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qsconsultwww.qsconsult.be 1

Willy Vandenbrande

Shainin: A concept for problemsolving

Lecture at the Shainin conference

Amelior

11 December 2009


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Dorian Shainin (1914 2000) Aeronautical engineer (MIT 1936)

Design Engineer for United Aircraft Corporations

Mentored by his friend Joseph M. Juran

Reliability consultant for Grumman Aerospace (LunarExcursion Module)

Reliability consultant for Pratt&Whitney (RL-10 rocket engine)

Developed over 20 statistical engineering techniques forproblem solving and reliability

Started Shainin Consultants in 1984, his son Peter is currentCEO.


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Dorian Shainin and ASQ

15th ASQ Honorary Member (1996)

First person to win all four major ASQmedals

In 2004 ASQ created the Dorian ShaininMedal For outstanding use of unique or creative

applications of statistical techniques in thesolving of problems related to the quality of aproduct or service.


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Dorian Shainin Not very well known outside USA

(compared to Deming, Juran)

1991: Publication of first edition ofWorld Class Quality by Keki Bothe

2000: Second edition (Keki and Adi Bothe)

Books brought attention to Shaininmethods, but are very biased.


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Problem Solving

Focus is on variation reduction

LSL USL

LSL = Lower Specification LimitUSL = Upper Specification Limit

Before

After


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Problem Solving

But also

LSL

Before

After


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Basic Shainin assumption The pareto principle of vital few and trivial many.

Only a few input variables are responsible for alarge part of the output behavior.

Red XTM

Pink XTM

Pale Pink XTM

Problem solving becomes the hunt for the Red XTM


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Shainin tools Recipe like methods / statistics in the background

Comparing extremes allows easier detection of causes

BOB Best of Best WOW Worst of Worse

Non parametrics with ranking tests in stead of calculationswith hypothesis tests

Graphical Methods

Working with small sample sizes

The truth is in the parts, not in the drawing: let the parts talk!


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Preliminary activities

Define the critical output variable(s) to be

improved (called problem Green Y)

Determine the quality of the Measurement

System used to evaluate the Green Y

A bad measurement system can in itself beresponsible for excessive variation

Improvements can only be seen if they can bemeasured


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Overview of Shainin tools

Components

Search

Multi-Vari

chart

Paired

Comparisons

Variables

Search

Full

Factorials

B vs C

Scatter Plots

Precontrol

Product / Process

Search

RSM methods

PositrolProcess

Certification

Clue generating

FormalDoe tools

Validation

Optimization

Assurance

Ongoingcontrol

Control

20 1000 variables

5 20 variables 4 or less variables

No interactions Interactions

Source: World Class Quality 2nd edition


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General comments Gradually narrowing down the search

Clear logic Analyzing

Improving

Controlling

Not all tools are Shainin tools

Whats in a name? Positrol versus Control Plan Process Certification versus Process Audit


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Tool details Overview of methods

More info on B vs CTM and Scatter Plots in

workshops

Some more detail on

Multi-Vari chart

Paired ComparisonTM

and Product/ProcessSearch

Pre Control


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Clue Generating / Multi-Vari Chart

Very useful tool and best applied beforebrainstorming causes on excess variationComments

Samples taken in production on current process

Could be a big measurement investmentSample Size

Divide total variation in categories

Search for causes of variation in the biggestcategory firstPrinciples

Problem type: excess variationWide applicabilityApplication

Understand the pattern of variationDefine areas where not to look for problemsAllow a more specific brainstorm

Objective


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Multi-Vari Chart

Breakdown of variation in 3 families:

Positional (within piece, between cavities, )

Cyclical (consecutive units, batch-to-batch, lot-to-

lot)

Temporal (hour-to-hour, shift-to-shift, )


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Multi-vari Chart

If one family of variationcontains a large part oftotal variation, we can

concentrate on

investigating variablesrelated to this family of

variation.


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Clue Generating / Component SearchTM

Disassembly / reassembly requirement limitsapplication.Comments

2 = 1 BOB and 1 WOW

Sample Size

Select BOB and WOW unit

Exchange components and observe behavior.Components that change behavior are Red X compPrinciples

Problem type: assembly does not perform to specLimitation: Disassembly / Reassembly must bepossible without product change

Application

Find the component(s) of an assembly that is (are)responsible for bad behaviorObjective


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Clue Generating / Paired ComparisonTM

Practical application of let the parts talk

Comments

5 to 6 pairs of 1 BOB and 1 WOW

Sample Size

Select pairs of BOB and WOW units

Look for differencesConsistent differences to be investigated furtherPrinciples

Problem type: occasional problems in productionflowApplication

Find directions for further investigation

Objective


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Paired ComparisonsTM: method

Step 1: take 1 good and 1 bad unit

As close as possible in time

Aim for BOB and WOW units

Step 2: note the differences between these units(visual, dimensional, mechanical, chemical, ). Let

the parts talk!

Step 3: take a second pair of good and bad units.

Repeat step 2


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Paired ComparisonsTM

: method Step 4: repeat this process with third, fourth, fith,

pair until a pattern of differences becomes apparent.

Step 5: dont take inconsistent differences into

account. Generally after the fith or sixth pair the

consistent differences that cause the variationbecome clear.


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Clue Generating / Product/Process Search

Tukey test is alternative for t-test

Widely applicable method

Problem: available data (process parameters)

Comments

8 BOB and 8 WOW units / batches

Sample Size

Select sets of BOB and WOW units batches - ..

Add product data / process parameters and rankApply Tukey test to determine important parametersPrinciples

Problem type: Various types of problemsApplication

Preselection of variables out of a large group ofpotential variablesObjective


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Product/Process Search: example

Transmission assemblies rejected for noise.

Components search shows idler shaft asresponsible component

One of the parameters of idler shaft is out of

round 8 good / 8 bad units selected and measured

for out of round


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Product/Process search: example

0.007

0.011

0.019

0.017

0.022

0.014

0.018

0.015

Out of round good units

(mm)

0.017

0.021

0.023

0.024

0.023

0.016

0.018

0.019

Out of round bad units

(mm)


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Tukey test procedure Rank individual units by parameter and

indicate Good / Bad. Count number of all good or all bad from

one side and vice versa from other side.

Make sum of both counts.

Determine confidence level to evaluate

significance.


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Tukey test confidence levels

99.9%13

99%10

95%7

90%6

ConfidenceTotal end count


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Tukey test: example

0.0230.0230.024

0.016

0.017

0.0180.0190.021

0.017

0.0180.019

0.022

0.007

0.0110.0140.015

BadGood

Top end count (all good)

4

Bottom end count (all bad)

3

Overlap region


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Tukey test: example Total end count = 4 + 3 = 7

95 % confidence that out-of-round idlershaft is important in explaining the

difference in noise levels.


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Formal Doe tools / Variables Search

Alternative to fractional factorials on two levels

Method comparable to components searchComments

Number of tests is determined by number of

variables and quality of ordering.Sample Size

List variables in order of criticality (process knowledge)and indicate good / bad level.

Swap factor settings and observe behavior.

Factors that change behavior (and interactions) are redXTM, Pink XTM

Principles

Problem type: Various types of problems

After clue generating more then 4 potential variablesleftApplication

Determine Red XTM, Pink XTM includingquantification of their effectObjective


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Formal Doe tools / Full Factorials

Well established method

Comments

Number of tests is determined by number ofvariables k (2k test combinations)Sample Size

Classical DOE with Full Factorials at two levels

Main Effects and interactions are calculatedPrinciples


After clue generating 4 or less variables leftApplication

Determine Red XTM, Pink XTM includingquantification of their effectObjective


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Formal Doe tools / B(etter) vs C(urrent)TM

Quick validation that works well with big

improvementsComments

3 B and 3 C tests (each test can involve severalunits test of variation reduction)

All 3Bs must be better than all 3CsSample Size

Create new process using optimum settingsand compare optimum with current.Principles


Application

Validation of Red XTM, Pink XTMObjective


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Optimization / Scatter Plots

Graphical method that could easily be transformedto a statistical methodComments

30 tests for each critical variable

Sample Size

Do tests around optimum and use graphicalregression to set tolerancePrinciples

Problem type: Variation Reduction and optimizing

signalApplication

Fine tune best level and realistic tolerance for RedXTM, Pink XTM if no interactions are presentObjective


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Optimization / Response Surface Methods

Method developed by George BoxComments

Depends on variables and surface.

Sample Size

Evolutionary Operation (EVOP) to scanresponse surface in direction of steepest

ascent

Principles

Problem type: Variation Reduction and optimizing

signalApplication

Fine tune best level and realistic tolerance for RedXTM, Pink XTM if interactions are presentObjective


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EVOP example


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Control / Positrol

Can be compared with a Control PlanComments

Checking frequency in the When column

Sample Size

Table of What, How, Who, Where and Whencontrol has to be exercised.Principles

Problem type: all types

Application

Assuring that optimum settings are keptObjective


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Control / Process Certification

Mix of 5S, Poka-Yoke, instructions, ISO 9000,audits,Comments

Checking frequency to be determined

Sample Size

Make overview of things that could influence the

process and install inspections, audits, Principles

Problem type: all types

Application

Eliminating peripheral causes of poor qualityObjective


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Control / Pre Control

Alternative to classical SPCTraffic lights system

Very practical method

Comments

Checking frequency to be determined

Sample Size

Divide total tolerance in colored zones and use

prescribed sampling and rules to control theprocess.

Principles

Problem type: control variation and setting of the

processApplication

Continuous checking of the quality of the processoutputObjective


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Pre-Control: chart construction

USL LSL

TARGET

TOL

1/4 TOL 1/4 TOL


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Pre-control: use of chart1. Start process: five consecutive units in

green needed as validation of set-up.2. If not possible: improve process.

3. In production: 2 consecutive units

4. Frequency: time interval between twostoppages (see action rules) / 6.


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Pre-control: action rules

Stop and act2 units in different yellow zone

Stop and act1 unit in red zone

Correct2 units in same yellow zone

Continue1 unit in green and 1 unit in yellowzone

Continue2 units in green zone

ActionResult of samples

After an intervention: 5 consecutive units in green zone


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Pre-control: example

TimeStart

Correct

Start


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Willy Vandenbrande

Willy Vandenbrande, Master TQMASQ Fellow - Six Sigma Black Belt

Montpellier 34B - 8310 Brugge

Belgi - BelgiumTel + 32 (0)479 36 03 75

E-mail [email protected] www.qsconsult.be

QS Consult

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