Shain in Taguchi

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Sigma Science Inc. Ross (303) 494-8521

Comparison of Selected Quality Improvement

Methodologies:

Shainin, Taguchi and Classical

Bill RossSix Sigma Associates

Introduction

In the rush toward continuous improvement there is a strong tendency to believe one

guru has all the answers. This comes about in large part due to wanting an easy cook

book approach which will not impact the current comfort zone of many companies and

groups. Unfortunately, many gurus (and their followers) are quick to fill this need. This

motivates gurus to not only push their approach, but denigrate others. In reality, the more

conversant a company is in each methodology the greater the companys choices to apply

those methodologies effectively.

This paper will provide a greatly simplifiedexamination of selected elements (tools and

techniques) considered part of the philosophies of Dr. Genichi Taguchi and Dorian Shainin.

In addition, a simplistic comparison will be made to classical philosophies. Where

applicable, the author will provide assessment of proposed methodologies.


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Comparison of Selected Quality Improvement Methodologies:


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Shainin Methods

Dorian Shainin, a quality consultant, prescribes methodologies that include, but are notlimited to, the following:

1. Sources of variation.

The core of Shainins methods is identifying the source of variation. Shainin focuses on

identifying the vital few factors (product or process parameters) that have a significant effect

on some desired output. He employs multi-vari studies, component search and variable

search techniques to accomplish this.

Assessment: Identification of the sources of variation (i.e., cause of the problem) is vital

in any methodology. Shainins methods rely heavily on vast engineering knowledge and

experience. The multi-vari study is a simple graphical technique to assist in identifying

families of variation (i.e., positional, cyclical, temporal), and is a technique that should be

used. The component and variable search techniques, however, are suspect:

! Assumptions are questionable (partial list):

Best and worst levels of a parameter are known. Sample is representative of the total process (inference space). Relationships that exist are linear. Noise factors are constant through time.! Statistical validity only good on samples tested.

! Efficiency not much better than one-factor-at-a-time experimentation.

! May isolate parameters, but not lead to improvement.

! Does not offer a direction to go in for continuous improvement.


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2. Process control.

Shainin believes control charts are useless. He recommends a technique called pre-

control to detect product discrepancies (pre-control depends heavily on item 4 discussed

herein).

Assessment: Pre-control is a part control tool to determine conformance to specification

and is not useful in reducing variation from target. It assumes a highly capable (Cpk > 1.5)

process that is in control. Pre-control may be useful for a process that is very mature

(optimized and in control), but is not recommended otherwise.

3. Process improvement.

Shainin uses classical methods of model building. Full factorial experiments are the key

tool used (these are classical methods).

Assessment: Full factorials have been proven to provide sufficient information for

building linear models complete with interactions. Linear models, however, may not allow us

to achieve maximums or optimal conditions. For this classical methods such as EVOP,

response surface methods and central composite designs are highly effective.

4. Tolerancing (for both product and process parameters).

Identifying appropriate tolerances is another important aspect of Shainin methods as

tolerances become critical to decision making. Shainin uses iso-plots and tolerance

parallelograms (a type of tolerance correlation study) to determine proper tolerances.

Included in the iso-plots methodology is a quantification of measurement system variation.

Assessment: Excellent idea to be used by product and process design engineers. Tools

are simple to use but effective. However, the measurement system evaluation method is not

extensive enough to give substantial enough information for improvement.


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Taguchi Methods

1. Sources of variation.

Taguchi has been a leader in popularizing the reduction of variation around some target

value. He has gone so far as to develop a measurement system, the loss function, to quantify

the cost associated with variation from target. Taguchi uses engineering knowledge combined

with orthogonal arrays (fractional factorials) to effectively identify sources of variation and

then reduce variation. Key in his philosophy is the identification of response variables

robust to interactions.

Assessment: Taguchis methods, albeit not the most statistically efficient, are effective at

identifying sources of variation (main effects) and setting those sources to reduce variation

and achieve target in some response variable. A key to his methods is the concept of robust

process where his experiments occur in a noisy environment (inner and outer arrays). This

may be somewhat complicated, but has proven effective. Taguchis focus on efficient

response variables is an excellent, but poorly understood, idea.

2. Process control.

Taguchi has been very innovative in his approach to process control. Rather than take

action on unexpected variation, he uses a technique he calls on-line control. With on-line

control, economic limits (based on the loss function) are determined and any variation from

target is acted upon. Key in this method is the identification of the costs of measurement and

adjustment as well as the correct amount of adjustment to achieve target.

Assessment: Although this technique has not been extensively documented, it is worthy of

investigation. The importance of developing a good model of the loss function, however, may

preclude this technique being used in the near future. The technique will work well with

automated process control methods.

3. Process improvement


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Taguchis methods focus not on developing the best model (as is the case with classical

methods), but on getting something to work better quickly. He employs the use of fully

saturated, multi-level designs run in the face of noise (while noise parameters are varied).

Assessment: Perhaps Taguchi attempts to do too much, too quickly. There are

significant risks associated with his methods (i.e., high level of confounding, poor models,

poor prediction possible), however his methods have had limited success. Care must be given

to address these risks. In the hands of a novice these methods can have disastrous

consequences. Conceptually his ideas are excellent, but some of the tools and techniques can

be improved upon (using classical statistical methods).

4. Tolerancing.

Taguchi believes this should be the last focus of quality efforts (after system and

parameter design are completed). Changes in tolerance can be costly.

Assessment: His tolerance design methodology is an excellent strategy for:

a. Selecting parameters that need tolerance control.

b. Identifying the appropriate levels.

c. Recognizing costs associated with tight tolerances.


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Summary Comparison

The following matrix will take a simplified look at key items for selected topics.

TOPICS SHAININ TAGUCHI CLASSICAL

(Box, etal.)

Philosophy

!Focus on sources ofvariation

!Appropriate tolerances!Search methods!Rely on engineering

knowledge

!Process/productrobustness (noise)

!Reduction of variationaround target

!Efficient responsevariables (loss function,S/N).

!Saturated designs

!Randomization!Sequential testing

!Best models

!Data transformation,

interactions, responsesurface

Screening

(Sources of

Variation)

!Multi-vari!Comp./variable search

!N/A (done inconjunction with

optimization).

!Fractional factorials

!Steepest ascent

!Nested designs

Optimization !Full factorial

!Saturated factorialdesigns

!Inner/outer arrays.!Robust response

variables

!Full factorial.

!Response surface.

!Multi-level.!Central composite

Control !Pre-control !On-line techniques !Control charts

Tolerancing!Tolerance

parallelograms

!ISO plots.!Tolerance designs

(factorial).

!Stack-up.

!RMS analysis


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Summary Tools

There are advantages and disadvantages to any one persons methodologies. Thefollowing matrix looks at advantages/disadvantages of the tools and techniques.

TOOL ADVANTAGES DISADVANTAGESSUGGESTED

ALTERNATIVES

S: Multi-vari

!Identify family ofvariation

!Graphical

!Easy

!Limited in infoobtained

!Nested factorial

designs

S: Tolerance

Parallelogra

m

!Identify appropriatetolerance

!Correlate customerrequirement to process

tolerance

!Easy

!Requires significantknowledge of product

and the right X

variable !Correlation/

regression analysis

S: Variable

Search

!Easy

!Statistically valid(SV).

!Inefficient!Costly/time

consuming

!Bad assumptions

!SV not necessarily

important

!Sequential designs

!Fractional factorials!Screening designs!Nested designs

T: Loss

Function

!Cost metric!Focus on targets!Efficient for

motivating reduction

in variation around

target values

!Subjectivity!Requires knowledge

of customer loss

!Cpk.

!CR

!Cp

!Cpm

!s2T: Inner/Outer

Arrays

!Robust to noise!Optimization in noisy

environment.!Inefficient!Complex!Questionable

!Blocking

!Randomization

!CovarianceT: Orthogonal

Arrays

!Balanced!Efficient

!Confounding!Complex

!Standard order designs

!Sequential designs

T: S/N Ratio!

Robust to interactions!Additivity good

!Dependent onresponse variable

!Not always the propertransform

!Coefficient of

variance

!s2, x analysis

!TransformationsNote: S is Shainin; T is Taguchi.


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Conclusion

The controversy over whose method is best is spurious. Constituents of the Six

Sigma Associatesbelieve in presenting a wide array of approaches and having the client

choose those that fit their situation (or perhaps more precisely their culture). We attempt to

present opposing arguments for different methodologies, but obviously may be biased

because of our direct application experiences.

References

Box, G.E.P. and Soren Bisgaard. The Scientific Context of Quality Improvement. Quality

Progress, June 1987.

Box, G.E.P, Soren Bisgaard and Conrad Fung. An Explanation and Critique of Taguchis

Contributions to Quality Engineering. Quality and Reliability Engineering International,

vol. 4, 1988, pp. 123-131.

Gunter, Bert. Statistically Designed Experiments. Quality Progress, December 1989-August

1990.

Logothetis, N., A Perspective on Shainins Approach to Experimental Design for Quality

Improvement, Quality and Reliability Engineering International, vol. 6, 1990, pp. 195-202.

Private Correspondence with G. Taguchi (1990), D. Shainin (1987) and G. Box (1990).

Shainin, Dorian. Better Than Taguchi Orthogonal Tables. ASQC Quality Congress

Transactions, 1986.

Taguchi, G. and Don Clausing. Robust Quality. Harvard Business Review, No. 90114,January-February, 1991.

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