1、An OverviewNot a lot of Details!,6 Overview,“Six Sigma”,If we cant express what we know in the form of numbers,we really dont know much about it.If we dont know much about it, we cant control it.If we cant control it, we are at the mercy of chance.,Mikel J. Harry President & CEO Six Sigma Academy, I
2、nc.,A Rigorous Method for Measuring & Controlling Our Quality,“.will bring GE to a whole new level of quality in a fraction of the time it would have taken to climb the learning curve on our own.”,John F. Welch, Jr. 1995 GE Annual Report,6 Overview,What Does “Sigma” Mean?,Sigma is a Measure of the C
3、onsistency of a Process,It (is Also the 18th Letter in the Greek Alphabet!,Why Does GE Need A Quality Initiative?,GE Raising The Bar New Goal to be “Best in the World” vs. #1 or #2 Customers are Expecting More, we Must Deliver “Ship-and-fix” Approach no Longer Tolerated in the Market Aim to Speed Pa
4、st Traditional Competitors in 5 Years Goal Consistent with Reduced Total CostsWe Must Acknowledge Our Vulnerabilities Poor Quality That Impacts Customers Problems with NPI Too High Internal Costs,6 Overview,We Need a Major Initiative to Move FromWhere we Are to Where we Want to be,6 Overview,Why Doe
5、s GE Need A Quality Initiative?,40%,35%,30%,25%,20%,10%,15%,5%,Cost of Failure (% of Sales),Defects per Million,3.4,233,6210,66,807,308,537,500,000,Sigma,6,5,4,3,2,1,Estimated Cost of Failure in US Industry is 15% of Sales; Taking GE From a 3 to a 6 Company Will Save $10.5 Billion per Year!,Why “Six
6、 Sigma”?,Proven Successful in “Quality-Demanding” Industries e.g.,Motorola, Texas Instruments (many process steps in series)Proven Method to Reduce CostsHighly Quantitative Method Science and Logic Instead of Gut FeelIncludes Manufacturing & Service (close to customer) and Provides Bridge to Design
7、for Quality ConceptsHas Support and Commitment of Top Management,It Works!,6 Overview,6 is Several Orders of Magnitude Better Than 3!,Sigma: A Measure of Quality,6 Overview,Where Does “Six Sigma” Come From?,Mikel J. Harry one of the Original ArchitectsPreviously Headed Quality Function at ABB and Mo
8、torolaNow President/CEO of Six Sigma Academy in Phoenix, ArizonaHas Consulted for Texas Instruments, Allied Signal (and others)Currently Retained by GE to Teach the Implementation,Deployment and Application of Six Sigma Concepts & Tools,Learning from Those Who Have had Success With 6Will Accelerate
9、its Implementation at GE,6 Overview,So.What is Six Sigma?,“THE SIX SIGMA BREAKTHROUGH STRATEGY”,6 Overview,How Do We Arrive at Sigma?,Measuring & Eliminating Defects is the “Core” of Six Sigma,Measurement System,Identify the CTQs,Look for Defects in Products or Services,“Critical to Quality” Charact
10、eristics orthe CustomerRequirements for aProduct or Service,Count Defectsor failures tomeet CTQ requirements inall process steps,Define Defect Opportunities,Any step in theprocess where aDefect could occurin a CTQ,Arrive at DPMO,Use the SIGMATABLE,Convert DPMO to Sigma,Defects Per MillionOpportuniti
11、es,2 3 4 5 6,308,53766,8076,2102333.4,PPM,Defects perMillion of Opportunity,SigmaLevel,6 Overview,Measurement System,23456,308,53766,8076,2102333.4,PPM,SIGMALEVEL,DEFECTS perMILLION OPPORTUNITY,IRS Tax Advice,Best Companies,Airline Safety,Average Company,GE,Average Company in 3 to 4Range,Some Sigma
12、“Benchmarks”,6 Overview,Measurement System,A Graphic/Quantitative Perspective on Variation,Average Value,Many Data Sets Have a Normal or Bell Shape,Number of People Arriving at CRD,Time,7:00,7:15,7:30,7:45,8:00,8:15,8:30,8:45,9:00,9:15,6 Overview,Problem Solving Approach,6Helps us Identify and Reduc
13、e VARIATION due to:- Insufficient Process Capability- Unstable Parts & Materials- Inadequate Design Margin,Target,USL,LSL,Target,USL,LSL,Target,USL,LSL,Center Process,Reduce Spread,Off-Target,Unpredictable,On-Target,6 Overview,Problem Solving Approach,“Lower Specification Limit”,“Upper Specification
14、 Limit”,Less Variation Means Fewer Defects & Higher Process Yields,6 Overview,Problem Solving Approach,Key Components of “BREAKTHROUGH STRATEGY”,Identify CTQ & CTP (Critical toProcess) VariablesDo Process MappingDevelop andValidate MeasurementSystems,Benchmark andBaseline ProcessesCalculate Yieldand
15、 SigmaTarget Opportunitiesand EstablishImprovement GoalsUse of Pareto Chart& Fishbone Diagrams,Use Design ofExperimentsIsolate the “Vital Few” from the “Trivial Many” Sources of VariationTest for Improvementin CenteringUse of Brainstormingand Action Workouts,Set up ControlMechanismsMonitor ProcessVa
16、riationMaintain “In Control”ProcessesUse of ControlCharts andProcedures,A Mix of Concepts and Tools,Will Also Integrate with NPI Process,6 Overview,Disciplined Change Process,A New Set of QUALITY MEASURES,Customer SatisfactionCost of Poor QualitySupplier QualityInternal PerformanceDesign for Manufac
17、turability,Will Apply to Manufacturing & Non-Manufacturing Processes and be Tracked & Reported by Each Business,6 Overview,Structure,6 Projects with the GE Businesses,Tabulation of GE Six Sigma Results,Benefit Target & Update,Current benefits level 10.865 MM,QPID loading : Carryover from 1999 : 4.05
18、9 Completed Projects 2000 : 3.313 Active Projects 2000 : 3.285 Total : 10.865 MM,Key Concepts & Tools,6 Overview,6 Overview,Changing Focus From Output to Process,Identifying and Fixing Root Causes Will Help us Obtain the Desired Output,f (X),Y =,Process Capability,6 Overview,Sustained Capabilityof t
19、he Process(long term),USL,T,Inherent Capabilityof the Process(short term),LSL,Target,Over Time, a “Typical” Process Will Shift and Drift by Approximately 1.5,6 Overview,“Short Term Centered” versus “Long Term Shifted”,Six Sigma Centered,LSL,T,Process Capability,.001 ppm,.001 ppm,+6,LSL,USL,T,3.4 ppm
20、,Six Sigma Shifted 1.5,Process Capability,Higher Defect Yield in Long Term Process Capability than Short Term Process Capability,-6,6 Overview,Tying it All Together,shift,C,D,A,B,0.51.01.52.02.5,1 2 3 4 5 6,C O N T R O L,POOR,GOOD,TECHNOLOGY,POOR,GOOD,short term,Problem Could be Control, Technology
21、or Both,6 Overview,Short Term Capability,Short Term Capability Ratio,(Cp),Cp =,LSL,-,6,USL,Example,6,3.0,-,( - 3.0,Cp =,Cp =,1,LSL,USL,2.5,0.5,3.0,Process Mean,T,Target,A 3 Process,The Potential Performance of a Process, if it Were on Target,6 Overview,Long Term Capability (Cpk),Long Term Capability
22、 Ratio,Example,Cp =,1 (previous chart),Target,=,-0.5,=,0,Cpk,1 -,(-0.5,-,0,3,=,Cpk,=,0.83,-,Off-Target Penalty,The Potential Performance of a Process, Corrected for an Off-Target Mean,LSL,USL,2.5,0.5,3.0,Process Mean,T,Target,A 3 Process,6 Overview,Z - Scale of Measure,Z,=,A Unit of Measure Equivale
23、nt to the Number of Standard Deviations that a Value is Away from the Target Value,-3.0,-0.5,3.0,Z - Values,USL,LSL,2.5,0.5,3.0,= Process Mean,Z,T,Target,0,A 3 Process,The Definitions of Yield,First Time Yield (Yft),=,Units Passed,Units Tested,=,65,70,=,0.93,Rolled Thruput Yield (Yrt),=,(Yield 1),(Y
24、ield 2),(Yield 3) . . . .,=,91,82,65,70,(,(,(,(,),),),),=,0.65,100,91,70,82,Normalized Yield (Ynm),=,=,1/n,(Yrt),(0.65),1/4,=,0.89,( n: Total Number of Processes ),6 Overview,Yield Exclusive of Rework,Probability of Zero Defects,Average Yield of All Processes,6 Overview,As the Number of Operations I
25、ncreases, a High Rolled Yield Requires a High for Each Operation,5,4,3,6,Process Mean Shifted 1.5at Each Operation,6 Overview,Baselining & Benchmarking an Existing Process,p (x),Defects,Benchmark,Baseline,Entitlement,Baselining = Current Process / Benchmarking = Ultimate Goal,Some Basic 6-Related To
26、ols,6 Overview,Scatter Diagram,Over Slept,Car WouldNot Start,Weather,FamilyProblems,Other,Pareto Diagram,Frequencyof Occurence,Reasons for Being Late for Work,Arrival Time at Work,Time Alarm Went Off,Materials,People,The Histogram,6 Overview,Some Basic 6-Related Tools,Plot of Daily Arrival Time,9:15
27、,7:00,7:15,7:30,7:45,8:00,8:15,8:30,8:45,9:00,Average Value,Number of People Arriving at CRD,Time,6 Overview,LCL,UCL,Range Chart,Some Basic 6-Related Tools,Monitors Changes in Average or Variation Over Time,Design of Experiments,6 Overview,SCREENING,OPTIMIZATION,CHARACTERIZATION,For ExperimentsInvol
28、ving a LargeNumber of FactorsUseful in Isolatingthe “Vital Few “ fromthe “Trivial Many”,For ExperimentsInvolving a RelativelySmall Number of FactorsUseful When StudyingRelatively UncomplicatedEffects & Interactions,For ExperimentsInvolving Only 2 or 3 FactorsUseful When StudyingHighly ComplicatedEff
29、ects & Relationships,DOE is More Effective Than Testing One Factor at a Time,6 Overview,Using the “One Factor at a Time” Approach,Time of Departure,321,7:15,7:30,7:45,8:00,8:15,Route,CombinationSelected,The Result,Use Route 2 and Leave at 7:15 to Reach Goal,6 Overview,Using “Design of Experiments” (
30、DOE),Time of Departure,DOE (i) Better Accounts for Interactive Variables Missed by “One Factor at a Time”, and (ii) Efficiently Searches for “Sweet Spot” in Parameter Space,The Variables,Time of Departure from Home & Route Takento Work,The Result,A Better Combination Allowing 15 More Minutes of Slee
31、p!,Actual Commuting Time Averages (minutes),321,7:15,7:30,7:45,8:00,8:15,Route,17 20 23 21 19,15 18 20 19 16,12 15 21 20 18,Original Conclusion,Best Combination “Sweet Spot”,A Practical Example(The “Cookbook”),6 Overview,6.and Baking Bread,Using a 12 Step Process,6 Overview,What is Important to the
32、Customer?RiseTextureSmellFreshnessTaste,Y = Taste!,6 Overview,Measure,6 Overview,How Could We Measure Taste (Y)?Panel of TastersRating Systemof 1 to 10Target: AverageRating at 8Desired: No Individual Ratings(“defects”) Below 7,Y = 1 2 3 4 5 6 7 8 9 10,Target,Defects,Worst,Best,But.Is this the Right
33、System?,Measure,6 Overview,How Could We Approach This?Blindfolded Panel RatesSeveral Loaf Samples Put “Repeat” Pieces from Same Loaf in Different Samples Consistent Ratings* onPieces from Same Loaf = “Repeatability”Consistent Ratings* onSamples Across the Panel = “Reproducibility”,“Repeatability” &“
34、Reproducibility” Suggest Valid Measurement Approach,Panel Member,Loaf 1 Loaf 2 Loaf 3,A 5 8 9B 4 9 1C 4 9 2D 8 9 8E 4 8 2F 5 9 1G 8 9 2,* Within,One Taste Unit,Measure,6 Overview,This is a 3 Process!,7 Defects (ratings below 7),24 Ratings (from our panel),=,.292,292,000 Defects per 1,ooo,ooo Loaves,
35、OR,Analyze,How Do We Approach This?Bake Several LoavesUnder “Normal”ConditionsHave Taster PanelAgain Do the RatingAverage Rating is 7.4But Variation is too Great for a 6 Process,6 Overview,How do we Define Improvement?Benchmark the CompetitionFocus on Defects( i.e. taste rating 7)Determine Whatis an
36、 “AcceptableSigma Level”Set Improvement Objectives Accordingly,Maybe a 5 Process Will Suffice!,1,000,000 - 100,000 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,000 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 - . . . . . . . . . . . . . . . . . . . . . . . . .
37、. . . . 100 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 -,2 3 4 5 6 7,“BETTER BREAD” Baking Process,Best Competitor,Range for Improvement,Defects Per Million,Sigma Scale,Analyze,6 Overview,How do we Determine the Potent
38、ial Sources of Variation (Xs)?Have the Chefs BrainstormSome Likely Ones Might be:- Amount of Salt Used- Brand of Flour- Baking Time- Baking Temperature- Brand of Yeast,Multiple Sources: Chefs, Suppliers, Controls,Analyze,6 Overview,How do we Screen for Causes of Variation (Xs)?Design an ExperimentUs
39、e Different Sourcesof Potential VariationHave Panel Ratethe Bread Used inthe ExperimentResults Lead to the“Vital Few” Causes,Source,Conclusion,Negligible,Major Cause,Negligible,Major Cause,Negligible,Focus on The “Vital Few”,Improve,6 Overview,How do we Find the Relationship Between the “Vital Few”
40、(Xs) and Taste (Y)?Conduct a More Detailed ExperimentFocus: Oven Temperature from 325 to 375 and 3 Brands of FlourRUN# TEMP BRAND1 325 A2 325 B3 325 C4 350 A5 350 B6 350 C7 375 A8 375 B9 375 C,Improve,Note: Time is a Factor Only if Temperature Changes Significantly,6 Overview,But.Is Our Measurement
41、System Correct?,Improve,6 Overview,How Could We Approach This?Need to Verify the Accuracy of OurTemperature GaugesNeed for “Benchmark”Instrumentation forComparisonRent Some Other“High End” GaugesCompare the Results,Verify that our Instruments are Accurate,Control,6 Overview,How Could We Approach Thi
42、s?Check A Number of OvensMonitor TemperaturesOver TimeFocus on the Process CapabilityLook for Degree ofVariation,Variation OK But.Average is High (and the algorithm should be checked),Control,6 Overview,What do we do Going Forward?Check Ovens Dailyfor Temperature LevelsAudit Usage Frequencyof Alternative FlourSupplier (e.g., Brand C)Periodically Reassemblethe Panel to Test Taste Chart the Results,And.Plot the Data Over Time,354353352351350349348,1 3 5 7 9 11 13 15 17 19 21 23 25,Control,
