1、An Overview.Not 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. HarryPresident & CEOSix Sigma Academy,
2、 Inc.,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
3、Consistency of a Process,It (is Also the 18th Letter in the Greek Alphabet!,Why Does GE Need A Quality Initiative?,GE Raising The BarNew Goal to be “Best in the World” vs. #1 or #2Customers are Expecting More, we Must Deliver“Ship-and-fix” Approach no Longer Tolerated in the MarketAim to Speed Past
4、Traditional Competitors in 5 YearsGoal Consistent with Reduced Total CostsWe Must Acknowledge Our VulnerabilitiesPoor Quality That Impacts CustomersProblems with NPIToo High Internal Costs,6 Overview,We Need a Major Initiative to Move From Where we Are to Where we Want to be,6 Overview,Why Does GE N
5、eed 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 Sigma
6、”?,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 for Q
7、uality 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 Architects Previously Headed Quality Function at ABB and Motoro
8、la Now President/CEO of Six Sigma Academy in Phoenix, Arizona Has 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 SuccessWith 6Will Accelerate i
9、ts 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 Defectsin Products orServices,“Critical to Quality” Characteri
10、stics or the Customer Requirements for a Product or Service,Count Defects or failures to meet CTQ requirements in all process steps,Define DefectOpportunities,Any step in the process where a Defect could occur in a CTQ,Arrive at DPMO,Use the SIGMA TABLE,Convert DPMO toSigma,Defects Per Million Oppor
11、tunities,23456,308,537 66,807 6,210 233 3.4,PPM,Defects per Million of Opportunity,Sigma Level,6 Overview,Measurement System,23456,308,537 66,807 6,210 233 3.4,PPM,SIGMA LEVEL,DEFECTS per MILLIONOPPORTUNITY,IRS Tax Advice,Best Companies,Airline Safety,Average Company,GE,Average Company in 3 to 4Rang
12、e,Some Sigma “Benchmarks”,6 Overview,Measurement System,A Graphic/Quantitative Perspective on Variation,Average Value,Many Data Sets Have a Normal or Bell Shape,Number ofPeopleArrivingat 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
13、 and Reduce VARIATION due to: - Insufficient Process Capability - Unstable Parts & Materials - Inadequate Design Margin,Target,USL,LSL,Target,USL,LSL,Target,USL,LSL,CenterProcess,ReduceSpread,Off-Target,Unpredictable,On-Target,6 Overview,Problem Solving Approach,“Lower Specification Limit”,“Upper Sp
14、ecification Limit”,Less Variation Means Fewer Defects & Higher Process Yields,6 Overview,Problem Solving Approach,Key Components of “BREAKTHROUGH STRATEGY”,Identify CTQ & CTP (Critical to Process) Variables Do Process Mapping Develop and Validate Measurement Systems,Benchmark and Baseline Processes
15、Calculate Yield and Sigma Target Opportunities and Establish Improvement Goals Use of Pareto Chart & Fishbone Diagrams,Use Design of Experiments Isolate the “Vital Few” from the “Trivial Many” Sources of Variation Test for Improvement in Centering Use of Brainstorming and Action Workouts,Set up Cont
16、rol Mechanisms Monitor Process Variation Maintain “In Control” Processes Use of Control Charts and Procedures,A Mix of Concepts and Tools,Will Also Integrate with NPI Process,6 Overview,Disciplined Change Process,A New Set of QUALITY MEASURES,Customer Satisfaction Cost of Poor Quality Supplier Quali
17、ty Internal Performance Design for Manufacturability,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
18、,QPID loading : Carryover from 1999 : 4.059Completed Projects 2000 :3.313Active Projects 2000 :3.285Total : 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 Capabilit
19、y,6 Overview,Sustained Capability of the Process (long term),USL,T,Inherent Capability of 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 Capabili
20、ty,.001 ppm,.001 ppm,+6,LSL,USL,T,3.4 ppm,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,CONTROL,POOR,GOOD,TECHNOLOGY,POOR,GOOD,short term,Pr
21、oblem Could be Control, Technology 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 Ca
22、pability (Cpk),Long Term Capability 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 Mea
23、sure,Z,=,A Unit of Measure Equivalent to the Number of StandardDeviations that a Value is Awayfrom 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 T
24、hruput Yield (Yrt),=,(Yield 1),(Yield 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 Exclusiveof Rework,Probability ofZero Defects,Average Yield of All Processes,6 Overvie
25、w,As the Number of Operations Increases, a HighRolled 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 = Ultimat
26、e Goal,Some Basic 6-Related Tools,6 Overview,Scatter Diagram,Over Slept,Car Would Not Start,Weather,Family Problems,Other,Pareto Diagram,Frequency ofOccurence,Reasons for Being Late for Work,Arrival Timeat Work,Time Alarm Went Off,Materials,People,The Histogram,6 Overview,Some Basic 6-Related Tools,
27、Plot of Daily Arrival Time,9:15,7:00,7:15,7:30,7:45,8:00,8:15,8:30,8:45,9:00,Average Value,Number ofPeopleArrivingat 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,CHARACTER
28、IZATION,For Experiments Involving a Large Number of Factors Useful in Isolating the “Vital Few “ from the “Trivial Many”,For Experiments Involving a Relatively Small Number of Factors Useful When Studying Relatively Uncomplicated Effects & Interactions,For Experiments Involving Only 2 or 3 Factors U
29、seful When Studying Highly Complicated Effects & 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,Combination Selected,The Result,Use Route 2 andLeave at 7:15 to Reach Goal,
30、6 Overview,Using “Design of Experiments” (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 Taken to Work,The Result,A Better C
31、ombination Allowing 15 More Minutes of Sleep!,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,BestCombination“Sweet Spot”,A Practical Example (The “Cookbook”),6 Overview,6.and Baking Bread,Using a 12 Step Pr
32、ocess,6 Overview,What is Important to the Customer? Rise Texture Smell Freshness Taste,Y = Taste!,6 Overview,Measure,6 Overview,How Could We Measure Taste (Y)? Panel of Tasters Rating System of 1 to 10 Target: Average Rating at 8 Desired: No Individual Ratings (“defects”) Below 7,Y = 1 2 3 4 5 6 7 8
33、 9 10,Target,Defects,Worst,Best,But.Is this the Right System?,Measure,6 Overview,How Could We Approach This? Blindfolded Panel Rates Several Loaf Samples Put “Repeat” Pieces from Same Loaf in Different Samples Consistent Ratings* on Pieces from Same Loaf = “Repeatability” Consistent Ratings* on Samp
34、les Across the Panel = “Reproducibility”,“Repeatability” &“Reproducibility” Suggest Valid Measurement Approach,Panel Member,Loaf 1 Loaf 2 Loaf 3,A 5 8 9 B 4 9 1 C 4 9 2 D 8 9 8 E 4 8 2 F 5 9 1 G 8 9 2,* Within,One Taste Unit,Measure,6 Overview,This is a 3 Process!,7 Defects (ratings below 7),24 Rati
35、ngs (from our panel),=,.292,292,000 Defects per1,ooo,ooo Loaves,OR,Analyze,How Do We Approach This? Bake Several Loaves Under “Normal” Conditions Have Taster Panel Again Do the Rating Average Rating is 7.4 But Variation is too Great for a 6 Process,6 Overview,How do we Define Improvement? Benchmark
36、the Competition Focus on Defects ( i.e. taste rating 7) Determine What is an “Acceptable Sigma Level” Set Improvement Objectives Accordingly,Maybe a 5 Process Will Suffice!,1,000,000 - 100,000 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,000 - . . . . . . . . . . . . . . . . . . .
37、. . . . . . . . . . 1,000 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 -,2 3 4 5 6 7,“BETTER BREAD” Baking Process,BestCompetitor,Range forImprovement,Defec
38、tsPer Million,Sigma Scale,Analyze,6 Overview,How do we Determine the Potential Sources of Variation (Xs)? Have the Chefs Brainstorm Some 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
39、Overview,How do we Screen for Causes of Variation (Xs)? Design an Experiment Use Different Sources of Potential Variation Have Panel Rate the Bread Used in the Experiment Results Lead to the “Vital Few” Causes,Source,Conclusion,Negligible,Major Cause,Negligible,Major Cause,Negligible,Focus on The “V
40、ital Few”,Improve,6 Overview,How do we Find the Relationship Between the “Vital Few” (Xs) and Taste (Y)? Conduct a More Detailed Experiment Focus: Oven Temperature from 325 to 375 and 3 Brands of Flour RUN# TEMP BRAND 1 325 A 2 325 B 3 325 C 4 350 A 5 350 B 6 350 C 7 375 A 8 375 B 9 375 C,Improve,No
41、te: Time is a Factor Only if TemperatureChanges Significantly,6 Overview,But.Is Our Measurement System Correct?,Improve,6 Overview,How Could We Approach This? Need to Verify the Accuracy of Our Temperature Gauges Need for “Benchmark” Instrumentation for Comparison Rent Some Other “High End” Gauges Compare the Results,Verify that our Instruments are Accurate,Control,6 Overview,How Could We Approach This? Check A Number of Ovens Monitor Temperatures Over Time Focus on the Process Capability Look for Degree of Variation,Variation OK But.Average is High (and the algorithm should be checked),
