Shainin: A concept for problem solving

Shainin: A concept for problem

solving

Lecture at the Shainin conference

Amelior

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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 (Lunar

Excursion Module)

•

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

•

Developed over 20 statistical engineering techniques for

problem solving and reliability

•

Started Shainin Consultants in 1984, his son Peter is current

CEO.

Dorian Shainin and ASQ

• 15th ASQ Honorary Member (1996)

• First person to win all four major ASQ

medals

• In 2004 ASQ created the Dorian Shainin

Medal

– For outstanding use of unique or creative

applications of statistical techniques in the

solving of problems related to the quality of a

product or service.

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Dorian Shainin

• Not very well known outside USA

(compared to Deming, Juran)

• 1991: Publication of first edition of

“World Class Quality” by Keki Bothe

• 2000: Second edition (Keki and Adi Bothe)

• Books brought attention to Shainin

Problem Solving

• Focus is on variation reduction

LSL USL

Before

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Problem Solving

• But also …

Basic Shainin assumption

• The pareto principle of vital few and trivial many.

• Only a few input variables are responsible for a

large part of the output behavior.

– Red X

– Pink X

– Pale Pink X

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Shainin tools

•

Recipe like methods / statistics in the background

•

Comparing extremes allows easier detection of causes

– BOB

– WOW

•

Non parametrics with ranking tests in stead of calculations

with hypothesis tests

•

Graphical Methods

•

Working with small sample sizes

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 be

responsible for excessive variation

– Improvements can only be seen if they can be

measured

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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 Positrol Process Certification Clue generating Formal Doe tools Validation Optimization Assurance Ongoing control Control 20 – 1000 variables

5 – 20 variables 4 or less variables

No interactions Interactions

General comments

• Gradually narrowing down the search

• Clear logic

– Analyzing

– Improving

– Controlling

• Not all tools are “Shainin” tools

• “What’s in a name?”

– Positrol versus Control Plan

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Tool details

• Overview of methods

• More info on B vs C

_{and Scatter Plots in}

workshops

• Some more detail on

– Multi-Vari chart

– Paired Comparison

_{and Product/Process}

Search

Clue Generating / Multi-Vari Chart

Very useful tool and best applied before brainstorming causes on excess variation

Comments

Samples taken in production on current process Could be a big measurement investment

Sample Size

Divide total variation in categories

Search for causes of variation in the biggest category first

Principles

Problem type: excess variation Wide applicability

Application

Understand the pattern of variation

Define areas where not to look for problems Allow a more specific brainstorm

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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)

Multi-vari Chart

• If one family of variation

contains a large part of

total variation, we can

concentrate on

investigating variables

related to this family of

variation.

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Clue Generating / Component Search

TM

Disassembly / reassembly requirement limits application.

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 comp

Principles

Problem type: assembly does not perform to spec Limitation: Disassembly / Reassembly must be possible without product change

Application

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

Clue Generating / Paired Comparison

TM

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 differences

Consistent differences to be investigated further

Principles

Problem type: occasional problems in production flow

Application

Find directions for further investigation

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Paired Comparisons

TM

: 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

Paired Comparisons

TM

: method

• Step 4: repeat this process with third, fourth, fith, …

pair until a pattern of differences becomes apparent.

• Step 5: don’t take inconsistent differences into

account. Generally after the fith or sixth pair the

consistent differences that cause the variation

become 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 rank Apply Tukey test to determine important parameters

Principles

Problem type: Various types of problems

Application

Preselection of variables out of a large group of potential variables

Product/Process Search: example

• Transmission assemblies rejected for noise.

• Components search shows idler shaft as

responsible 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)

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

Confidence

Total end count

Tukey test: example

Top end count (all good)

4

Bottom end count (all bad)

3

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Tukey test: example

• Total end count = 4 + 3 = 7

• 95 % confidence that out-of-round idler

shaft is important in explaining the

Formal Doe tools / Variables Search

Alternative to fractional factorials on two levels Method comparable to components search

Comments

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 red XTM_{, Pink X}TM

Principles

Problem type: Various types of problems

After clue generating more then 4 potential variables left

Application

Determine Red XTM_{, Pink X}TM _including

quantification of their effect

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Formal Doe tools / Full Factorials

Well established method

Comments

Number of tests is determined by number of variables k (2k _{test combinations)}

Sample Size

Classical DOE with Full Factorials at two levels Main Effects and interactions are calculated

Principles

Problem type: Various types of problems After clue generating 4 or less variables left

Application

Determine Red XTM_{, Pink X}TM _including

quantification of their effect

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

Quick validation that works well with big improvements

Comments

3 B and 3 C tests (each test can involve several units – test of variation reduction)

All 3B’s must be better than all 3C’s

Sample Size

Create new process using optimum settings

and compare optimum with current.

Principles

Problem type: Various types of problems

Application

Validation of Red XTM_{, Pink X}TM

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Optimization / Scatter Plots

Graphical method that could easily be transformed to a statistical method

Comments

30 tests for each critical variable

Sample Size

Do tests around optimum and use graphical

regression to set tolerance

Principles

Problem type: Variation Reduction and optimizing signal

Application

Fine tune best level and realistic tolerance for Red XTM_{, Pink X}TM _{if no interactions are present}

Optimization / Response Surface Methods

Method developed by George Box

Comments

Depends on variables and surface.

Sample Size

Evolutionary Operation (EVOP) to scan

response surface in direction of steepest

ascent

Principles

Problem type: Variation Reduction and optimizing signal

Application

Fine tune best level and realistic tolerance for Red XTM_{, Pink X}TM _{if interactions are present}

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Control / Positrol

Can be compared with a Control Plan

Comments

Checking frequency in the When column

Sample Size

Table of What, How, Who, Where and When

control has to be exercised.

Principles

Problem type: all types

Application

Assuring that optimum settings are kept

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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 quality

Control / Pre Control

Alternative to classical SPC Traffic 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 the process.

Principles

Problem type: control variation and setting of the process

Application

Continuous checking of the quality of the process output

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Pre-Control: chart construction

USL

LSL

TARGET

½ TOL

Pre-control: use of chart

1. 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 two

stoppages (see action rules) / 6.

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Pre-control: action rules

Stop and act 2 units in different yellow zone

Stop and act 1 unit in red zone

Correct 2 units in same yellow zone

Continue 1 unit in green and 1 unit in yellow

zone

Continue 2 units in green zone

Action Result of samples

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Willy Vandenbrande Willy Vandenbrande, Master TQM

ASQ Fellow - Six Sigma Black Belt Montpellier 34 B - 8310 Brugge België - Belgium Tel + 32 (0)479 36 03 75 E-mail [email protected] Website www.qsconsult.be

QS Consult