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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
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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
Variable SearchFull FactorialExperiments
Interactionpresent?
ScatterPlotExperiment
Response SurfaceMethod Experiment
Process Certification
Positrol
Pre-control
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Searching For Red X: Problem Solving StepsShainins Methods Problem Solving Steps
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Unit-to-Unitvariation Within Unitvariation
Time-to-Timevariation
ProductProcess Search
Red XIdentified?
B vs.CExperiment
Variable SearchFull FactorialExperiments
Interactionpresent?
ScatterPlotExperiment
Response SurfaceMethod Experiment
Process Certification
Positrol
Pre-control
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Searching For Red X: Problem Solving StepsShainins Methods Problem Solving Steps
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