Quality Tools presentation

8/18/2019 Quality Tools presentation

1/125

1MTSU

Quality ToolsQuality Tools


2/125

2MTSU

Plan

Do

Study

Act

The PDSA Cycle The PDSA Cycle


3/125

3MTSU

The Process Improvement The Process ImprovementCycleCycle

Implement theImproved process

Select aprocess

Study/document

Seek ways toImprove it

Design anImproved process

Evaluate

Document


4/125

4MTSU

Process ImprovementProcess Improvement

Tools Tools• There are a number of tools

that can be used for problemsolvin and processimprovement

• Tools aid in data collection andinterpretation! and provide thebasis for decision ma"in


5/125

5MTSU

Seven #asic Quality ToolsSeven #asic Quality Tools

$% Chec" sheets&%'lo(charts)% Scatter dia rams*% +isto rams,% Pareto analysis-% Control charts.% Cause/and/e0ect dia rams


6/125

6MTSU

Billing Errors

Wrong Account

Wrong Amount

A/R Errors

Wrong Account

Wrong Amount

onday

Chec" SheetChec" Sheet


7/1257MTSU

'lo(chart'lo(chart

Process 1ood2 Process 1ood2 Process

Process


8/1258MTSU

Scatter Dia ramScatter Dia ram

Variable A

Variable B


9/1259MTSU

+isto ram+isto ram

frequency

A B C D E


10/12510MTSU

80% of theproblemsmay beattributed to20% of thecauses.

80% of theproblemsmay beattributed to20% of thecauses.

Smearedprint

N u m

b e r o f

d e f e c t s

Off center

Missinglabel

Loose Other

Pareto AnalysisPareto Analysis


11/12511MTSU

970

980

990

1000

1010

1020

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

UCL

LCL

Control ChartControl Chart


12/12512MTSU

Cause/and/30ect Dia ramCause/and/30ect Dia ram

Effect

MaterialsMethods

E uipment!eople

En"ironment

#ause

#ause

#ause

#ause

#ause

#ause#ause

#ause

#ause#ause

#ause

#ause


13/125

13MTSU

Trac"in Improvements Trac"in Improvements

!"#

#"#

#"##"#

!"#!"#

Process not centeredand not sta$le

Process centered

and sta$le

Additional improvementsmade to the process


14/125

14MTSU

Seven Mana ement ToolsSeven Mana ement Tools

$% A4nity Dia ram&%Interrelationship Dia raph)% Tree Dia rams*% Prioriti5ation Matrices,% Matri6 Dia ram-% Process Decision Pro ram Chart.% Activity 7et(or" Dia ram


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15MTSU

Methods for 1eneratinMethods for 1eneratinIdeasIdeas

• #rainstormin• Quality circles• Intervie(in• Benchmarking

• ,8&+


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16MTSU

A #rief +istory of QualityA #rief +istory of Quality


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17MTSU

America 9e/discoversAmerica 9e/discoversDeminDemin


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18MTSU

:: and ;uranand ;uran


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19MTSU

9ediscoverin the 1urus9ediscoverin the 1urus

• Demin< 3mphasis on Statistical Control

< $* Points for Mana ement

• ;uran

< Quality Plannin and Analysis< Mana erial #rea"throu h< Quality Control +andboo"


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20MTSU

S t a t i s t i c s


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22MTSU


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23MTSU


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24MTSU


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25MTSU


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26MTSU


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27MTSU


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28MTSU


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30MTSU

S t a t i s t i c s


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31MTSU

8 t T C


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32MTSU

8 at Types o Compan esa ypes o ompan esCan #ene=t from Si6Can #ene=t from Si6

Si ma2Si ma2• Companies that bene=t from structuredor ani5ational improvement

• Companies that need to improve customersatisfaction

• All types of companies can bene=t>< Manufacturin

< Service< 7on/pro=t< 3ducational


33/125

33MTSU

8hat is Si6 Si ma28hat is Si6 Si ma2

A%Customer 'ocus < 'ocus on (hatis critical to customers

#% Data Driven < 36tensive use ofstatistical tools

C%9obust Methodolo y < Tools plusimplementation methods to ma"esuccess more li"ely


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34MTSU

8hat is Si6 Si ma28hat is Si6 Si ma2Key ConceptsKey Concepts

$% Critical to Quality> 8hat attributesare most important to the

customer2 ?CTQ! CTC! CTD@&%Defect> 'ailin to deliver (hat the

customer (ants e6pects ?DPMB@

)% ariation> The level ofunpredictability the customere6periences


35/125

35MTSU

8hat is Si6 Si ma28hat is Si6 Si ma2 Key ConceptsKey Concepts

,% Process Capability> 8hat yourprocess can deliver < consistently

-% Stable Bperations> Stable ops arepredictable

.% Desi n for Si6 Si ma> Desi ninto meet customer needs andprocess capability


36/125

36MTSU

DMAD / DMAICDMAD / DMAIC

Define

Design

Verify

Measure

Analyze

Define

Improve

Control

Measure

Analyze

New Processes Existing Processes


37/125

37MTSU

Statistics iteStatistics iteCenteredCentered 33 σσ ProcessProcess

LSL USL


38/125

38MTSU

Statistics iteStatistics ite CenteredCentered 33 σσ ProcessProcess

LSL USL


39/125

39MTSU


LSL USL

!n"c!n#!rming$r!%uct

1&300 '$()


40/125

40MTSU


LSL USL


2&600 '$()


41/125

41MTSU

Statistics iteStatistics ite ShiftedShifted 33 σσ ProcessProcess

LSL USL

1*5 mean shi#t


42/125

42MTSU


LSL USL


66&800 '$()

1*5 mean shi#t


43/125

43MTSU


Cost to your company – 15-30%of sales

LSL USL


66&800 '$()

1*5 mean shi#t


44/125


45/125

45MTSU

Statistics iteStatistics ite Centered -Centered - σσ ProcessProcess

LSL USL


46/125

46MTSU

Statistics iteStatistics ite Shifted -Shifted - σσ ProcessProcess

LSL USL

1*5 mean shi#t


47/125

47MTSU

Statistics iteStatistics ite Shifted -Shifted - σσ ProcessProcess

LSL USL

1*5 mean shi#t


3*4 '$()


48/125

48MTSU

&&.&&&''% $' Sigma

• 20(000 lost articles of mail per20(000 lost articles of mail per

hourhour

• )(000 incorrect surgical)(000 incorrect surgicaloperations per *ee+ operations per *ee+

• ,*o short or long landings at,*o short or long landings at

most ma-or airports each daymost ma-or airports each day

• 200(000 *rong drug prescriptions200(000 *rong drug prescriptionseach yeareach year

• No electricity for almost se"enNo electricity for almost se"en

hours each monthhours each month

• Se"en articles lost per hourSe"en articles lost per hour

• 2 incorrect operations per *ee+2 incorrect operations per *ee+

• One short or long landing e"eryOne short or long landing e"eryfi"e yearsfi"e years

• 0 *rong prescriptions per year0 *rong prescriptions per year

• One hour *ithout electricity e"eryOne hour *ithout electricity e"ery/ years/ years

&&% $/.8 Sigma

Why Six Sigma?Why Six Sigma?


49/125

49MTSU

Si6 Si maSi6 Si maBreakthrough Strategy Breakthrough Strategy

• De=ne• Measure• Analy5e• Improve• Control


50/125

50MTSU

DMAD / DMAICDMAD / DMAIC

Define

Design

Verify

Measure

Analyze

Define

Improve

Control

Measure

Analyze

Existing ProcessesNew Processes


51/125

51MTSU

De=ne / Selectin ProEectsDe=ne / Selectin ProEects

• The proEect must relate tocustomer satisfaction

• The proEectFs results must reducedefects by some threshold amount• The proEect should achieve some

threshold of cost savin s%


52/125

52MTSU

Criteria for ProEectCriteria for ProEectSelectionSelection

• Does it involve recurrinevents2

• Is the scope narro(2• Do measures e6ist2• Do you have control of the

process2


53/125

53MTSU

De=ne Phase! ContinuedDe=ne Phase! Continued

• If these criteria are met then>$@ Identify the customers involved! both

internal and e6ternal to the function%&@ 'ind out (hat the customerFs CTFsare ?Critical to Quality! Critical toDelivery! Critical to Cost! etc@%)@ De=ne the proEect scope and oals%*@ Map the process to be improved%


54/125

54MTSU

De=ne Phase / ToolsDe=ne Phase / Tools

• $r!+ect Charter• Sta"eholder

Analysis• A4nity Dia ram• SIPBC

• oice of theCustomer• CT Tree

• Gano Model• S8BT Analysis

• Cause/and/30ectDia rams

• SupplierSe mentation

• ProEectMana ement


55/125

55MTSU

CharterCharter

• Problem statement• #usiness case• 1oals! milestones!

success criteria! Hdeliverables

• ProEect scope boundaries

• 9oles H responsibilities• Sta"eholder support

approval needed


56/125

56MTSU

De=ne PhaseDe=ne PhaseCharter Development Charter Development

Charter < An a reement bet(eenmana ement and proEect teammembers about (hat the team (illaccomplish%

h dCh P idi


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57MTSU

Charter> ProvidinCharter> ProvidinDirectionDirection

ou have to be

careful if youdonFt "no((here youFre

oin ! %%%because youmi ht not et

there%


58/125

h


59/125

59MTSU

De=ne PhaseDe=ne PhaseCharter – What it doesCharter – What it does

• Provides overvie( of purpose• Describes (hy you are (or"in on

this proEect ?business case@• De=nes scope of proEect• Determines deliverables• De=nes measures of success• Determines resources available


60/125

60MTSU

#usiness Case#usiness Case

$!tential,m-r!.ement

$!tential ,m-act

Improve Juality 9educe cost! inventoryImprove BTD Shorta es ! inventory

Select better suppliers Q! K! T! reduceinventory

Implement ratinsystem

Improve supplye4ciency! bettersu liers


61/125

61MTSU

De=ne Phase / ToolsDe=ne Phase / Tools

• ProEect Charter• Sta"eholder

Analysis• A4nity Dia ram• SIPBC•

oice of theCustomer• CT Tree

• Gano Model• S8BT Analysis

• Cause/and/30ectDia rams

• SupplierSe mentation

• ProEectMana ement


62/125

62MTSU

SIPBC 36ampleSIPBC 36ample

/u--liers ,n-uts $r!cesses )ut-uts Cust!mers

Bps M t SupplierPerf%

/u--lier.aluati!n

Survey Bps M t

#uyers Complaints

9atinsystem

#uyers

3n r % Tech9eJts

ImprovedSupplierPerform%

3n ineerin

Mf % 9atinsystem

Commit%tosuppliers

Mf %


63/125


64/125

64MTSU

De=ne ButputsDe=ne Butputs

• Bnce completed! the De=ne Phaseshould ans(er the follo(in

Juestions>$@ 8ho is the customer2&@ 8hat matters2

)@ 8hat is the scope2*@ 8hat defect am I tryin to reduce2,@ 8hat are the improvement tar ets2

D PhD Ph


65/125

65MTSU

De=ne PhaseDe=ne Phase Toll 1ate 9evie( Toll 1ate 9evie(

• Submit proEect storyboard tosponsor

• Toll 1ate revie( presentation• Sponsor provides feedbac"• ProEect corrections are made prior

to proceedin to the ne6t phase


66/125

66MTSU

The Measure Phase The Measure Phase

• Purpose< To collect current performance of the

process identi=ed in the De=ne phase

< This data is used to determinesources of variation and serve as abenchmar" to validate improvements


67/125

67MTSU

MeasurementsMeasurements

• #ene=ts of havin ood data needto out(ei h the costs of ettin it

• 8hat does this measure do for theProEect2


68/125

68MTSU

The Measure Phase The Measure Phase

• Upon completion of the measure phase!ProEect Teams (ill have>

L A plan for collectin data that speci=es thetype of data needed and techniJues forcollectin the data

L A validated measurement system thatensures the accuracy and consistency ofthe data collected

L A su4cient data set for problem analysis


69/125

69MTSU

Measure / Gey ConceptsMeasure / Gey Concepts

• Measurement• ariation

< 36ists naturally in any process and is thereason Si6 Si ma proEects are underta"en

• Data• Data Collection Plan

• Measurement System Analysis< 3nsures measurement techniJues are

reproducible and repeatable


70/125

70MTSU

9ecordin Measurements9ecordin Measurements

• ) sta es< The output sta e

L These tell ho( (ell customer needs arebein met

< Parts of the processL These are ta"en at critical points in the

process< The input sta e

L These evaluate contributions to the processthat are turned into value for the customer


71/125

71MTSU


• Butput Sta e< Shorta es

< ine shutdo(ns< Quality < discrepant material< Material price variances

< Internal customer survey


72/125

72MTSU


• Parts of the process< ProEect milestones

< Supplier ship on time performance< Supplier BTD< Supplier internal throu hput yield

< Supplier su ested cost reductions


73/125

73MTSU


• The input sta e< Supplier base si5e

< #uyers (ith de rees< of spend covered by TCFs< of spend from reverse auction

< Supplier 'M3AFs


74/125

74MTSU

Bperational De=nitionsBperational De=nitions

8alter She(hart! the inventorof statistical process control!

believed his (or" onoperational de=nitions to be of

reater importance than his(or" on control charts%


75/125

75MTSU


NAn !-erati!nal %e niti!n is a

procedure a reed upon fortranslation of a concept intomeasurement of some "ind%O

Bperational de=nitions should bevalid and reliable%

Demin ! $ -


76/125

76MTSU

Bperational De=nitions2Bperational De=nitions2

Slump! I ainFt in noslump% I Eust ainFt

hittin %


77/125

77MTSU


• Bn/Time Delivery• Bn/Time Payment

• ate• Defective• Clean• 1ood communication• 3n ineerin support

Do you, your management, and yourDo you, your management, and your

suppliers agree on these definitions?suppliers agree on these definitions?


78/125

78MTSU

Determinin Data TypeDeterminin Data Type

• 8hat do (e (ant to "no(2

• 9evie( materials developed durindesi n phase

• 8hat characteristics do (e need tolearn more about2


79/125

79MTSU

Data Collection PlanData Collection Plan

• 8hat data (ill be collected2

• 8hy is it needed2• 8ho is responsible2• +o( (ill it be collected2• 8hen (ill it be collected2• 8here (ill it be collected2


80/125


81/125

81MTSU

Analy5e PhaseAnaly5e Phase

• The analy5e phase allo(s theProEect Team to tar etimprovement opportunities by

ta"in a closer loo" at the data%


82/125

82MTSU


• Capability Analysis / establishin currentperformance level

• 1raphical Analysis / a visual indicationof performance usin raphs

• 9oot Cause Analysis < developin ahypothesis about the causes of variation

• 9oot Cause eri=cation < verifyin thatthe planned action (ill enerate thedesired improvement


83/125

83MTSU

Process CapabilityProcess Capability

8hen selectin a process toperform an operation! the inherent

variability of process output shouldbe compared to the ran e ortolerances allo(ed by thedesi nerFs speci=cations%


84/125

84MTSU

Process CapabilityProcess Capability

#ower S peci(ication

!pper Speci(ication

A signi(icant portion o( the proce ss output(alls outside o( the speci(ication width

In other words) is theprocess capa$le o(producing the item

withinspeci(ications*

uch o( the process output(its within speci(ication width

Almost all o( the process output(its within the speci(ication width

process distribution



85/125

85MTSU

Analy5e PhaseAnaly5e Phase Cause +ypothesesCause +ypotheses

• Identifyin Bbvious Process Problems< Disconnects< #ottlenec"s< 9edundancies< Unnecessary distance< 9e(or"

< Decision points

Process Map Review



86/125

86MTSU


• Quantifyin alue/Added Steps

< alue/Addin< alue/3nablin< 7on/ alue/Addin

Process Map Review



87/125

87MTSU


• Process Time Analysis

< 8or" time ?value! but often only , @< 8ait ?H Jueue@ time ?usually dominates@< Setup time ?tremendous levera e@< Move time ?process dependent@



88/125

88MTSU

Analy5e PhaseAnaly5e Phase Tools Tools

• #rainstormin % % % And beyondR• Process Maps

• Cause/and/30ect Dia rams• 'ocused Problem Statement• Statistical Tools


89/125

3lements of Improve3lements of Improve


90/125

90MTSU

3lements of Improve3lements of ImprovePhasePhase

1enerate ImprovementAlternatives

Create a NShould #eO Process MapConduct 'M3APerform Cost #ene=t Analysis

Pilotalidate Improvement


91/125

'ailure Modes 30ects'ailure Modes 30ects


92/125

92MTSU

ailure Modes 30ectsailure Modes 30ectsAnalysisAnalysis

• Identify failure modes < !o" can this product or process #ail$

• Identify failure e0ects < Whathappens "hen this #ailure occurs$

• Identify potential causes of thee0ects H their probability of

occurrin %• 9ate the li"elihood of detectin the

occurrence%

3'M3A / B


93/125

93MTSU

'M3A / Butput'M3A / Butput

• 9an"ed list of products thatcontribute to ris"

•ist of actions and personsresponsible for addressin the ris"

• 9evised ran"ed priority list

Improvement PhaseImprovement Phase


94/125

94MTSU

Improvement PhaseImprovement PhaseMethodsMethods

!"#

#"#

#"##"#

!"#!"#

Process not centeredand not in control

Process centeredand in control

Evidence o( additionalimprovements

9evie( of Improvement9evie( of Improvement


95/125

95MTSU

9evie( of Improvement9evie( of ImprovementPhasePhase

• 1enerate ImprovementAlternatives

• Create a NShould #eO Process Map• Conduct 'M3A• Perform Cost #ene=t Analysis

• Pilot• alidate Improvement

1eneratin Improvement1eneratin Improvement


96/125

96MTSU

1eneratin Improvement1eneratin ImprovementAlternativesAlternatives

De=ne Improvement Criteria

1enerate Possible Improvements3valuate Improvements and Ma"e#est Choice

ilPil


97/125

97MTSU

PilotPilot

#ene=ts of Pilot• Determine best (ay to implement the

improvement• o(ers ris" of failure• Increases opportunity for feedbac"• Bbtain buy/in from a0ected personnel

• Provides opportunity to revise theimprovement before fullimplementation


98/125

98MTSU

Sta"eholder AnalysisSta"eholder Analysis

$e!-le !rr!u-s

Le.el !#

C!mmitment

#uy Mf 3n

3nthusiastic Support+elp it (or"Compliant+esitantIndi0erentUncooperative

Bpposed

9evie( of Implementation9evie( of Implementation


99/125

99MTSU

9evie( of Implementation( pPhasePhase

• 1enerate ImprovementAlternatives

• Create a NShould #eO Process Map• Conduct 'M3A• Perform Cost #ene=t Analysis

• Pilot• alidate Improvement

Control PhaseControl Phase


100/125

100MTSU

8hy is it important28hy is it important2

• The Control Phase be ins as theproEect team tries to eliminate

errors by “Mistake Proo ng” their improvement alternative%• Mista"e Proo=n attempts to

eliminate the opportunities forerror%


101/125


102/125

C t l Ph )C t l Ph )


103/125

103MTSU

Control Phase )Control Phase )

• The Control Phase ends (hen>$@ Standard Bperatin Procedures havebeen updated%

&@ Process Bperators! the people (ho dothe Eob! have been trained for the ne(process%

• Bnce completed! the Control Phase

should sustain the ains the proEectmade (hile implementin on oinprocess controls%

C t l Ph *C t l Ph *


104/125

104MTSU

Control Phase *Control Phase *

• 8hen is a proEect complete2$@ 8hen other #lac" #elts can see

the on oin controls (or"&@ 8hen the customer sees theresults)@ 8hen the business sees themoney%

Si6 Si maSi6 Si ma


105/125

105MTSU

Si Sigma PeopleSi Sigma People

• 36ecutives• Champions ?deployment! proEect@

• Master #lac" #elts• #lac" #elts• 1reen #elts



106/125

106MTSU

MethodsMethods

• Durin the Control Phase the ProEectteam (ill>$@ Develop a plan to ma"e sure themeasurement system ill remainrele!ant over the lon term%&@"sta#lis$ Control C$arts the processo(ner (ill use to mana e the process%)@ Create a Control Plan to addresssituations that mi ht cause the process tomove out of control%

Statistical Process ControlStatistical Process Control


107/125

107MTSU

?SPC@?SPC@

• Chance variations are the many sources ofvariation (ithin a process that is in

statistical control% They behave li"e aconstant system of random chance causes%

• If only natural causes of variation arepresent! the output of a process forms adistribution that is stable over time and ispredictable%



108/125

108MTSU

?SPC@?SPC@

• Assi nable variation in a process canbe traced to a speci=c reason%

Machine (ear MisadEusted eJuipment 'ati ued or untrained (or"ers

• If assi nable causes of variation are

present! the process output is notstable over time and is not predictable%

SPC / Assi nable Ca sesSPC / Assi nable Causes


109/125

109MTSU

SPC / Assi nable CausesSPC / Assi nable Causes

The operational de=nition ofassi nable variation is variation

that causes out/of/control pointson a control chart%

7atural Patterns or ariations7atural Patterns or ariations


110/125

110MTSU

7atural Patterns or ariations7atural Patterns or ariations

7atural patterns e6hibit the follo(in characteristics>

• Most of the points are near the centerline%• A fe( points spread out and approach the control

limits%• 7one ?or only on rare occasions@ of the points

e6ceeds the control limits%

%e#erence& Statistical Quality Control +andboo" ' Western Electric

Unnatural Patterns orUnnatural Patterns or


111/125

111MTSU

ariationsariations

Unnatural patterns e6hibit the follo(in characteristics>

• Absence of points near the centerline produces apattern "no(n as a Nmi6ture%O

• Absence of points near the control limits produces anunnatural pattern "no(n as Nstrati=cation%O

• Presence of points outside of the control limits

produces an unnatural pattern "no(n as Ninstability%O%e#erence& Statistical Quality Control +andboo" ' Western Electric

Tests for Unnatural PatternsTests for Unnatural Patterns


112/125

112MTSU

Tests for Unnatural Patterns Tests for Unnatural Patterns

• Instability< & single point falls outsi'e of t$e 3 sigma control

limits(< T(o out of three successive points fall in the outer one third

of the control limits%< 'our out of =ve successive points fall in the outer t(o thirds

of the control limits%< 3i ht successive points fall on one side of the centerline%

• Systematic variable< A lon series of points are hi h! lo(! hi h! lo( (ithout

interruption%%e#erence& Statistical Quality Control +andboo" ' Western Electric



113/125

113MTSU

8hy use avera es28hy use avera es2

• To create a normal distribution

• Avera es are more sensitive tochan e than individuals

Central imit TheoremCentral imit Theorem


114/125

114MTSU

SimulationSimulation

The distribution ofa sampleapproachesnormal even(hen the parentpopulation is notnormallydistributed%



115/125

115MTSU

!"#

#"#

'+"hart

!"#

#"#

R+chart

Dri(t Detected

,o dri(t detected

process mean drifting upward

ProcessDistribution

Mean and 9an e ChartsMean and 9an e Charts


116/125


117/125



118/125

118MTSU

Control PlansControl Plans

• Structured approach for desi ninvalue added control methods

• Control actions necessary toensure output Juality

• May include controls any(here in

the process



119/125

119MTSU

9adar Chart9adar ChartProduct Features -

Data Entry Errors

Days Late

Savings by Project


120/125

'inal Toll 1ate 9evie('inal Toll 1ate 9evie(


121/125

121MTSU

inal Toll 1ate 9evie(inal Toll 1ate 9evie(

• Submit proEect storyboard to sponsor• Toll 1ate revie( presentation

< Did the team carry out the a reed upontas"s2

< Did the team achieve the desired results2

• Sponsor provides feedbac"

• ProEect corrections are made prior totermination or proceedin to ne6t cycle

8hy TQM 'ails8hy TQM 'ails


122/125

122MTSU

8hy TQM ails8hy TQM ails

• ac" of Top Mana ement Commitment

• 8asted 3ducation H Trainin

• ac" of Short/Term 9esults

• 'ailure to 3mpo(er 3mployees

Brown, Hitchcock, and Willard - 1994

"ommitment!p -ront

Appropriate.raining

Bi "ase.hreshold

"harter

8hy Si6 Si ma 'ails8hy Si6 Si ma 'ails


123/125

123MTSU

y?and it does@?and it does@

• ac" of TopMana ementCommitment

• Interdepartmental cross/functional issues

• Communication

• People



124/125

124MTSU


im all Bullingt!n& $h*'*

9eferences


125/125

#oo"s>• Si6 Si ma Poc"et 1uide ?9ath H Stron Fs@• The #lac" #elt Memory ;o er ?1BA QPC@• Si6 Si ma ?+arry and Schroeder@• Implementin Si6 Si ma ?#reyfo le@• The Si6 Si ma 8ay Team 'ieldboo" ?Pande! et al@• The ision of Si6 Si ma> A roadmap for brea"throu h

?+arry@• 8hy TQM 'ails and 8hat To Do About It ?#ro(n! +itchcoc"!

H 8illard@

8eb sites>• (((%isi6si ma%com• (((% e%com si6si ma

(((% J%
http://www.ge.com/sixsigmahttp://www.asq.org/http://www.ge.com/sixsigma

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