Doe Shainin Methods Tips

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QBASE Engineering Sdn FAQs and Quality Tips page 1

I List of Clue-Generating Toolsa. Multi-Vari Analysisb. Concentration Chartsc. Component Searchd. Paired Comparisone. Product/Process Search

III. DOE Optimizationa. Scatter Plot - to Achieve Realistic

Specification and Tolerances

b. Response Surface Method(RSM) - to Optimize Interactions

II. Formal Design of ExperimentTechnique to Characterize a Product / Processa. Variable Searchb. The Full Factorialc. B versus C

Statistical Process Control:

a. Pre-control Simple & effective techniqueof Process Control

V. Transition from DOE to Statistical Process Control

a. Positrol:Holding the gains

B. Process Certification:Eliminating Peripheral Causes of Poor Quality

Shainins Methods Practical Design of ExperimentShainins DOE Tools

For detail contact:[email protected] [email protected]

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CLUE GENERATION TOOLS

Multi-vary Experiments

to reduce a larger number of unknowns and unmanageable causes of variation into a much

smaller of related variables containing the Red X (i.e most dominant cause.)

It is a graphical technique to zoom in to the most likely cause of the problem by eliminating

non-contributing causes of variation.

In most application, multi-vari technique acts as the first filter which later followed by other

clue generation tools.

ConcentrationCharts

Sequel to Multi-vary Experiments. It is used to pinpoint repetitive defects

by location or components

Determines how a product/process is running; a quick snapshots without massive historical

data and can be substitute for replace process capability studies in some white collar

applications

Normally used when the Red X is within-unit

Min 9 to 15 or until 80% of historic variation is captured.

Component Search

From hundred of thousands of components/ subassemblies, home in the Red X, capturing

the magnitude of ALL important main effects and interaction effects.

Normally used when there are two differently performing assemblies ( labeled as good

and bad) with interchangeable components (electric motors, suspension system of a

car..)

Typically use at prototype, engineering pilot run, production pilot run, or in field.

Require only 2 samples One(1) good unit and one(1) bad unit

I List of Clue-Generating Toolsa. Multi-Vari Analysisb. Concentration Chartsc. Component Searchd. Paired Comparisone. Product/Process Search


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CLUE GENERATING TOOLS

Paired Comparison Used to identify the Red X when the good and bad units, assembly or

subassembly cannot disassemble and reassemble without damaging or

destroying or radically changing the good and bad units properties.

Use in situation where there are two differently performing assemblies (

labeled as good and bad) incapable of interchangeable of the

components.

Commonly used in New product and/or process design production, field,

support services, administrative works, farms, hospital, and schools.

It is a logical sequence to component search, when the Red X, distilled

from the system, subsystem, and subassembly component search, cannot

be disassemble any further.

Sample size required : 12 to 16 - 6 to 8 good units and 6 to 8 bad

units in rank order.

Product Process Search To identify important product variables identified with paired comparison.

To identify important process variables associated with 8 good and 8 bad

products.

Commonly used in situation where it is difficult to isolate important process

variables with multi-vari

Typically used during prototype, engineering pilot run, production pilot run,

in field or in full production

Sample size required: Sufficient units through a process to produce 8

good units and 8 bad units and their associated process parameters


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FORMAL DESIGN OF EXPERIMENT TECHNIQUES

Variable Search Excellent problem prevention tools normally used to Pinpoint the Red X,

Pink X etc.

Capture the Magnitude of all important main effects and interaction

effects. Of the red X, pink X etc.

To identify any unimportant factors so that their tolerances can be

liberated to reduce cost.

Normally used when there are High Number of variable to investigate (

5 to 20 variables).

Application in white collar work (off-line quality control).

Typically used in R& D , Development engineering, Product Process

Characterization in Production .

For pinpointing the Red X after Multi-Vari or Paired Comparison

experiments have been conducted.

Sample Size required - 1 to 20

Full Factorial

Variable Search

Latin Square

Plackett-Burman

Fractional Factorial

Taguchi Orthogonal

Array

PUREST

Most CONTAMINATED

II. Formal Design of ExperimentTechnique to Characterize a Product / Process

a. Variable Searchb. The Full Factorialc. B versus C


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FORMAL DESIGN OF EXPERIMENT TECHNIQUES

B versus C Basically used as Verification Tool.

To predict how much better a given product or process is than

another, with confidence of 90% or higher.

To assure the permanency of an improved product or process over a

previous one.

To select one product or process over another, even if there is not

improvement in quality, because of some tangible benefits, such as

cost or cycle time.

To evaluate more than just two product, processes, materials

(B,C,D,E etc) simultaneously

Full Factorial To determined which of the 2,3 or 4 variables - filtered through one

or more clue-generation techniques- are important and which are

unimportant;

To open up tolerance of the unimportant variables to reduce costs;

To quantify the magnitude and desired direction of the unimportant

variables and their interaction effects, and to tighten the tolerance

of these variables to achieve a Cp, Cpk = 2.00 and more;

Investigative tool at design or prototype stage where samples

are limited for other clue-generating tools.;

Note: Even though Full factorial experiment is a problem-solving

tool, it is not recommended to use it as a start of a problem

investigation bypassing other clue generation tools.


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DOE FOR OPTIMIZATION

Scatter plot Used to establish Realistic Specifications and Realistic Tolerances.

Used to adjust or Tighten the Tolerances of the important

product/process or Red X variables to achieve high Cpks.

Open up the Tolerances of the unimportant variables to reduce cost.

Response Surface Methods (RSM)

To determine the BEST combinations of levels of two or more

INTERACTING input variables ( identified in previous DOE

experiments) to achieve a maximum, minimum , or optimum Green Y

( Response, output and Green Y are the same terms).

III. DOE Optimizationa. Scatter Plot - to Achieve Realistic

Specification and Tolerances

b. Response Surface Method(RSM) - to Optimize Interactions


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Vi) Statistical Process Control:a. Pre-control Simple & effective techniqueof Process Control

V. Transition from DOE to Statistical Process Controla. Positrol:

Holding the gains

B. Process Certification:Eliminating Peripheral Causes of Poor Quality

TRANSITION FROM DOE TO SPC

Positrol The POSITROL plan determines:a) WHAT the variable characterized and optimized through previous DOE experiment.B) WHO should be performing the monitoring, measuring and recording each of important variables.C) HOW determines the correct instrumentation to measure these important variables( observing the 5:1 rule ).D) WHERE determine optimum location of measuring the process parameters so that it truly reflects the correct value.E)WHEN is the frequency of measurement, determine initially by engineering judgment, but later by pre-control.

Process Certification Use process certification to eliminate the Peripheral Causes of Variation and Poor Quality such as:

Management/supervision inadequacyViolation of Good Manufacturing Practices (GMP)Plant/Equipment inattentionEnvironment Neglect

Human Shortcomings

STATISTICAL PROCESS CONTROL

SPC: Pre-Control The use of simple and cost effective pro-control chart and reaction

plan to ensure the process sustain the improvement achieved.

Typically at the last stage of improvement process.


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Problem Solving Framework - linking all the Shainins Tools

Problem Solving Steps Objectives Common Shainins DOE Tools

1.Define the problem( Green Y)

Proper Understanding and defining the problem at hand.

2. Quantify and measure the Green Y

To pinpoint the problemImprove the resolution of the problem

Measure scatter plot Use Likert Scale to convert attributes into variables

3.Problem History (problem history, defect rate, cost)

Understanding historical background of the problem

Trends (Pareto, Defect rate, Cost )

4.Generate Clues To identify all possible causes of the problem and sources of variationTo identify the possible variables/factors related to the problem

Multi-vari ( including concentration chart) Component SearchPaired ComparisonProduct/Process Search

5.Formal Design of Experiment (DOE)

To identify the possible process variables/factors related to the problemTo identify the possible parts/components related to the problem

Variable Search Full Factorial B vs.C

6. Turn Problem on and Off ensuring permanence of improvement

To validate the possible parts/components related to the problem

> B vs. C

7. Establish realistic specification and Tolerances (optimize)

To specified the optimize the Red X ( significant cause(s) ) with proper tolerances.

Scatter Plot Response Surfaced Method (RSM)

8. 8. Hold the process improvement gains

To maintain the improvement achieved through well defined series of control mechanisms

Positirol

9. Hold the Gain with SPC Manage the improved / validate processDaily management of the process

Pre-control


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Note: Solving for the Red X, Pink X and Pale Pink X can:1. Reduce variation3. Eliminate the Green Y (problem)2. Achieve Cpk of 2.00 to 10.00 with one, two or three experiments

50%

Green Y

1 2 3 4 5 6 7 Causes/variable/factors/component/parts

The Vital FewThe Trivial Many

Red XPink X

Pale Pink X

Relationship between Green Y and Red X : Pareto Principle


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Searching For Red X: Problem Solving Steps

Problem Green Y

Measurement System /Discrimination Ratio Accuracy, Bias ,precision

5:1 Accuracy

Variation FamilyKnown?

Unit-to-Unitvariation

Within Unitvariation Time-to-Time

variation

Componentsearch Experiment

Assemble/Reassemble

Green YConstant?

ProgressiveDisassembly

Part/Component Related

Multi-Vari Experiment

Paired Comparison

Red XIdentified?

B vs.CExperiment

Variable SearchFull FactorialExperiments

Interactionpresent?

Scatter-PlotExperiment

Response SurfaceMethod Experiment

Process Certification

Positrol

Pre-control

Scatter Plot/MultivariWithin Instruments

Instrument-to-instrumentsOperator-to-operator

Likert Chart

Shainins Methods Problem Solving Steps

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Unit-to-Unitvariation

Within Unitvariation

Time-to-Timevariation

Concentration Chart

PairedComparison

Red XIdentified?

B vs.CExperiment


Interactionpresent?

ScatterPlotExperiment



Positrol

Pre-control

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Unit-to-Unitvariation Within Unitvariation

Time-to-Timevariation

ProductProcess Search

Red XIdentified?

B vs.CExperiment


Interactionpresent?

ScatterPlotExperiment



Positrol

Pre-control

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