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IE 3265R. Lindeke, Ph. D.

Quality Management in POM Part 2

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Topics

Managing a Quality System Total Quality Management (TQM)

Achieving Quality in a System Look early and often

6 Sigma an approach & a technique Make it a part ofthe process

The Customers Voice in TotalQuality Management QFD and the House ofQuality

Quality Engineering Loss Function Quality Studies Experimental Approaches

T.M.; FMEA; Shainin

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Taguchis Loss Function

Taguchi defines Quality Level ofaproduct as the Total Loss incurredby society due tofailure ofaproduct toperform as desired

when it deviates from the deliveredtarget performance levels.

This includes costs associatedwith poorperformance, operatingcosts (which changes as a product

ages) and any added expensesdue to harmful side effects oftheproduct in use

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Exploring the Taguchi Method

Considering the LossFunction, it is quantifiable

Larger is Better:

Smaller is Better:

Nominal is Best:

2

1( ) L y k y

! - 2

( ) L y ky!

2

( ):

m is the target of the

process specification

L y k y mwhere

!

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Considering the Cost ofLoss

k in the L(y) equation is found from:

02

0

0

0

is cost of repair or replace

a product and must include

loss due to unavailability

during repairis the functional limit on

y of a product where it would

fail to perform its function

half the

Ak

A

!(

(

time

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Loss Function Example: (nominal isbest)

We can define a processes averageloss as:

s is process (product) StandardDeviation

ybar is process (product) mean

22 L k s y m ! -

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Example cont.

A0 is $2 (a very low numberof this type!)found by estimating that the loss is 10% ofthe $20 product cost when a part is exactly8.55 or 8.45 units

Process specification is: 8.5+.05 units

Historically: ybar

= 8.492 and s = 0.016

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Example Cont.

Average Loss:

Ifwe make 250,000 units a year

Annual Loss is $64,000

2 222 0.016 8.492 8.500

.05

800 .00032 $0.256

L

L

!

- ! !

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Fixing it

Shift the Mean tonominal

Reduce variation(s = 0.01)

Fix Both!

22800 .016 0 $0.2048

Annual Loss is $51200 about 20% reduction

L ! !-

22800 .010 .008 $0.1312

Annual Loss is $32800 about 50% reduction

L ! !-

22800 .010 0 $0.08

Annual Loss is $20000 about 66% reduction

L ! !-

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Taguchi Methods

Help companies toperform the Quality Fix! Quality problems are due to Noises in the product or

process system

Noise is any undesirable effect that increasesvariability

Conduct extensive Problem Analyses

Employ Inter-disciplinary Teams

Per form Designed Experimental Analyses Evaluate Experiments using ANOVA and Signal-

to noise techniques

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Defining the Taguchi Approach

The Point Then Is To ProduceProcesses Or Products The Are

ROBUST AGAINST NOISES Dont spend the money to eliminate all

noise, build designs (product andprocess) that can perform as desired low variability in the presence ofnoise!

WE SAY:ROBUSTNESS = HIGH QUALITY

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Defining the Taguchi Approach

Noise Factors Cause Functional Variation

They Fall IntoThree Classes

1. Outer Noise Environmental Conditions 2. Inner Noise Lifetime Deterioration

3. Between Product Noise Piece To Piece

Variation

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Taguchi

Method isStep-by-Step:

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Defining the Taguchi Approach

TO RELIABLY MEET OUR DESIGNGOALS MEANS: DESIGNINGQUALITY IN!

We find that Taguchi consideredTHREE LEVELS OF DESIGN:

level 1: SYSTEM DESIGN

level 2: PARAMETER DESIGN level 3: TOLERANCE DESIGN

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Defining the Taguchi ApproachSYSTEM DESIGN:

All About Innovation NewIdeas, Techniques,Philosophies

Application OfScience AndEngineering Knowledge

Includes Selection Of:

Materials

Pr ocesses

Tentative Parameter Values

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Defining the Taguchi ApproachParameterDesign:

Tests For Levels OfParameterValues

Selects "Best Levels" For Operating

Parameters to be Least Sensitive toNoises

Develops Processes Or ProductsThat Are Robust

A Key StepTo Increasing QualityWithout Increased Cost

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Defining the Taguchi ApproachTolerance Design:

A "Last Resort" Improvement Step

Identifies Parameters Having thegreatest Influence On Output

Variation

Tightens Tolerances On TheseParameters

Typically Means Increases InCost

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Selecting Parameters for Study and

Contr

ol

Select The Quality Characteristic

Define The Measurement Technique

Ennumerate, Consider,And Select TheIndependent Variables And Interactions

Brainstorming

Shainins technique where they are determined by

looking at the products FMEA failure mode and effects analysis

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Preliminary Steps in Improvement

Studies To Adequately Address The Problem At

Hand We Must:1. Understand Its Relationship With The Goals

We Are Trying To Achieve2. Explore/Review Past Performance compareto desired Solutions

3. Prepare An 80/20 Or Pareto Chart OfThesePast Events

4. Develop A "Process Control" Chart -- ThisHelps To Better See The Relationshipbetween Potential Control And NoiseFactors

A Wise Person Can Say: A ProblemWell Defined IsAlreadyNearly Solved!!

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Going Down the ImprovementRoad

Start By Generating The ProblemCandidates List:

Brainstorm The Product Or Process Develop Cause And Effects (Ishikawa)Diagrams

Using Process Flow Charts ToStimulate Ideas

Develop Pareto Charts For QualityProblems

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DEVELOPING A Cause-and-EffectDiagram:

1. Construct A Straight Horizontal Line (Right Facing)

2. Write Quality Characteristic At Right

3. Draw 45 Lines From Main Horizontal (4 Or 5) For MajorCategories: Manpower, Materials, Machines, Methods And

Environment

4. Add Possible Causes By Connecting Horizontal Lines To 45"Main Cause" Rays

5. Add More Detailed Potential Causes Using Angled Rays ToHorizontal Possible Cause Lines

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Generic Fishbone C&E Diagram

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Building the Experiment WorkingFrom a Cause & Effect Diagram

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Designing A Useful Experiment

Taguchi methods use a cookbookapproach!! Building Experiments forselected factors on the C&E Diagram

Selection is from a discrete set ofOrthogonal Arrays

Note: an orthogonal array (OA) is a specialfractional factorial design that allows studyofmain factors and 2-way interactions

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T.M. Summary

Taguchi methods (TM) are product orprocess improvement techniques thatuse DOE methods for improvements

A set ofcookbook designs are available and they can be modified to build arich set ofstudies (beyond what wehave seen in MP labs!)

TM requires a commitment to complete

studies and the discipline to continue inthe face ofsetbacks (as do all qualityimprovement methods!)

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Simplified DOE

Shainin Tools these are a series ofsteps to logically identify the rootcauses ofvariation

These tools are simple to implement,statistically powerful and practical

Initial Step is to sample product (overtime) and examine the sample lots forvariability to identify causative factors

this step is called the multi-vari chartapproach Shainin refers to root cause factors as the

Red X, Pink X, and Pink-Pink X causes

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Shainins

ExperimentalApproachesto QualityVariability

Control:

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Shainin Ideas exploring

further Red X the primary cause ofvariation

Pink X the secondarycauses ofvariation

Pink-Pink X significant butminor causes ofvariation (afactor that still must becontrolled!)

Any otherfactors should besubstituted by lower costsolutions (wider tolerance,cheaper material, etc.)

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Basis ofShainins QualityImprovement Approaches

As Shainin Said: Dont ask the engineers, theydont know, ask the parts

Contrast with Brainstorming approach ofTaguchiMethod

Multi-Vari is designed to identify the likely homeofthe Red X factors not necessarily the factorsthemselves

Shainin suggests that we look into three sourceofvariation regimes:

Positional

Cyclical

Temporal

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Does themean shiftin time orbetween

productsor is theproduct(alone)showing

thevariability?

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Positional Variations:

These are variation within a givenunit (ofproduction)

Like porosity in castings or cracks

Or acr oss a unit with many parts like a

transmission, turbine or circuit board

Could be variations by location inbatch loading processes

Cavity to cavity variation in plastic injectionmolding, etc.

Various tele-marketers at a fund raiser

Variation from machine-to-machine,person-to-person orplant-to-plant

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Cyclical Variation

Variation between consecutiveunits drawn from a process(consider calls on a softwarehelp line)

Variation AMONG groups ofunits

Batch-to Batch Variations

Lot-to-lot variations

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Temporal Variations

Variations from hour-to-hour

Variation shift-to-shift

Variations from day-to-day Variation from week-to-week

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Components Search the

prerequisites The technique is applicable (primarily) inassbly operations where good units andbad units are found

Per formance (output) must be measurableand repeatable

Units must be capable ofdisassembly andreassembly without significant change inoriginal performance

There must be at least 2 assemblies or

units one good, one bad

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The procedure:

Select the good and bad unit

Determine the quantitative parameterby which to measure the units

Dissemble the good unit reassemble and measure it again.Disassemble and reassemble thenmeasure the bad units again. If the

differenceD

between good and badexceeds the d difference (withinunits) by 5:1, a significant andrepeatable difference between goodand bad units is established

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Procedure (cont.)

Based on engineering judgment, rank thelikely component problems, within a unit, indescending orderofperceived importance.

Switch the top ranked component from the

good unit to the bad unit or assembly withthe corresponding component in the badassembly going to the good assembly.Measure the 2 (reassembled) units.

Ifthere is no change: the good unit stays goodbad stays bad, the top guessed component (A) isunimportant goon to component B

Ifthere is a partial change in the twomeasurements A is not the only importantvariable. A could be a Pink X family. Goon toComponent B

Ifthere is a complete reversal in outputs oftheassemblies, A could be in the Red X family. Thereis nofurther need for components search.

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Procedure (cont.)

Regardless ofwhich ofthe threeoutcomes above are observed,restore component A to the originalunits to assure original conditionsare repeated. Then, repeat theprevious 2 steps for the next mostimportant components: B, C, D, etc.ifeach swap leads to no or partial

change Ultimately, the Red X family will be

IDd (on complete reversal) or twoormore Pink X orpale Pink X families

ifonly partial reversals are observed

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Procedure (cont.)

With the important variablesidentified, a capping run with thevariables banded together as goodor bad assemblies must be used toverify their importance

Finally, a factorial matrix, using data

generated during the search, isdrawn to determine, quantitatively,main effects and interactive effects.

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Paired Comparisons

This is a technique likecomponents search butwhen products do not lendthemselves to disassembly

(perhaps it is a component in acomponent search!) Requires that there be several

Good and Bad units that canbe compared

Requires that a suitableparameter can be identified todistinguish Good from Bad

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Steps in Paired Comparison

1. Randomly select one Good and one Bad unit callit pairone

2. Observe the differences between the 2 units these

can be visual, dimensional, electrical, mechanical,chemical, etc. Observe using appropriate means (eye,optical or electron microscopic, X-ray, Spectrographic,tests-to-failure, etc)

3. Select a 2nd pair, observe and note as with pair 1.

4. Repeat with additional pairs until a pattern ofrepeatability is observed between goods & bads

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Reviewing:

The previous (three methods) are ones thatfollowed directly from Shainins talk to theanimals (products) approach

In each, before we began actively specifying

the DOE parameters, we collect as muchinformation as we can from good or badproducts

As stated by one user: The product solutionwas sought forover 18 months, we talked to

engineers & designers; we talked toengineering managers, even productsuppliers all without a successful solution,butwe nevertalkedtothe parts. With thecomponent search technique we identified

the problem in just 3 days

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Taking the Next step: Variables

Search The objective is to Pinpoint the Red X, Pink X and one to three (more) critical

interacting variables

Its possible that the Red X is due to strong interactions between

twoor more variables Finally we are still trying to separate the important variables fromunimportant ones

Variables search is a way to get statistically significantresults without executing a large numberofexperimental

runs (achieving knowledge at reduced c

ost) It has been shown the this binary comparison technique

(on 5 to 15 variables) can be successful in 20, 22, 24 or26 runs vs. 256, 512, 1024, etc. runs using traditionalDOE

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Variables Search is a 2 stage

process:1. List the important input variables as chosen by

engineering judgment (in descending orderofability to influence output)

2. Assign 2 levels to each factor a best andworst level (within reasonable bounds)

3. Run 2 experiments, one with all factors at bestlevels, the second with all factors at worstlevels. Run two replications sets

4. Apply the D:d u 5:1 rule (as above)

5. Ifthe 5:1 ratio is exceeded, the Red X iscaptured in the factor set tested.

STAGE 1:

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Stage 1 (cont):

6. Ifthe ratio is less than 5:1, the right factors are notchosen or 1 or more factors have been reversedbetween best & worst levels. Disappointing, but notfatal!

a. Ifthe wrong factors were chosen in opinion ofdesign team decide on new factors and rerun Stage 1

b. Ifthe team believes it has the correct factors included, but somehave reversed levels, run B vs. C tests on each suspiciousfactor to see iffactor levels are in fact reversed

c. One could try the selected factors (4 at a time) using fullfactorial experiments could be prone tofailure too ifinteracting factors are separated during testing!

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Moving on to Stage 2:1. Run an experiment with AW (a at worst level) and the

rest offactors at best levels (RB)a) Ifthere is no change in best results in Stage 1 step 3, factor A is

in fact unimportantb) Ifthere is a partial change from best results toward Worst

results A is not the only important factor. A could be Pink Xc) Ifa complete reversal in Bestto Worst results in Stage 1 step 3,

A is the Red X

2. Run a second test with AB and RWa) Ifno change from Worstresults in Stage 1 the topfactor A is

further confirmed as unimportantb) Ifthere is a partial change in the worst results in Stage 1 toward Best results A is further confirmed as a possible PinkX factor

c) Ifa complete reversal Best results in Stage 1 areapproximated, A is reconfirmed as the Red X

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Continuing Stage 2:

3. Per form the same component search swapofstep 1 & 2 for the rest ofthe factors to separateimportant from unimportant factors

4. Ifno single Red X factor, but twoor three PinkX factors are found, perform a capping orvalidation experiment with the Pink Xs at thebest levels (remaining factors at their worstlevels). The results should approximate thebest results ofStep 3, Stage 1.

5. Run a second capping experiment with Pinksat worst level, the rest at Best level shouldapprox. the worst results in Step 3, Stage 1.

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Variables Search Example:

Press Brake Op

eration A press brake was showing high variability with poor CPK

The Press Brake was viewed as a Black Magicoperation the worked sometimes then went bad for noreason

Causes ofthe operational variability were hotly debated,Issues included: Raw Sheet metal

Thickness

Hardness

Press Brake Factors (some which are difficult or impossible tocontrol)

The company investigated new P. Brakes but observedno realistic and reliable improvements Even high cost automated brakes sometimes produced poor

results!

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A Variables Search wasPerformed

Goal was to consistently achieve a s.005tolerance (or closer!)

6 Factors were chosen:

A. Punch/Die Alignment B: Aligned, W: notSpecially Aligned

B. Metal Thickness B: Thick, W: Thin

C. Metal Hardness B: Hard, W: Soft

D. Metal Bow B: Flat, W: Bowed

E. Ram Storage B: Coin Form, W: Air Form

F. Holding Material B: Level, W: Angle

Results reported in Process Widths which istwice tolerance, in 0.001 units

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Results:

STAGE 1 Pr ocess Width (x.001)

All Best All W orst

Initial 4 47

Rep 1 4 61

D = 50; d = 7 D:d 7:1 (> 5:1) so a significantrepeatable difference; Red X (or Pink Xs) capturedas a factor

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Factorial Analysis: D & F

D Best D Worst

F Best 4, 4, 3, 5, 7,

7, 4Avg: 4.9

23, 18

Avg: 20.5

Row Sum:

25.4

F Worst 73, 20

Avg: 51.5

47, 102, 61

47, 72, 70,

20; Avg: 57.8

Row Sum:109.3

DiagonalSum: 72

Column Sum:56.4

Column Sum:78.3

DiagonalSum: 62.7

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Factorial Analysis:

20.5 51.8 4.9 51.5 78.3 56.4

2 2

10.95

51.5 57.8 4.9 20.5 109.3 25.4

2 2

41.95

72 62.7

2

4.7

1 2D. Sum D. Sum

(interaction)2

D

F

DF

! !

!

! !

!

! !

!

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Factorial Analysis:

Factor G is Red X: It has a 41.9 main effecton the process spread

Factor D is a Pink X with 10.9 main effect onprocess spread

Their interaction is minor with a contributionof4.9 toprocess spread

With D & F controlled, using a holding fixtureto assure level and reduction in bowing (butwith hardness and thickness tolerancesopen up leading to reduced raw metal costs)the process spread was reduced to 0.004(s.002) much better than the original targetofs.005 with an observed CPK of2.5!

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Introduction to Failure Mode andEffects Analysis (FMEA)

Tool used to systematically evaluate a product,process, or system

Developed in 1950s by US Navy, for use with flightcontrol systems

Today its used in several industries, in manyapplications products

processes

equipment

software service

Conducted on new or existing products/processes

Presentation focuses on FMEA for existing process

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Benefits ofFMEA

Collects all potential issues intoone document Can serve as troubleshooting guide Is valuable resource for new employees at the process

Provides analytical assessment ofprocess risk

Prioritizes potential problems at process Total process risk can be summarized, and compared toother

processes to better allocate resources

Serves as baseline forfuture improvement at process Actions resulting in improvements can be documented Personnel responsible for improvements can gain recognition

Controls can be effectively implemented Example: Horizontal Bond Process: FMs improvedby 40%;

causes improvedby 37%. Overall risk in half in about3 months.

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FMEA Development

Assemble a team ofpeople familiar withprocess

Brainstorm process/product related defects(Failure Modes)

List Effects, Causes, and Current Controlsfor each failure mode

Assign ratings (1-10) for Severity,Occurrence, and Detection for each failuremode 1 is best, 10 is worst

Determine Risk Priority Number (RPN) foreach failure mode Calculated as Severity x Occurrence x Detection

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Typical FMEA Evaluation Sheet

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Capturing The Essence ofFMEA

The FMEA is a tool to systematicallyevaluate a process orproduct

Use this methodology to: Prioritize which processes/ parameters/

characteristics to work on (Plan) Take action to improve process (Do) Implement controls to verify/validate

process (Check) Update FMEA scores, and start focusing

on next highest FM or cause (Act Plan)