6S 标准培训教材ppt

nullnullAn Overview....Not a lot of Details!!null6 Overview “Six Sigma” If we can’t express what we know in the form of numbers, we really don’t know much about it. If we don’t know much about it, we can’t control it. If we can’t control it, we are at the mercy of chance. Mikel J. Harry President & CEO Six Sigma Academy, 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 Reportnull6 Overview What Does “Sigma” Mean? Sigma is a Measure of the Consistency of a ProcessIt (is Also the 18th Letter in the Greek Alphabet!Why Does GE Need A Quality Initiative?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 Past Traditional Competitors in 5 Years Goal Consistent with Reduced Total Costs We Must Acknowledge Our Vulnerabilities Poor Quality That Impacts Customers Problems with NPI Too High Internal Costs6 Overview We Need a Major Initiative to Move From Where we Are to Where we Want to benull6 Overview Why Does GE Need A Quality Initiative?40%35%30%25%20%10%15% 5%Cost of Failure (% of Sales)Defects per Million3.4233621066,807308,537500,000Sigma654321 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”?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 & Service (close to customer) and Provides Bridge to Design for Quality Concepts Has Support and Commitment of Top ManagementIt Works!!!null6 Overview 6 is Several Orders of Magnitude Better Than 3!!!Sigma: A Measure of Qualitynull6 Overview Where Does “Six Sigma” Come From? Mikel J. Harry one of the Original 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 Learning from Those Who Have had Success With 6Will Accelerate its Implementation at GEnull6 Overview So...What is Six Sigma?“THE SIX SIGMA BREAKTHROUGH STRATEGY”null6 Overview How Do We Arrive at Sigma?Measuring & Eliminating Defects is the “Core” of Six SigmaMeasurement SystemIdentify the CTQsLook for 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 DPMO Use the SIGMA TABLEConvert DPMO to Sigma Defects Per Million Opportunities2 3 4 5 6308,537 66,807 6,210 233 3.4PPM Defects per Million of Opportunity Sigma Levelnull6 Overview Measurement System2 3 4 5 6308,537 66,807 6,210 233 3.4PPM SIGMA LEVEL DEFECTS per MILLION OPPORTUNITYIRS Tax AdviceBest CompaniesAirline SafetyAverage CompanyGE Average Company in 3 to 4Range Some Sigma “Benchmarks” null6 Overview Measurement SystemA Graphic/Quantitative Perspective on VariationAverage ValueMany Data Sets Have a Normal or Bell ShapeNumber of People Arriving at CRDTime7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 9:15null6 Overview Problem Solving Approach 6Helps us Identify and Reduce VARIATION due to: - Insufficient Process Capability - Unstable Parts & Materials - Inadequate Design MarginnullTargetUSLLSLTargetUSLLSLTargetUSLLSLCenter ProcessReduce SpreadOff-TargetUnpredictableOn-Target6 Overview Problem Solving Approach“Lower Specification Limit” “Upper Specification Limit”Less Variation Means Fewer Defects & Higher Process Yields null6 Overview Problem Solving ApproachKey Components of “BREAKTHROUGH STRATEGY” Identify CTQ & CTP (Critical to Process) Variables Do Process 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 Test 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 Processnull6 Overview Disciplined Change ProcessA 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 Businessnull6 Overview Structure6 Projects with the GE Businessesnullnullnull Tabulation of GE Six Sigma ResultsnullBenefit Target & UpdateCurrent benefits level @ 10.865 MMQPID loading : Carryover from 1999 : 4.059 Completed Projects 2000 : 3.313 Active Projects 2000 : 3.285 Total : 10.865 MMnullnull Key Concepts & Tools 6 Overview null6 Overview Changing Focus From Output to Process Identifying and Fixing Root Causes Will Help us Obtain the Desired Outputf (X)Y =nullProcess Capability6 Overview Sustained Capability of the Process (long term)USLTInherent Capability of the Process (short term)LSLTargetOver Time, a “Typical” Process Will Shift and Drift by Approximately 1.5null6 Overview “Short Term Centered” versus “Long Term Shifted”Six Sigma CenteredLSLT Process Capability.001 ppm.001 ppm+6 LSLUSLT3.4 ppmSix Sigma Shifted 1.5 Process CapabilityHigher Defect Yield in Long Term Process Capability than Short Term Process Capability -6 null6 Overview Tying it All TogethershiftCDAB0.5 1.0 1.5 2.0 2.51 2 3 4 5 6C O N T R O LPOORGOODTECHNOLOGYPOORGOODshort termProblem Could be Control, Technology or Bothnull6 Overview Short Term CapabilityShort Term Capability Ratio(Cp)Cp = LSL-6USL Example6 3.0-( - 3.0Cp =Cp =1LSLUSL2.5 0.53.0Process Mean TTargetA 3 Process The Potential Performance of a Process, if it Were on Targetnull6 Overview Long Term Capability (Cpk)Long Term Capability RatioExampleCp =1 (previous chart)Target = -0.5 =0Cpk1 - (-0.5-03 =Cpk =0.83-Off-Target PenaltyThe Potential Performance of a Process, Corrected for an Off-Target MeanLSLUSL2.5 0.53.0Process Mean TTargetA 3 Processnull6 Overview Z - Scale of MeasureZ =A Unit of Measure Equivalent to the Number of Standard Deviations that a Value is Away from the Target Value-3.0-0.53.0Z - Values USLLSL2.50.53.0= Process Mean Z TTarget 0A 3 ProcessnullThe Definitions of Yield First Time Yield (Yft)=Units PassedUnits Tested= 65 70=0.93 Rolled Thruput Yield (Yrt)=(Yield 1)(Yield 2)(Yield 3) . . . . = 91 82 65 70(((())))=0.65100 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 ReworkProbability of Zero DefectsAverage Yield of All Processesnull6 Overview As the Number of Operations Increases, a High Rolled Yield Requires a High for Each Operation 5 4 3 6Process Mean Shifted 1.5at Each Operationnull6 Overview Baselining & Benchmarking an Existing Processp (x)DefectsBenchmarkBaseline EntitlementBaselining = Current Process / Benchmarking = Ultimate GoalnullSome Basic 6-Related Tools6 Overview Scatter Diagram Over Slept Car Would Not StartWeather Family ProblemsOtherPareto Diagram Frequency of OccurenceReasons for Being Late for WorkArrival Time at WorkTime Alarm Went OffnullMaterialsPeopleThe Histogram6 Overview Some Basic 6-Related ToolsPlot of Daily Arrival Time 9:157:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00Average ValueNumber of People Arriving at CRDTimenull6 Overview LCLUCLRange ChartSome Basic 6-Related ToolsMonitors Changes in Average or Variation Over TimenullDesign of Experiments6 Overview SCREENINGOPTIMIZATIONCHARACTERIZATION 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 Useful When Studying Highly Complicated Effects & RelationshipsDOE is More Effective Than Testing One Factor at a Timenull6 Overview Using the “One Factor at a Time” Approach Time of Departure3 2 17:157:307:458:008:15RouteCombination SelectedThe ResultUse Route 2 and Leave at 7:15 to Reach Goalnull6 Overview Using “Design of Experiments” (DOE)Time of DepartureDOE (i) Better Accounts for Interactive Variables Missed by “One Factor at a Time”, and (ii) Efficiently Searches for “Sweet Spot” in Parameter SpaceThe Variables Time of Departure from Home & Route Taken to WorkThe ResultA Better Combination Allowing 15 More Minutes of Sleep!!!Actual Commuting Time Averages (minutes)3 2 17:157:307:458:008:15Route17 20 23 21 1915 18 20 19 1612 15 21 20 18 Original Conclusion Best Combination “Sweet Spot”null A Practical Example (The “Cookbook”)6 Overview null6.....and Baking BreadUsing a 12 Step Process6 Overview nullWhat is Important to the Customer? Rise Texture Smell Freshness TasteY = Taste!!6 Overview Measurenull6 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 9 10 TargetDefectsWorstBestBut.....Is this the Right System?Measurenull6 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 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 2 F 5 9 1 G 8 9 2* Within One Taste UnitMeasurenull6 Overview This is a 3 Process!7 Defects (ratings below 7)24 Ratings (from our panel)=.292292,000 Defects per 1,ooo,ooo LoavesORAnalyzeHow 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 Processnull6 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!1,000,000 - 100,000 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,000 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,000 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 - 2 3 4 5 6 7 “BETTER BREAD” Baking Process Best Competitor Range for Improvement Defects Per MillionSigma ScaleAnalyzenull6 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, ControlsAnalyzenull6 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” CausesSourceConclusionNegligibleMajor CauseNegligibleMajor CauseNegligibleFocus on The “Vital Few”Improvenull6 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 CImprove Note: Time is a Factor Only if Temperature Changes Significantlynull6 Overview But.....Is Our Measurement System Correct?Improvenull6 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 ResultsVerify that our Instruments are AccurateControlnull6 Overview How Could We Approach This? Check A Number of Ovens Monitor Temperatures Over Time Focus on the Process Capability Look for Degree of VariationVariation OK But...Average is High (and the algorithm should be checked)Controlnull6 Overview What do we do Going Forward? Check Ovens Daily for Temperature Levels Audit Usage Frequency of Alternative Flour Supplier (e.g., Brand C) Periodically Reassemble the Panel to Test Taste Chart the ResultsAnd.....Plot the Data Over Time354 353 352 351 350 349 348 1 3 5 7 9 11 13 15 17 19 21 23 25Control