6 SIGMA: THE SEARCH 6 SIGMA: THE SEARCH FOR AND CONTROL OF XFOR AND CONTROL OF X

Michael W. PiczakMichael W. PiczakDipl.T., B.Comm., MBADipl.T., B.Comm., MBA

University of BuffaloUniversity of Buffalo

May 2003May 2003

WHO IS USING IT?WHO IS USING IT? MOTOROLAMOTOROLA GENERAL ELECTRICGENERAL ELECTRIC TEXAS INSTRUMENTSTEXAS INSTRUMENTS SONYSONY LOCKHEED-MARTINLOCKHEED-MARTIN RAYTHEONRAYTHEON CAMCOCAMCO BOART LONGYEARBOART LONGYEAR ALLIED SIGNALALLIED SIGNAL CRANE VALVESCRANE VALVES

GOALS OF 6 SIGMAGOALS OF 6 SIGMA

Defect reductionDefect reduction Yield improvementYield improvement Improved customer satisfactionImproved customer satisfaction Higher net incomeHigher net income Cost reductionCost reduction Process improvement and optimizationProcess improvement and optimization Bottleneck relief Bottleneck relief

2 ROADS TO PROFITABILITY2 ROADS TO PROFITABILITY

FIXING BELIEFFIXING BELIEF

Method of tenacityMethod of tenacity Method of authorityMethod of authority Method of reasoningMethod of reasoning Method of scienceMethod of science

6 SIGMA: IN GOLFING PERSPECTIVE6 SIGMA: IN GOLFING PERSPECTIVE

6 sigma quality levels amount to 3.4 defects 6 sigma quality levels amount to 3.4 defects per million or 99.999966% good.per million or 99.999966% good.

2 sigma = missing 6 putts per round2 sigma = missing 6 putts per round 3 sigma = missing 1 putt per round3 sigma = missing 1 putt per round 4 sigma = missing 1 putt every 9 rounds4 sigma = missing 1 putt every 9 rounds 5 sigma = missing 1 putt every 2.33 years5 sigma = missing 1 putt every 2.33 years 6 sigma = missing 1 putt every 163 years6 sigma = missing 1 putt every 163 years* based on 100 rounds per year* based on 100 rounds per year

PROBLEM SOLVING PROBLEM SOLVING TOOLS AND MODELSTOOLS AND MODELS

MICHAEL W. PICZAKMICHAEL W. PICZAKDIPL.T., B.COMM., MBADIPL.T., B.COMM., MBA

MARCH 2002MARCH 2002

Traditional Problem Traditional Problem Solving Models…Solving Models…

Piczak, Piczak, 19951995

NARROW VS. BROAD NARROW VS. BROAD CONCEPTUALIZATIONCONCEPTUALIZATION

AS QUALITY PROGRAM AS QUALITY PROGRAM

OROR

STAND ALONE OPERATIONS STAND ALONE OPERATIONS RESEARCH METHODOLOGYRESEARCH METHODOLOGY

AS QUALITY PROGRAMAS QUALITY PROGRAM

Improvement Process

Basic Components

Quality Initiatives

Enabling Initiatives and Tools

Improvement ToolsQuality Measurement

1 . D e fine P roduc ts and S e rv ices2 . Iden tify C us tom er R equ irem en ts3 . C om pare P roduc t w ith R equ irem en ts4 . D esc ribe the P rocess5 . Im prove the P rocess6 . M easure Q ua lity and P roduc tiv ity

1 . S e lf D irec ted W ork T eam s2 . S ho rt-cyc le M anu fac tu ring3 . D es ign fo r M anu fac tu re4 . B enchm ark ing5 . S ta tis tica l P rocess C on tro l6 . S upp lie r Q ua lifica tion

1 . O ld M e trics - p rocess m ean ( ) ands tandard dev ia tion ( )2 . C apab ility Index C p , C pk3 . N ew M e trics - de fec ts pe r un it (dpu ), de fec ts pe r m illion un its (dpm u)4 . C os t o f Q ua lity S tud ies

1 . Q ua lity F unc tion D ep loym en t2 . F low cha rts3 . P are to C harts4 . H is tog ram s5 . C ause-and-E ffec t D iag ram s6 . E xperim en ta l D es ign7 . G uage R & R

QUALITY INITIATIVESIMPROVEMENT

PROCESS

IMPROVEMENT TOOLSQUALITY

MEASUREMENT

6 SIGM A

Motorola’s 6 Sigma ProgramMotorola’s 6 Sigma Program

AS STAND ALONE OPERATIONS AS STAND ALONE OPERATIONS RESEARCH METHODOLOGYRESEARCH METHODOLOGY

WHO IS THE ENEMY?WHO IS THE ENEMY?

VARIATIONVARIATION

WHAT ARE WE REACHING FOR?WHAT ARE WE REACHING FOR?

PRIMARY SOURCES OF VARIATIONPRIMARY SOURCES OF VARIATION

Inadequate design marginInadequate design margin Unstable parts and materialUnstable parts and material Insufficient process capabilityInsufficient process capability

WHERE TO FOCUS?WHERE TO FOCUS?

For each product or process critical to quality For each product or process critical to quality (CTQ):(CTQ):

MeasureMeasure AnalyzeAnalyze ImproveImprove ControlControl

GENESIS OF 6 SIGMAGENESIS OF 6 SIGMA

DefineRequirements and

Set Targets

Measure ResultsAgainst Targets

AnalyzeDifferences

Between Targetsand Results

Recommend andImplement

Improvements

ELEMENT 1ELEMENT 1

PORTER’S 5 FORCES MODELPORTER’S 5 FORCES MODEL

PEST MODELPEST MODEL

VOICE OF THE CUSTOMERVOICE OF THE CUSTOMER

2 Brands of customers2 Brands of customersinternalinternalexternalexternal

Voice of the Voice of the customercustomer

DESCRIBE THE PROCESSDESCRIBE THE PROCESS

M1 R N

R efe r to phys ic iano rder OR respond to

ca ll be ll re : P R N

$2033 .05 A 121G et na rco tics keys ifnecessary and take

cart to room

$4062 .45 A 122W ake res iden t &

repos ition by e leva tinghead o f bed , ad jus ting

pos ition (e .g . fla t onback)

$10157 .95 A 123

P our m ed ica tion , se lec ttab le ts o r c rush in to

app lesauce & g ive tores iden t

$2033 .05 A 124 Low er head o f bed &repos ition com fo rtab ly

$10157 .95 A 125 D ocum ent rec iep t o r

re fusa l o f m ed ica tion inN urs ing N o tes , M A R SS hee t & R eport S hee t

$2033 .05 A 126

IdentifiedR x N eed

O bta inedK eys/C hart

P reparedR es ident

A dm in is teredM edication

R e-pos itionedR es ident

I1M onitored

R es ident

C1 P rofess iona l G u ide lines C2 H osp ita l P o lic ies & P rocedures

M2 R P N

O 1R esident C areP rov ided

O 2A dm in is teredM edication

IMPROVING THE PROCESSIMPROVING THE PROCESS

EliminationElimination SimplificationSimplification CombinationCombination ReuseReuse Parallel processingParallel processing SubcontractingSubcontracting

CRITICAL EXAMINATIONCRITICAL EXAMINATION

NO NEW PROBLEMS PLEASENO NEW PROBLEMS PLEASE

Poka Yoke techniquesPoka Yoke techniques• guide pinsguide pins

• templatestemplates

• limit switcheslimit switches

• limited computer screen fieldslimited computer screen fields

• checklistschecklists

• interconnectsinterconnects

GETTING BETTER?GETTING BETTER?

The need to measure in quantitative The need to measure in quantitative terms importantterms important

ISO/QS9000 demands it in terms of ISO/QS9000 demands it in terms of quality and effectivenessquality and effectiveness

• customer satisfactioncustomer satisfaction• quality levels (# non-conformances, dpu, dpmo)quality levels (# non-conformances, dpu, dpmo)• cycle timescycle times• die change timesdie change times

ELEMENT 2: MEASUREMENTELEMENT 2: MEASUREMENT

Quality Measurement

1 . O ld M etrics - p rocess m ean ( ) ands tandard dev ia tion ( )2 . C apab ility Index C p , C pk3 . N ew M e trics - de fec ts pe r un it (dpu ), de fec ts pe r m illion un its (dpm u)4 . C os t o f Q ua lity S tud ies

OLD METRICSOLD METRICS

Measures of central tendency or Measures of central tendency or typicality (mean, median, mode)typicality (mean, median, mode)

Measures of dispersion (range, variance, Measures of dispersion (range, variance, standard deviation)standard deviation)

THE NORMAL DISTRIBUTIONTHE NORMAL DISTRIBUTION

A KEY FORMULAA KEY FORMULA

NORMAL CURVE CHARACTERISTICSNORMAL CURVE CHARACTERISTICS

ContinuousContinuous SymmetricalSymmetrical Tails asymptotic to zeroTails asymptotic to zero Bell shapedBell shaped Mean = median = modeMean = median = mode Total area under curve = 1Total area under curve = 1

VISUALIZING VARIATIONVISUALIZING VARIATION

PROCESS CAPABILITYPROCESS CAPABILITY

PROCESS CAPABILITY IIPROCESS CAPABILITY II

CpkCpk

CAPABILITY ST & LTCAPABILITY ST & LT

Cp LONG TERM (LT)Cp LONG TERM (LT)

ST to LTST to LT

NEW METRICSNEW METRICS

dpudpu dmpodmpo

A CAVEAT A CAVEAT ABOUT DPMOABOUT DPMO

JURAN’S COSTS OF QUALITY JURAN’S COSTS OF QUALITY

Prevention Appraisal

D iscre tionary C os ts

InternalNon-conform ance

ExternalNon-conform ance

C onsequentia l C os ts

C osts O f Q ua lity

ELEMENT 3: QUALITY INITIATIVESELEMENT 3: QUALITY INITIATIVES

Quality Initiatives

1 . S e lf D irec ted W ork T eam s2 . S ho rt-cyc le M anu fac tu ring3 . D es ign fo r M anu fac tu re4 . B enchm ark ing5 . S ta tis tica l P rocess C on tro l6 . S upp lie r Q ua lifica tion

SDWTsSDWTs

From Quality Progress, From Quality Progress, October 1996October 1996

LITERATURE IDENTIFIED BENEFITSLITERATURE IDENTIFIED BENEFITS

Productivity Productivity 15% -250% 15% -250%

All employees can perform all tasksAll employees can perform all tasks

Costs Costs 30% 30%

Cycle time Cycle time 50%-90% 50%-90%

Inventory Inventory 66% 66%

Rework due to engineering flaws Rework due to engineering flaws 50% 50%

BENEFITS CONT’DBENEFITS CONT’D

Late jobs Late jobs 1000% 1000%

Quality Quality

Recurring defective product problems Recurring defective product problems 10% 10%

Return on investment/sales Return on investment/sales

BENEFITS CONT’DBENEFITS CONT’D

Sales Sales 830% 830%

Operating statistics improved by 25-40%Operating statistics improved by 25-40%

Accounts receivable Accounts receivable from 66 days to 51 from 66 days to 51 daysdays

Corporate overhead Corporate overhead from $100M to from $100M to $24M$24M

Accidents Accidents 72% 72%

SHORT CYCLE MFG.SHORT CYCLE MFG.

SMEDSMEDautomated & computerized inspectionautomated & computerized inspection X and moving range control chartsX and moving range control charts automated systems automated systems

(MAPs/CAD/CAM/flexible mfg., etc.)(MAPs/CAD/CAM/flexible mfg., etc.) flexible, self directed work forceflexible, self directed work force

DFMDFM

group technologygroup technology accessibility of different parts & areasaccessibility of different parts & areas ease of workpiece handlingease of workpiece handling ergonomic principlesergonomic principles safety requirementssafety requirements appearanceappearance QFDQFD

BENCHMARKINGBENCHMARKING

more than just organized tourismmore than just organized tourism more than just a nice walk over at a more than just a nice walk over at a

friend’s plantfriend’s plant not industrial espionagenot industrial espionage not a one way channel of communicationnot a one way channel of communication inside and outside of your industryinside and outside of your industry

THE ALCOA SEQUENCETHE ALCOA SEQUENCE

SPCSPC

using numbers to describe absence or using numbers to describe absence or presence of a phenomenonpresence of a phenomenon

systematic gathering of datasystematic gathering of data using a collection of analytics that using a collection of analytics that

promote common understanding and promote common understanding and profound knowledge (Deming)profound knowledge (Deming)

emphasis is on measurementemphasis is on measurement

STATISTICSSTATISTICS

CollectingCollecting OrganizingOrganizing SummarizingSummarizing AnalyzingAnalyzing PresentingPresenting

THE ANALYST’S DUTYTHE ANALYST’S DUTY

start with a regularity, uniformity or curiositystart with a regularity, uniformity or curiosity identify all previously significant predictors of identify all previously significant predictors of

phemon in questionphemon in question theorize as to why independent variables (X’s) theorize as to why independent variables (X’s)

should be predictive of dependent variables (Y)should be predictive of dependent variables (Y) tease out the key X’s that impact Y and control tease out the key X’s that impact Y and control

samesame

ELEMENT 4: IMPROVEMENT TOOLSELEMENT 4: IMPROVEMENT TOOLS

Voice of the Voice of the customercustomer

Flowchart For Classic Problem SolvingFlowchart For Classic Problem Solving

Don’t Mess With It!

YES NO

YES

YOU IDIOT!

NO

Will it Blow UpIn Your Hands?

NO

Look The Other Way

Anyone ElseKnows? You’re F*@~#D!

YESYES

NO

Hide ItCan You Blame Someone Else?

NO

NO PROBLEM!

Yes

Is It Working?

Did You Mess With It?

PARETO ANALYSISPARETO ANALYSIS

HISTOGRAMSHISTOGRAMS

CAUSE & EFFECT DIAGRAMS & CARDSCAUSE & EFFECT DIAGRAMS & CARDS

SYSTEMATIC CAUSES OF VARIATIONSYSTEMATIC CAUSES OF VARIATION

Lack of preventative maintenanceLack of preventative maintenance Worn toolsWorn tools Operator performanceOperator performance Environmental changesEnvironmental changes Sorting practicesSorting practices MaterialsMaterials Measurement SystemMeasurement System

SELECTION OF RESPONSE VARIABLE (Y)

CHOICE OF FACTORS (Xi’s), LEVELS, RANGES

RECOGNITION OF & STATEMENT OF PROBLEM

CHOICE OF EXPERIMENTAL DESIGN

PERFORMING EXPERIMENT

STATISTICAL ANALYSIS OF DATA

CONCLUSIONS, RECOMMENDATIONS, NEXT STEPS

NASA DATA & REGRESSION LINENASA DATA & REGRESSION LINE

'O' RING EROSION x TEMPERATURE

-20-10

0102030405060

0 25 50 75 100

Launch Temperature (F.)

Ero

sio

n D

epth

(t

ho

usa

nd

ths)

REGRESSION ANALYSISREGRESSION ANALYSIS

ALWAYS LOOK AT THE DATA FIRSTALWAYS LOOK AT THE DATA FIRST

AS STAND ALONE OPERATIONS AS STAND ALONE OPERATIONS RESEARCH METHODOLOGYRESEARCH METHODOLOGY

THE HUNT FOR XTHE HUNT FOR X

6 SIGMA: OLD WINE OUT 6 SIGMA: OLD WINE OUT OF NEW BOTTLES?OF NEW BOTTLES?

 

RANKING BY USE OF PARETO PRIORITY INDEX (PPI)

PROJECT SAVINGS PROBABILITY COST TIME PPI(K$) (K$) (YEARS)

A 100 0.7 10 2 3.5

B 50 0.7 2 1 17.5

C 30 0.8 1.6 0.25 60

D 10 0.9 0.5 0.5 36

E 1.5 0.6 1 0.1 9

DUPONT’S PPIDUPONT’S PPI

3 KINDS OF STATISTICS3 KINDS OF STATISTICS

Descriptive Descriptive InferentialInferential PredictivePredictive

GE’s MAICGE’s MAIC

JURAN’s 6 SIGMA PROGRAMJURAN’s 6 SIGMA PROGRAM

Xs, Ys, and Zs of DOEXs, Ys, and Zs of DOE

SELECTION OF RESPONSE VARIABLE (Y)

CHOICE OF FACTORS (Xi’s), LEVELS, RANGES

RECOGNITION OF & STATEMENT OF PROBLEM

CHOICE OF EXPERIMENTAL DESIGN

PERFORMING EXPERIMENT

STATISTICAL ANALYSIS OF DATA

CONCLUSIONS, RECOMMENDATIONS, NEXT STEPSCLASSIC DOECLASSIC DOE

6 SIGMA TRAINING TERMINAL 6 SIGMA TRAINING TERMINAL PERFORMANCE OBJECTIVESPERFORMANCE OBJECTIVES

As a result of taking this program, the participant As a result of taking this program, the participant will be able to:will be able to:

Appreciate the scope of 6 Sigma practices in Appreciate the scope of 6 Sigma practices in context of other company initiativescontext of other company initiatives

Apply a variety of tools to solve problemsApply a variety of tools to solve problems

T.P.O.s CONTINUED...T.P.O.s CONTINUED...

Participate as a contributing member of Participate as a contributing member of a continuous improvement or problem a continuous improvement or problem solving teamsolving team

Use Minitab as a data analysis toolUse Minitab as a data analysis tool

PEDAGOGICAL APPROACHPEDAGOGICAL APPROACH

LectureLecture Discussion, debate and argumentDiscussion, debate and argument VideosVideos Hands-on exercises using general and Hands-on exercises using general and

company specific examplescompany specific examples

A BASIC RESEARCH PARADIGMA BASIC RESEARCH PARADIGM

Select the operation to be analyzedSelect the operation to be analyzed Explicate key variables a prioriExplicate key variables a priori Gather data on key X’s and Y’sGather data on key X’s and Y’s Enter data and edit sameEnter data and edit same Verify data integrity via Look, Counts, Describe, Verify data integrity via Look, Counts, Describe,

Scatter DiagramScatter Diagram Run DescriptivesRun Descriptives Generate other graphs & charts of dataGenerate other graphs & charts of data GR&Rs, regressions, ANOVAs, DOEs, GR&Rs, regressions, ANOVAs, DOEs,

THE JOURNEYTHE JOURNEY

Most companies presently at 3-4 sigmaMost companies presently at 3-4 sigma The move is toward 6 sigma (Cp = 2)The move is toward 6 sigma (Cp = 2) Literature has references to 12 sigma Literature has references to 12 sigma

(Cp = ?)(Cp = ?)Tinnerman-Pallnut is at 50 ppb on Tinnerman-Pallnut is at 50 ppb on

selected product linesselected product lines

6 SIGMA: THE SEARCH 6 SIGMA: THE SEARCH FOR AND CONTROL OF XFOR AND CONTROL OF X

Michael W. PiczakMichael W. PiczakDipl.T., B.Comm., MBADipl.T., B.Comm., MBA