
create compound graphics. This is an ideal tool for comparing month-to-month or year-
to-year data.

StatReporter -- A reporting tool that is accessible from within a STATGRAPHICS Centurion
session. Users can combine tables, graphs and their own notes into a personalized report.
Using the Paste-Link feature, the StatReporter information updates whenever you update
the linked analysis.

StatPublish -- A reporting tool that allows output to be saved in HTML format on a server
so that anyone within the organization can view the results using only a web browser.

These features, plus others, embrace a new way of thinking about completing tasks
quickly and efficiently. They can be a powerful force in decision making or empowering
employees.

The following sections provide explanations and examples of how various analyses support
multiple aspects of the goals of a Six Sigma implementation: methods for monitoring,
controlling, and improving a process through statistical analysis.


DMAIC Menu
The Six Sigma menu option in STATGRAPHICS Centurion organizes the statistical
procedures into sections according to the Six Sigma DMAIC strategy. DMAIC groups the
activities in a typical Six Sigma program into 5 phases: Define, Measure, Analyze, Improve,
and Control.


Define

The first phase in applying Six Sigma is to define the needs of one’s customers and to
determine the relationship between those needs and key process parameters. The key
STATGRAPHICS procedures in this phase are:



                                                                                              3
Quality Function Deployment - creates and displays a QFD matrix in the form of a
“House of Quality”. QFD is a customer-driven planning process by which products and
services are matched to the needs of customers. Beginning with a set of customer needs,
design requirements are established and the relationships between the needs and
requirements identified.
                                      Interactions
                                        Strong Positive
                                        Positive
                                        Negative
                                        Strong Negative


                           Relationship
                            Strong = 9.0



                                                                                          Module Cost




                                                                                                                      Op. Period




                                                                                                                                                                                              Availability
                                                                  Capability
                            Medium = 3.0




                                                                                                                                                                     Flexibility
                                                                                                                      Fail Free




                                                                                                                                                                                   Maintain
                                                                  Process




                                                                                                                                                                     Process




                                                                                                                                                                                              of Array
                                                                                                                                                                                   Cost to




                                                                                                                                                                                                             Priority
                                                                                                                                                          Weight
                            Weak = 1.0




                                                                                                                                                                                                                               Theirs
                                                                                                        MTBF
                                                          Yield




                                                                                                                                                                                                                        Ours
                                                                               Rate




                                                                                                                                           Size
 Customer Demands




                    Low Cost                                                                                                   10.0 2.7 4.0
                    Reproducible                                                                                               10.0 2.9 5.0
                    Factory Implementation/Demo.                                                                               10.0 2.1 3.2
                    Process Verification (Proof)                                                                               10.0 3.1 3.0
                    High Reliability (>85,000) hrs.                                                                             9.0 2.6 3.3
                    Small Size - Light Weight                                                                                   8.0 2.4 2.8
                    Performance Expandable                                                                                      6.0 3.5 1.8
                    Meets Module Elect. Performance                                                                             5.0 3.6 2.1
                    System Maintainability                                                                                      1.0 3.5 3.5
                                              Absolute weight 49.0 60.0 56.0 48.0 22.0 22.0 15.0 11.0 16.0 21.0 19.0 69.0 26.4 28.7
                                               Relative weight 438.0 504.0 500.0 428.0 123.0 123.0 113.0 88.0 122.0 113.0 95.0 345.0 193.1 228.9
                                                                                                                                           4"x.46"x0.2"
                                                                                                        85,000 hrs.

                                                                                                                        10,000 hrs.
                                                                               1000/day




                                                                                                                                                          1 OZ.
                                                                                          $200
                                                          80%

                                                                    1.33




                                                                                                                                                                       TBD

                                                                                                                                                                                    TBD

                                                                                                                                                                                                TBD
                                                                                               Design Requirements



Process Map - used to map the critical steps involved in process development, in
reengineering efforts, in monitoring quality, and in many other areas.

                                                                                                                                      1                            Flow Chart
                                                                                                                                                                    Process

                                                                                                                                      2                              Audit
                                                                                                                                                                    Process

                                           4             Correct                                        Yes                           3
                                                                                                                                                                   Problems?
                                                        Problems
                                                                                                                                                                           No
                                                                                                                                      5
                                                                                                                                                              Collect Data

                                                                                                                                      6
                                                                                                                                                             Analyze Data


                                           13                                                      No                                 7
                                                         Meeting                                                                                                   In Control?
                                                                                                                                                                        Yes
                                                                                                                                      8                               Plot
                                                                                                                                                                   Histogram

                                                                                                                                      9                   Estimate Process
                                                                                                                                                              Capability

                                           11                                                            No                           10
                                                       Sort Product                                                                                                Capable?
                                                                                                                                                                        Yes
                                                                                                                                      12                            Improve
                                                                                                                                                                   Capability




                                                                                                                                                                                                                                        4
Cost of Quality Trend Analysis - illustrates the costs of poor quality by constructing a
chart displaying prevention, appraisal, and failure costs over time. In addition, statistical
runs tests are performed to determine whether or not significant trends exist in any of
those time series.

                                                     Cost of Quality History
                             80
                                                                                                                              Prevention
                                                                                                                              Appraisal
                             60
                                                                                                                              Int. Failure
                                                                                                                              Total COQ
                     COQ



                             40                                                                                               Total Failure

                             20

                              0
                                       1993
                                              1994
                                                       1995
                                                              1996
                                                                      1997
                                                                             1998
                                                                                    1999
                                                                                           2000
                                                                                                  2001
                                                                                                         2002
                                                                                                                2003
                                                                                                                       2004
Pareto Analysis - a statistical procedure that seeks to discover from an analysis of defect
reports or customer complaints which “vital few” causes are responsible for most of the
reported problems. The old adage states that 80% of reported problems can usually be
traced to 20% of the various underlying causes. By concentrating improvement efforts on
rectifying the vital 20%, you can have the greatest immediate impact on product quality.

                                            Pareto Chart for Frequency
                            180                                                                              163 164 165166
                                                                                               158 160 162
                                                                           148   151 154 156
                            150                              135   140 144
                                                       129
                                                 116
                            120
                frequency




                                            99
                            90
                                       70
                            60
                                  36
                            30
                             0
                                     Processing out of order
                                                Film on parts
                                         Machining problems




                                           Paint out of limits
                                        Incorrect dimensions



                                         Masking insufficient
                                             Parts damaged




                                             Misaligned weld
                                            Adhesive failure




                                             Voids in casting




                                            Powdery adoline
                                            Salt-spray failure

                                           Unfinished fairing
                                          Wrong part issued




                                      Primer cans damaged
                                     Delaminated composite
                                       Supplied parts rusted




                                    Improper test procedure

                                  Paint damaged by etching




                                                                                                                                              5
Cause and Effect Diagrams - illustrates the causes of a problem or effect by creating a
diagram resembling the skeleton of a fish. It is often used to help identify the factors that
need to be corrected. The diagram may also be used to display the variables that have an
effect on a response that is to be optimized.


             Raw card                         Solder process                      Inspection
                                                              Splatter
                                                              Chain speed
                              Short circuit                                                    Measurement
                                                             Height
                                                                Wave pum  p
                                Shroud                       Flow                               Test coverage
                                                                 Flux
                               Time
                                 M oisture content              Setup                            Inspector
                               Tem perature                       Tem  perature
                                                                Control
                                                                                                                        Circuit board defects
                                                                                    Wrong component
                                 Wrong part                                        Missing component

                                Functional failure                          Autoposition
                                                                              Alignm ent
                            Vendor                                          Operator
                              M issing fromreel
                            Setup                                            Crimp

             Components                                     Component insertion




Measure
The second phase in the DMAIC strategy is one in which measurements are taken of
process performance. The resulting data is displayed in different ways to show how well the
process is operating.

Run Chart - plots data in sequential order. Tests may also be performed on the data to
determine whether they represent a random series, or whether there is evidence of mixing,
clustering, oscillation, or trending.




                                                                 Run Chart
                              300


                              280
                 strength




                              260
                                                                                                                     median = 255.0

                              240


                              220
                                    0:00             3:20         6:40            10:00        13:20         16:40

                                                                         time



                                                                                                                                                6
Scatterplots – plots of raw measurement data. When multiple variables have been
measured, a matrix plot can be very helpful. The diagonal of the matrix contains box-and-
whisker plots for each variable. The off-diagonal positions contain 2-variable scatterplots
for all pairs of variables. From the plot, one can often detect relationships amongst the
variables, the presence of outliers, and other interesting features of the data.



                Horsepower



                                        Luggage



                                                    Mid Price


                                                                   MPG City



                                                                              MPG Highway



                                                                                             Weight




Exploratory Plots – used to illustrate different aspects of the data. A bubble chart plots
two variables on the axes and displays the values of two others through the color and size of
the points.




                                            Bubble Chart for Horsepower
                                50                                                           Passengers
                                                                                               2
                                45
                                                                                               4
                  MPG Highway




                                40                                                             5
                                                                                               6
                                35                                                             7
                                                                                               8
                                30

                                25

                                20
                                     1600    2100   2600        3100   3600    4100   4600

                                                           Weight




                                                                                                          7
Gage Studies – used to estimate the repeatability and reproducibility of a measurement
system. It also estimates important quantities such as the total variation, the precision-to-
tolerance ratio, the standard deviation of the measurement error, and the percent of study
contribution from various error components. Before embarking on any Six Sigma program,
it is vital to insure that one’s measurement system is able to measure adequately the
variables that are critical to process quality.




                                 Gage Measurements by Appraiser
                          2.8                                                           Operators
                                                                                          A
                          1.8                                                             B
                                                                                          C
                Average




                          0.8

                          -0.2

                          -1.2

                          -2.2
                                      1   2   3      4    5    6     7   8   9 10
                                                              Part


Sample Size Determination – calculates the required sample size for describing a
process, for comparing two or more alternatives, for constructing a control chart, or for
designing an experiment. Finding the amount of data needed so that the statistical
procedures will have sufficient power while staying within budget is of obvious importance.



                                                     Power Curve
                          100                      Factorial 2^5
                                                   Half fraction 2^5-1
                           80                      Mixed level fraction 3*2^4-1
                           60
                  Power




                           40

                           20

                            0
                                 -2           -1               0             1      2
                                                         True Effect




                                                                                                    8
Analyze

The third phase in the DMAIC strategy is one in which statistical methods are used to
analyze the data collected in the Measurement phase.

Capability Analysis – compares a sample of measurements collected from a process to
established specification limits for that variable. An estimate is derived of the percentage of
items likely to be out of spec. Also calculated are a variety of capability indices that
compare the observed performance to the specification limits. Methods are available for
handling data from both normal and non-normal distributions.


                                            Process Capability for strength
                                                                                         Normal
                                          LSL = 200.0, Nominal = 250.0, USL = 300.0      Mean=254.64
                               25
                                                                                         Std. Dev.=10.6823
                               20
                                                                                         Cp = 1.64
                                                                                         Pp = 1.56
                   frequency




                               15
                                                                                         Cpk = 1.49
                                                                                         Ppk = 1.42
                               10
                                                                                         K = 0.09
                                5

                                0
                                     200       220      240     260      280     300
                                                          strength

Outlier Identification – helps determine whether or not a sample of numeric
observations contains outliers. An “outlier” is an observation that does not come from the
same distribution as the rest of the sample. Both graphical methods and formal statistical
tests are provided.


                                            Outlier Plot with Sigma Limits
                                    Sample mean = 98.2492, std. deviation = 0.733183
                               103


                               101                                                      4
                 Temperature




                                                                                        3
                                                                                        2
                               99                                                       1
                                                                                        0
                                                                                        -1
                               97                                                       -2
                                                                                        -3
                               95                                                       -4
                                      0         30       60      90      120      150

                                                        Row number




                                                                                                             9
Comparison of Two Samples – performs statistical tests to determine whether or not
there are significant differences between the populations from which the two samples were
taken. Such tests are widely used, as when comparing a new treatment with an old
treatment, when comparing a test agent against a control, or when comparing performance
at two different locations.



                                                             Box-and-Whisker Plot


                                      Placebo




                  Test Agent



                                                 93    103      113     123      133    143    153




Weibull Analysis – used to analyze data representing lifetimes or times until failure. The
data often include censoring, in which some failure times are not known exactly due to
removals of items from the test. Estimates of critical quantities such as the 90th percentile
(P90 value) can be obtained together with confidence limits.



                                                                 Weibull Plot
                                         99.9
                                           99 90.0
                                           90                                                          Est.: MLE
                 cumulative percent




                                           70                                                          Shape: 3.16047
                                           50                                                          Scale: 27718.7
                                           30
                                           20                                                          Threshold: 0.0
                                           10                                                          Failures: 11
                                            5
                                                                                                       Sample size: 38
                                            1
                                          0.5

                                          0.1
                                                                                       36089.5 [29146.8,56446.6]
                                                1000                   10000                  100000
                                                                      Distance




                                                                                                                         10
Improve

After analyzing the current state of a process, the next stage seeks to improve its quality.
This stage involves the construction of statistical models expressing the relationship
between key quality measurements and controllable factors. A vital technique at this stage
is the statistical design of experiments, which insures that the most information possible is
obtained from the smallest expenditure of time and money.

Variance Components Analysis – experiments designed to estimate the contribution to
the variance of a process introduced at different points in the process. Such experiments are
often performed to determine where to focus subsequent experiments in order to have the
biggest impact on overall process variability.

                                Component Deviation Plot for moisture



                      batch                                                       19.49%


                    sample                                                        78.01%


                        test                                                      2.51%
                                -14   -9        -4        1        6    11   16
                                               deviation from mean



Screening Experiments – used to identify the factors that have the greatest impact on
the quality of goods and services. These experiments usually involve many factors, some of
which may interact with each other.



                                Standardized Pareto Chart for reacted
                   B:catalyst
              D:temperature                                                          +
                          BD                                                         -
                          DE
             E:concentration
                          CE
                  A:feed rate
                          BC
                          AB
                          BE
                          AE
                          AD
                          AC
                          CD
                  C:agitation
                                0          5         10       15       20    25
                                               Standardized effect


                                                                                           11
Optimization Experiments – used to find the ideal combination of controllable factors.
The output of these experiments is a statistical model that can be graphed and
mathematically optimized.




                                             Estimated Response Surface                 strength
                                                        polyethylene=1.1                    5.0
                                                                                            6.0
                                                                                            7.0
                                                                                            8.0
                               12.3                                                         9.0
                               11.3                                                         10.0
                               10.3                                                         11.0
                    strength




                                9.3
                                8.3
                                7.3
                                6.3                                                64
                                                                               5861
                                5.3                                          55
                                   220      240                          4952
                                                  260                  46
                                                          280    300        cooling bar temperature
                                         sealing temperature




Mixture Designs – used to find optimal percentages of the components in blending
problems. Special designs are needed due to constraints imposed on the sum of the
components.




                           Contours of Estimated Response Surface
                                                     fuel=50.0                           burn rate
                                                                                            30.0
                                                                                            40.0
                                                                                            50.0
                                                                                            60.0
                                binder=20.0                            oxidizer=20.0        70.0
                                                                                            80.0
                                                                                            90.0
                                                                                            100.0
                                                                                            110.0

              oxidizer=40.0                          fuel=30.0                     binder=40.0




                                                                                                      12
Control

Once process improvements have been made, it is vital that real-time monitoring be put in
place to insure that the system does not return to its earlier behavior. Phase II control
charts that plot data in real-time are important tools in this phase. Forecasting future
behavior is also important, so that corrective action may be taken before problems arise.

EWMA Control Charts – plots a weighted moving average of recent observations collected
from the process. Such charts are generally superior to simple X Charts or X-bar Charts,
since they can detect small shifts from the target mean more quickly.


                                           EWMA Chart for X
                        10.8
                                                                                 UCL = 10.62
                        10.5                                                     CTR = 10.00
                                                                                 LCL = 9.38
                 EWMA




                        10.2

                         9.9

                         9.6

                         9.3
                               0      5      10       15        20   25    30
                                                  Observation


ARIMA Control Charts – used to monitor processes which are sampled at short
increments of time. In such cases, consecutive observations are often serially correlated, so
that typical control charts that assume independence between consecutive observations
give too many false alarms.

                                   ARIMA Chart for Concentration
                         19
                                                                                 UCL = 17.63
                                                                                 CTR = 17.01
                         18                                                      LCL = 15.63
                   X




                         17


                         16


                         15
                               0      20     40       60        80   100   120

                                                  Observation




                                                                                               13
Neural Network Classifiers – a nonparametric method for classifying observations into
one of g groups based on p observed quantitative variables. When the quality of a product
depends on multiple variables, separating good items from bad items often requires such a
multivariate approach.


                                                         Classification Plot
                                        beryllium=0.3,saturated hydrocarbons=6.3,aromatic hydrocarbons=10.3
                                   60
                                                                                                              Grade 1
                                   50                                                                         Grade 2
                                   40                                                                         Grade 3
                 iron




                                   30
                                   20

                                   10
                                    0
                                          0         2          4        6         8          10       12
                                                                   vanadium



Automatic Forecasting – used to predict future behavior of a process so that adjustments
may be made if needed. Automatic procedures fit many different statistical models and
select the one that fits the historical behavior most closely.


                                        Time Sequence Plot for Concentration
                                                          ARIMA(2,0,1) with constant
                                   18.4
                                                                                                              actual
                                    18                                                                        forecast
                                                                                                              95.0% limits
                   Concentration




                                   17.6

                                   17.2

                                   16.8

                                   16.4

                                    16
                                          0:00          3:00           6:00           9:00           12:00




                                                                                                                             14
Conclusions
To compete in the world market, companies have to move toward a Six Sigma level of
performance. The preceding has been a very brief overview of a few of the more than 150
statistical tools available in STATGRAPHICS Centurion XV that can help achieve that
goal.

In practice, integrating STATGRAPHICS Centurion into a Six Sigma program should
translate into cost reduction and profit improvement because:

   •   It helps define specification limits and set realistic tolerances for machines and
       process variables through the use of capability indices.

   •   It helps companies set up a plan of action for processes that highlight out-of-control
       conditions and helps establish preventive maintenance controls to ensure that
       products meet specification requirements.

   •   It helps provide prevention plans during production with techniques for establishing
       and controlling critical machine parameters and product characteristics.

   •   It provides techniques that can reduce setup and process variability, and helps to
       standardize the use of SPC methodology.

   •   It contains methodologies for optimizing processes; for example, Design of
       Experiments techniques to identify and reduce causes of variation and to improve
       product/process manufacturability, design, quality, and functionality.

STATGRAPHICS Centurion can be an important component of any company's Six Sigma
program. The combination of text and graphical information, accessed through an easy-to-
use interface, addresses the largest impediments to success for any Six Sigma program by
keeping all employees in the company engaged in the process of quality assurance.
STATGRAPHICS Centurion provides Six Sigma statistical analysis and reporting for
everyone.




                                                                                            15

				
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