西格玛 标准培训教材（英文PPT ）

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. Harry President Taking GE From a 3 to

2、a 6 Company Will Save $10.5 Billion per Year!,Why “Six Sigma”?,Proven Successful in “Quality-Demanding” Industries e.g., Motorola, Texas Instruments (many process steps in series) Proven Method to Reduce Costs Highly Quantitative Method Science and Logic Instead of Gut Feel Includes Manufacturing &

3、Service (close to customer) and Provides Bridge to Design for Quality Concepts Has 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

4、 Architects Previously Headed Quality Function at ABB and Motorola 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

5、,Learning from Those Who Have had Success With 6Will Accelerate 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 f

6、or Defects in Products or Services,“Critical to Quality” Characteristics or the Customer Requirements for a Product or Service,Count Defects or failures to meet CTQ requirements in all process steps,Define Defect Opportunities,Any step in the process where a Defect could occur in a CTQ,Arrive at DPM

7、O,Use the SIGMA TABLE,Convert DPMO to Sigma,Defects Per Million Opportunities,2 3 4 5 6,308,537 66,807 6,210 233 3.4,PPM,Defects per Million of Opportunity,Sigma Level,6 Overview,Measurement System,2 3 4 5 6,308,537 66,807 6,210 233 3.4,PPM,SIGMA LEVEL,DEFECTS per MILLION OPPORTUNITY,IRS Tax Advice,

8、Best Companies,Airline Safety,Average Company,GE,Average Company in 3 to 4Range,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 of People Arriving at CRD,Time,7:00,7:15,7:30,7:45,8:0

9、0,8:15,8:30,8:45,9:00,9:15,6 Overview,Problem Solving Approach,6Helps us Identify and Reduce 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,

10、On-Target,6 Overview,Problem Solving Approach,“Lower Specification Limit”,“Upper Specification 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

11、 Mapping Develop and Validate Measurement Systems,Benchmark and Baseline Processes 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 Tes

12、t for Improvement in Centering Use of Brainstorming and Action Workouts,Set up Control 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 书签,6 Overview,Disciplined Change Process,A

13、 New Set of QUALITY MEASURES,Customer Satisfaction Cost of Poor Quality Supplier Quality 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,Tabul

14、ation of GE Six Sigma Results,Benefit Target & Update,Current benefits level 10.865 MM,QPID loading : Carryover from 1999 : 4.059 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,Identifyin

15、g and Fixing Root Causes Will Help us Obtain the Desired Output,f (X),Y =,Process Capability,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

16、,“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,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,shi

17、ft,C,D,A,B,0.5 1.0 1.5 2.0 2.5,1 2 3 4 5 6,C O N T R O L,POOR,GOOD,TECHNOLOGY,POOR,GOOD,short term,Problem Could be Control, Technology 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,Ta

18、rget,A 3 Process,The Potential Performance of a Process, if it Were on Target,6 Overview,Long Term Capability (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

19、 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 Equivalent 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 Proc

20、ess,The Definitions of Yield,First Time Yield (Yft),=,Units Passed,Units Tested,=,65,70,=,0.93,Rolled Thruput 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 ),

21、6 Overview,Yield Exclusive of Rework,Probability of Zero Defects,Average Yield of All Processes,6 Overview,As the Number of Operations Increases, 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

22、Process,p (x),Defects,Benchmark,Baseline,Entitlement,Baselining = Current Process / Benchmarking = Ultimate Goal,Some Basic 6-Related Tools,6 Overview,Scatter Diagram,Over Slept,Car Would Not Start,Weather,Family Problems,Other,Pareto Diagram,Frequency of Occurence,Reasons for Being Late for Work,Ar

23、rival Time at Work,Time Alarm Went Off,Materials,People,The Histogram,6 Overview,Some Basic 6-Related Tools,Plot of Daily Arrival Time,9:15,7:00,7:15,7:30,7:45,8:00,8:15,8:30,8:45,9:00,Average Value,Number of People Arriving at CRD,Time,6 Overview,LCL,UCL,Range Chart,Some Basic 6-Related Tools,Monit

24、ors Changes in Average or Variation Over Time,Design of Experiments,6 Overview,SCREENING,OPTIMIZATION,CHARACTERIZATION,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 Usefu

25、l When Studying Relatively Uncomplicated Effects & Interactions,For Experiments Involving Only 2 or 3 Factors Useful 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

26、,3 2 1,7:15,7:30,7:45,8:00,8:15,Route,Combination Selected,The Result,Use Route 2 and Leave at 7:15 to Reach Goal,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 “Sw

27、eet Spot” in Parameter Space,The Variables,Time of Departure from Home & Route Taken to Work,The Result,A Better Combination Allowing 15 More Minutes of Sleep!,Actual Commuting Time Averages (minutes),3 2 1,7:15,7:30,7:45,8:00,8:15,Route,17 20 23 21 19,15 18 20 19 16,12 15 21 20 18,Original Conclusi

28、on,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 Customer? Rise Texture Smell Freshness Taste,Y = Taste!,6 Overview,Measure,6 Overview,How Could We Measure Taste (Y)? Panel of Tasters Ratin

29、g 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 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 Sam

30、e Loaf in Different Samples Consistent Ratings* on Pieces from Same Loaf = “Repeatability” Consistent Ratings* on Samples 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

31、 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 Ratings (from our panel),=,.292,292,000 Defects per 1,ooo,ooo Loaves,OR,Analyze,How Do We Approach This? Bake Several Loaves Under “Normal” Conditions Have Taster Panel Again Do the Rati

32、ng Average Rating is 7.4 But Variation is too Great for a 6 Process,6 Overview,How do we Define Improvement? Benchmark 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!,1,000,00

33、0 - 100,000 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,000 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 - . . . . . . . . . . . . .

34、. . . . . . . . . . . . . . . . 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 Potential Sources of Variation (Xs)? Have the Chefs Brainstorm Some Likely Ones Might be: - Amount of Salt Us

35、ed - 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 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 “Vital Few”,Improve,6 Overview,How