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					Understanding Manufacturing Process Variability
                   IE 285
             November 14, 2002
                  Presented by
                 Toni L. Doolen
                        Who I am
 Education   –
   BS in BS in Material Science and Engineering and Electrical
    Engineering at Cornell University
   MS in Manufacturing Systems Engineering at Stanford
    University
   PhD in Industrial Engineering at Oregon State University

 Work    Experience – 13 years at Hewlett Packard Co
   Fab (semiconductor manufacturing) process engineer
   Senior member of technical staff working on process control
    implementation for inkjet cartridge assembly lines world-wide
   Systems engineering manager responsible for hardware and
    software development associated with the design, build and
    qualification of customized automated equipment
                         Who I am
 Research  interests include manufacturing systems; human
  factors engineering; and statistical analysis
 Joined OSU Industrial and Manufacturing Engineering
  faculty in June 2001
 Outside of work
    Married  with 3 children (ages 9, 7, and 7). My husband is a
     manager at Hewlett Packard
    Golf (whenever I get the chance)
    Reading, cooking, violin (and going to lots of soccer games and
     gymnastic meets )
    Active in Society of Women Engineers – particularly in
     outreach to encourage girls to consider engineering careers
Processes, Variation, & Measurement
                     Process

A process is a set of related activities that are based
on a set of inputs and result in outputs that have
added value. A process includes people, equipment,
materials, methods, and environment that work
together to produce output. A process is how we
create products and services.
        A Simple Process Example

Making spaghetti
     What are the related activities?
     What are some inputs?
     What are the outputs?
     What is the added value of the process?
                    Variation

 In the industrial and business world, no two things
  are ever exactly alike…this is why engineers
  include tolerances in specifications
 Variation exists in products, services, and the
  processes used to create them
 In trying to understand the causes of variation and
  predict the occurrences of variation, it is necessary
  to measure variation
        A Simple Process Example

Making spaghetti
     What are some of things that might vary from one
      batch of spaghetti to the next?
     What are some of the reasons for this variation?
      Processes and Performance
            Measurement
"You can't control what you don't measure".
 (Deming, W.E. Out of the Crisis. Cambridge, MA: MIT, 1986. )


Without measurement there is no way to know how a
process is performing; therefore there is no way to
improve it. By measuring the voice of the customer and
the voice of the process, gaps can be identified between
the two. This information gives us direction in our
improvement efforts as we begin closing the gap.
              A Simple Example

Making spaghetti
     What performance measures are related to making
      spaghetti?
     What is the voice of the customer?
     What is the voice of the process?
Process Control
           Measures of location

 The average and mean are the same quantity. The
  average (mean) of 6, 9, 10, 11, and 13 is
  (6+9+10+11+13)/5 = 9.8
 The median is found by listing data from high to
  low and finding the value that is the middle.
  The median of 6, 9, 10, 11, 13 is 10
                     Variation

 The  difference in the reproducibility of a particular
  action. The difference between a particular action
  and the target outcome
 Random variation – stable consistent patterns of
  variation over time aka controlled variation,
  natural variation
 Nonrandom variation – patterns of variation that
  change over time aka special or assignable cause
  variation
        Deming on Variation

“If I had to reduce my message to management
to just a few words, I’d say it all had to do with
variation”
                                     Deming (1982)
               Measures of variation
 The  range of 6, 9, 10, 11, and 13 is the highest value
  minus the lowest value  13 - 6 = 7
 The variance is calculated by looking at the “average”
  difference between each value and the overall average of
  the data

   [(6 – 9.8)2 + (9 – 9.8)2 + (10 – 9.8)2 + (11 – 9.8)2 + (13 – 9.8)2 ]
   /(5-1) = 6.7

 The  standard deviation is the square root of the
  variance2.59
           Inspecting in Quality

  A traditional approach in manufacturing is to
  depend on production to make a product that
  meets customer requirements and to use inspection
  of the final product as a gate to make sure the
  customer gets what they want.
 This approach is wasteful since it
  allows time and material to be
  invested even when the product or
  service might not be usable
                    Prevention

    Process control methodologies such as design of
    experiments, control charts, and gage studies
    enable us to study, characterize, optimize, and
    understand our processes and gain confidence that
    our process is producing a product or service that
    will meet the customer requirements
            Designed Experiments
 In studying existing processes or developing new
  processes, we need to understand the relationship between
  inputs and outputs.
 Statistically designed experiments provide us with a
  structured procedure for obtaining the most information
  possible on these relationships with a minimum sized
  experiment.
 Statistically designed experiments allow us to construct
  experiments to test the relationships and relative impact of
  multiple process characteristics with process outputs
                   Control Charts

 The  goal of using control charts to monitor key attributes
  of a process is to determine when the process is operating
  in control (only random variation) vs. when we need to
  take action because special or assignable cause variation
  is present
 If we can identify and eliminate special causes, the
  process will be in control and we can use statistical
  analyses to predict its behavior; so we will know whether
  or not we can meet customer requirements
Gage Studies
                Gage Studies

A   gage is a measurement tool or system
 In studying, characterizing, optimizing, and
  controlling our processes, we measure important
  characteristics or attributes about our process
 We use gages to measure these attributes
 Gages may be simple (a ruler) or extremely
  complex (ellipsometer), but all gages share one
  thing – variation
 Measurement System Components

A measurement system typically includes the
 following components:
  An operator
  A reference (often called a standard)
  A procedure
  A gage
  An environment
 Variability in Measurement Systems

 As with any other manufacturing process, a
  measurement system is subject to variability
  which can be either random or special cause in
  nature.
 Examples of special causes might be
   Untrained operators
   Uncalibrated gages
   Lack of procedures
     Measurement System Analysis

 The goal of a measurement system analysis is to
  detect and eliminate sources of variation that are a
  result of the system used to measure product or
  process attributes.
 We strive to have measurements systems that are
   Accurate
   Precise
   Capable
                  Definitions

 Accuracy:  The difference between the observed
  average value of measurements and the true value
 Precision: Degree of agreement between
  individual measurements on a specific sample –
  composed of repeatability and reproducibility
 Capability: The total variability of measurements;
  measured using a Precision/Tolerance (P/T) ratio
                    Definitions
 Repeatability: Variation in measurements obtained with
  one gage when used several times by one operator while
  measuring identical characteristics on the same parts.
 Reproducibility: Variation in the average of the
  measurements made by different operators using the same
  gage when measuring identical characteristics on the same
  parts.
 P/T Ratio: Ratio between the precision and the tolerance
  (specification window) for the characteristic being
  measured
        Assessing Gage Acceptability

 P/T Ratio less than 10% -- gage is acceptable
 P/T Ratio between 10 and 30% -- gage may be
  acceptable, depending on importance of
  application, cost of gage, cost of repairs, etc.
 P/T Ratio over 30% -- Gage system needs
  improvement or to be replaced
Ishikawa Diagrams
              Sources of Variation

 Materials
 Methods
 Machines
 Measurements
 Humans
 Environment
             Ishikawa diagrams

 Ishikawa  diagrams are also called cause/effect
  diagrams and fishbone diagrams
 This type of diagram can provide a foundation to
  break down a complex process into manageable
  factors. You can then generate ideas for potential
  sources of variation
 The basic diagram looks like a fish skeleton, with
  a main idea forming the backbone and connecting
  ideas forming the smaller bones.
Fishbone diagram example
      Fishbone diagram how-to’s (1)

1.   Clearly define the effect or symptom for which
     the causes must be identified.
2.   Place the effect or symptom being explored at
     the right, enclosed in a box.
3.   Draw the central spine as a thick line pointing to
     it from the left.
4.   Brainstorm to identify the "major categories" of
     possible causes using the 6 “sources
     of variation”
      Fishbone diagram how-to’s (2)

5.   Place each major category in a box and connect
     it to the central spine.
6.   Within each major category, ask "Why does this
     happen? Why does this condition exist?"
7.   Continue to add clauses to each branch until the
     fishbone is completed.
8.   Once all the bones have been completed, identify
     the likely, actionable root cause.
        A Simple Process Example

Making spaghetti
     Create an Ishikawa diagram to identify possible
      sources of variation that may lead to “bad spaghetti.”

				
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