奧特利夫精益生產(chǎn)之ppt課件

1、1APS Training ModuleQF 6 SigmaAutoliv14-April-2006QF-TM-004-C2.02AutolivQuality assurance Quality methods Six Sigma StandardsPull system Safety & Ergonomy Dont judge, dont blame !Flexibility & MotivationDiscipline to standards Continuous improvementProcess & ResultCustomersCompanyEmployeesSocietyQua

2、lity firstProfitabilityCompetitivenessSatisfaction Employee involvement Team work5SMuda eliminationTPMJust in time Leveling & CapacityLine conceptsContinuous flowTakt timeFrequent deliveriesSix Sigma3Why this trainingDuring this training you will learn:What 6 Sigma isA measurement of qualityA proble

3、m solving methodExamples of tools in 6 Sigma4What is 6 SigmaSigma is the Greek letter used to represent variation A sigma level is a measurement of qualityA 6 Sigma level process creates close to no failuresA sigma level is a measurement of how well the process meets customer specifications6 Sigma i

4、s a problem solving methodCustomer centeredSystematicData driven5A Measurement of Quality62s308,5373s 66,8074s 6,2105s 2336s 3.4A Measurement of QualityA 6 Sigma process has only about 3.4 ppm!Sigma LevelPPM7A Measurement of QualityOut of 365 Rounds per Year2s 6 missed putts per round3 1 missed putt

5、 per round4 1 missed putt every 9 rounds5 1 missed putt in 1.5 years6 1 missed putt in 45 years8A Problem Solving Method6 Sigma is a set of Customer-Centered, Systematic, Data Driven tools and methods to help improve processes and thereby solve and prevent problems.9APS ToolboxThe APS Toolbox is exp

6、anded with the 6 Sigma Tools to solve more complex problems with increased efficiencyNumber of CasesProblem Complexity6 Sigma5 WhyPDCAAPS Toolbox10Customer CenteredWho is the customer?What is critical to the quality (CTQ) for the customer?How are the customers expectations not being met? Actions bas

7、ed on data are the key to Customer Satisfaction 11DefineMeasureAnalyzeImproveControlDMAIC1- Form a team2- Describe the problem6- Evaluate results3- Implement containment actions4- Identify root cause7- Prevent recurrence8- Congratulate the team5- Choose & implement corrective actions8-DSystematic6 S

8、igma follows a roadmapTo link tools more powerfully togetherTo guide the team through the problemPlanDoCheckActPDCA12The DMAIC MethodDMAICDefine project or problemMeasure current situationProcess behavior chartsCapability analysisPareto chartsCreate Cause & Effect MatrixSelect corrective actionsDocu

9、ment ResultsDevelop Project PlanSelect teamBusiness caseSMART objectiveProject scopeKey metricOperational definitionConduct Measurement System Analysis (MSA)Create VMEAImplement Corrective actionsImplement control methodControl planStandardsTraining matrixSWIAuditSPCCreate Process Map or Fishbone Di

10、agramRoot cause analysis processVMEA ActionsTesting potential root causesOFAT TestsDOE TestsVerify corrective actionsCapability analysisProcess behavior chartsOFAT TestsDOE ConfirmatoryDocument Lessons LearnedDetermine customers CTQsOptimize ProcessDOE TestsResponse OptimizerResponse surface models

11、(RSM)Confirm root causeReproduce failureOFAT TestsDOE Tests13Systematic6 Sigma is about reducing variationUnderstand the systemY=f(x)Determine to dominate source of variationControl the dominate source14Inputs causing the problem at handAll possible input variablesProcess MapC&E MatrixThe DMAIC Meth

12、odProcess MapCollects all known input and output variablesCause and Effect MatrixPrioritizes the variables most likely to have a major impactVMEAStudies how selected variables can cause the process to failStatistical tools are then used to quantify the relationship between these selected input and o

13、utput variablesVMEA15Data DrivenKey Concepts“Variable always trumps categoricalVariable data is more powerful than categorical because it contains more information“Data is innocent until proven guiltyVariation in a process is considered to be random until evidence shows otherwise“A graph is worth a

14、thousand dataThe correct graph or statistical tool can objectively make conclusions much better than raw or tabulated data16Data DrivenAttempting to control variation without understanding and quantifying it, simply adds one more source to the problem17Data DrivenBased on the data, what is this proc

15、ess doing?Getting betterGetting worseStaying the sameWhat will the % scrap be in three months?18Data DrivenBased on the chart, what is this process doing?Getting betterGetting worseStaying the sameWhat will the % scrap be in three months?19Data DrivenBased on the chart, what is this process doing?Ge

16、tting betterGetting worseStaying the sameWhat will the % scrap be in three months?20Measurement System AnalysisGage R&RObjectiveEvaluates whether a measuring system for variable data is adequateReportsRepeatabilityAmount of variation due to gage and methodReproducibilityAmount of variation due to ch

17、anging operators% Study VariationPercent of observed variation that is due to the measurement system% ToleranceCompares the measurement system to the tolerance StudyVar %StudyVar %ToleranceSource StdDev (SD) (5.15*SD) (%SV) (SV/Toler)Total Gage R&R 0.0089286 0.045982 19.37 9.20 Repeatability 0.00828

18、86 0.042686 17.98 8.54 Reproducibility 0.0033197 0.017096 7.20 3.42 Appraiser 0.0033197 0.017096 7.20 3.42Part-To-Part 0.0452258 0.232913 98.11 46.58Total Variation 0.0460987 0.237408 100.00 47.48Number of Distinct Categories = 721Measurement System AnalysisKappa TestObjectiveEvaluates whether a mea

19、suring system for categorical data is adequateReportsKappa Score.7 and .9 is excellentPercent Agreement and Confidence IntervalsFleiss Kappa StatisticsResponse Kappa SEKappa Z P(vs0)Good 0.769656 0.0632456 12.1693 0.0000Short 0.769656 0.0632456 12.1693 0.0000Fleiss Kappa StatisticsResponse Kappa SEK

20、appa Z P(vs0)Good 0.253472 0.0632456 4.00775 0.0000Short 0.253472 0.0632456 4.00775 0.0000 Kappa0.7696560.769656 Kappa0.2534720.253472BeforeAfter22Data Driven Example 1Youre the plant managerYou are reviewing an areas scrap rateWhat should you do?MonthScrap RateGive area a plaque for an all-time low

21、 in scrap!Wished you had the plaque back.Thats better!Five months of steadily increasing scrap!23Data Driven Example 1Process behavior chartsIdentify special cause variation from common cause variationCommon CauseThe inherent random variation caused by many inputsSpecial CauseThe non-random changes

22、in a process usually caused by one inputAll changes in scrap rate were due to common cause variation!24Data Driven Example 2Youre the production managerA machine supplier offers you a new machine to reduce cycle-timeThe new machine is $50,000A reduction of 2 seconds in cycle-time could save $100,000

23、 over a yearThe following data is given by the machine supplierMachine Cycle TimesOldNew8.79.419.913.96.35.921.216.812.311.8 13.68 11.56AveragesDo you purchase the new machine?25Data Driven Example 2T TestDistinguishes if differences between samples are significant or simply by random chanceInterval

24、 PlotDisplays the confidence intervals around each sample mean and visually represents the t test evaluationOther Statistical TestsANOVARegressionChi-SquaredProportionTest of Equal VarianceT Test Results: P-Value = 0.284Conclusion: The difference between machines is by random chance.26Data Driven Ex

25、ample 3A team has implemented improvements that they believe will reduce the variability by at least 20%Management wants less than 5% chance of missing the potential improvementManagement also wants less than 5% chance of implementing the changes if the improvement is not realHow many samples should

26、 be tested before and after the change?27Data Driven Example 3Sample size evaluationDetermines the samples needed to see changes based on:RiskThe probability of making a wrong decisionDifferenceThe magnitude of the change to be tested compare to the normal variation Samples for 20% reduction at 95%

27、confidence220 samples before and after changes!28Data Driven Example 4The web sensitivity for a seatbelt model has too much variationThe team needs to know where to set and how to control these component dimensions:Inertial mass weightBearing heightSpring rateLever length How does the team determine

28、 the nominal dimensions and tolerances?29Data Driven Example 4Which factors have an affect?What is the optimal condition?RememberReducing component tolerances costs moneyData are fictional values30Data Driven Example 4DOE (Design of Experiments)A systematic set of tests that efficiently determine th

29、e effect of each input on the outputsInteractionHow one input changes the effect of another input on the output (not seen in single factor tests)ResultsThese results can be used to determine the settings of the inputs to optimize the outputData are fictional values31ControlControl is the last step o

30、f DMAIC that ensures the improvements are continuedTools in ControlSPCPoke YokeLessons LearnedThe idea is to continually improveDefineMeasureAnalyzeImproveControl32DesignEngineering &DevelopmentSupportServicesProcessing &ManufacturingShipping &ReceivingProductUse &CustomerExperienceWhere Does 6 Sigma Apply?33Pro