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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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