1、Day 1 AgendavWelcome and IntroductionsvCourse StructureMeeting Guidelines/Course Agenda/Report Out CriteriavGroup ExpectationsvIntroduction to Six Sigma ApplicationsvRed Bead ExperimentvIntroduction to Probability DistributionsvCommon Probability Distributions and Their UsesvCorrelation AnalysisDay
2、2 AgendavTeam Report Outs on Day 1 MaterialvCentral Limit TheoremvProcess CapabilityvMulti-Vari AnalysisvSample Size ConsiderationsDay 3 AgendavTeam Report Outs on Day 2 MaterialvConfidence IntervalsvControl ChartsvHypothesis TestingvANOVA(Analysis of Variation)vContingency TablesDay 4 AgendavTeam R
3、eport Outs on Practicum ApplicationvDesign of ExperimentsvWrap Up-Positives and DeltasClass GuidelinesvQ&A as we govBreaks HourlyvHomework ReadingsAs assigned in SyllabusvGradingClass Preparation30%Team Classroom Exercises30%Team Presentations40%v10 Minute Daily Presentation(Day 2 and 3)on Applicati
4、on of previous days workv20 minute final Practicum application (Last day)vCopy on Floppy as well as hard copyvPowerpoint preferredvRotate PresentersvQ&A from the classINTRODUCTION TO SIX SIGMA APPLICATIONSLearning ObjectivesvHave a broad understanding of statistical concepts and tools.vUnderstand ho
5、w statistical concepts can be used to improve business processes.vUnderstand the relationship between the curriculum and the four step six sigma problem solving process(Measure,Analyze,Improve and Control).What is Six Sigma?A PhilosophyA Quality LevelA Structured Problem-Solving ApproachA ProgramSCu
6、stomer Critical To Quality(CTQ)CriteriaSBreakthrough ImprovementsSFact-driven,Measurement-based,Statistically Analyzed PrioritizationSControlling the Input&Process Variations Yields a Predictable ProductS6s s=3.4 Defects per Million OpportunitiesSPhased Project:Measure,Analyze,Improve,ControlSDedica
7、ted,Trained BlackBeltsSPrioritized ProjectsSTeams-Process Participants&OwnersPOSITIONING SIX SIGMA THE FRUIT OF SIX SIGMALogic and IntuitionSeven Basic ToolsProcess Characterization and OptimizationProcess EntitlementDesign for ManufacturabilityUNLOCKING THE HIDDEN FACTORYVALUE STREAM TO THE CUSTOME
8、RPROCESSES WHICH PROVIDE PRODUCT VALUE IN THE CUSTOMERS EYESFEATURES OR CHARACTERISTICS THE CUSTOMER WOULD PAY FOR.WASTE DUE TO INCAPABLE PROCESSESWASTE SCATTERED THROUGHOUT THE VALUE STREAMEXCESS INVENTORYREWORKWAIT TIMEEXCESS HANDLINGEXCESS TRAVEL DISTANCESTEST AND INSPECTIONWaste is a significant
9、 cost driver and has a major impact on the bottom line.Common Six Sigma Project AreasvManufacturing Defect ReductionvCycle Time ReductionvCost ReductionvInventory ReductionvProduct Development and IntroductionvLabor ReductionvIncreased Utilization of ResourcesvProduct Sales ImprovementvCapacity Impr
10、ovementsvDelivery ImprovementsThe Focus of Six Sigma.Y=f(x)All critical characteristics(Y)are driven by factors(x)which are“upstream”from the results.Attempting to manage results(Y)only causes increased costs due to rework,test and inspectionUnderstanding and controlling the causative factors(x)is t
11、he real key to high quality at low cost.INSPECTION EXERCISEThe necessity of training farm hands for first class farms in the fatherly handling of farm livestock is foremost in the minds of farm owners.Since the forefathers of the farm owners trained the farm hands for first class farms in the father
12、ly handling of farm livestock,the farm owners feel they should carry on with the family tradition of training farm hands of first class farms in the fatherly handling of farm livestock because they believe it is the basis of good fundamental farm management.How many fs can you identify in 1 minute o
13、f inspection.INSPECTION EXERCISEThe necessity of*training f*arm hands f*or f*irst class f*arms in the f*atherly handling of*f*arm livestock is f*oremost in the minds of*f*arm owners.Since the f*oref*athers of*the f*arm owners trained the f*arm hands f*or f*irst class f*arms in the f*atherly handling
14、 of*f*arm livestock,the f*arm owners f*eel they should carry on with the f*amily tradition of*training f*arm hands of*f*irst class f*arms in the f*atherly handling of*f*arm livestock because they believe it is the basis of*good f*undamental f*arm management.How many fs can you identify in 1 minute o
15、f inspection.36 total are available.SIX SIGMA COMPARISONFocus on PreventionFocus on FirefightingLow cost/high throughputHigh cost/low throughputPoka Yoke Control StrategiesReliance on Test and InspectionStable/Predictable ProcessesProcesses based on Random ProbabilityProactiveReactiveLow Failure Rat
16、esHigh Failure RatesFocus on Long TermFocus on Short TermEfficientWastefulManage by Metrics and AnalysisManage by“Seat of the pants”Six Sigma Traditional“SIX SIGMA TAKES US FROM FIXING PRODUCTS SO THEY ARE EXCELLENT,TO FIXING PROCESSES SO THEY PRODUCE EXCELLENT PRODUCTS”Dr.George Sarney,President,Si
17、ebe Control SystemsIMPROVEMENT ROADMAPBreakthroughStrategyCharacterizationPhase 1:MeasurementPhase 2:AnalysisOptimizationPhase 3:ImprovementPhase 4:ControlDefine the problem and verify the primary and secondary measurement systems.Identify the few factors which are directly influencing the problem.D
18、etermine values for the few contributing factors which resolve the problem.Determine long term control measures which will ensure that the contributing factors remain controlled.ObjectiveMeasurements are critical.If we cant accurately measure something,we really dont know much about it.If we dont kn
19、ow much about it,we cant control it.If we cant control it,we are at the mercy of chance.WHY STATISTICS?THE ROLE OF STATISTICS IN SIX SIGMA.vWE DONT KNOW WHAT WE DONT KNOWIF WE DONT HAVE DATA,WE DONT KNOWIF WE DONT KNOW,WE CAN NOT ACTIF WE CAN NOT ACT,THE RISK IS HIGHIF WE DO KNOW AND ACT,THE RISK IS
20、 MANAGEDIF WE DO KNOW AND DO NOT ACT,WE DESERVE THE LOSS.DR.Mikel J.HarryvTO GET DATA WE MUST MEASUREvDATA MUST BE CONVERTED TO INFORMATIONvINFORMATION IS DERIVED FROM DATA THROUGH STATISTICSUSLT LSLWHY STATISTICS?THE ROLE OF STATISTICS IN SIX SIGMA.vIgnorance is not bliss,it is the food of failure
21、and the breeding ground for loss.DR.Mikel J.Harry Years ago a statistician might have claimed that statistics dealt with the processing of data.Todays statistician will be more likely to say that statistics is concerned with decision making in the face of uncertainty.BartlettUSLT LSLm Sales Receipts
22、m On Time Deliverym Process Capacitym Order Fulfillment Timem Reduction of Wastem Product Development Timem Process Yieldsm Scrap Reductionm Inventory Reductionm Floor Space UtilizationWHAT DOES IT MEAN?Random Chance or Certainty.Which would you choose.?Learning ObjectivesvHave a broad understanding
23、 of statistical concepts and tools.vUnderstand how statistical concepts can be used to improve business processes.vUnderstand the relationship between the curriculum and the four step six sigma problem solving process(Measure,Analyze,Improve and Control).RED BEAD EXPERIMENTLearning ObjectivesvHave a
24、n understanding of the difference between random variation and a statistically significant event.vUnderstand the difference between attempting to manage an outcome(Y)as opposed to managing upstream effects(xs).vUnderstand how the concept of statistical significance can be used to improve business pr
25、ocesses.WELCOME TO THE WHITE BEAD FACTORYHIRING NEEDSPRODUCTION SUPERVISOR4 PRODUCTION WORKERS2 INSPECTORS1 INSPECTION SUPERVISOR1 TALLY KEEPERSTANDING ORDERSvFollow the process exactly.vDo not improvise or vary from the documented process.vYour performance will be based solely on your ability to pr
26、oduce white beads.vNo questions will be allowed after the initial training period.vYour defect quota is no more than 5 off color beads allowed per paddle.WHITE BEAD MANUFACTURING PROCESS PROCEDURESvThe operator will take the bead paddle in the right hand.vInsert the bead paddle at a 45 degree angle
27、into the bead bowl.vAgitate the bead paddle gently in the bead bowl until all spaces are filled.vGently withdraw the bead paddle from the bowl at a 45 degree angle and allow the free beads to run off.vWithout touching the beads,show the paddle to inspector#1 and wait until the off color beads are ta
28、llied.vMove to inspector#2 and wait until the off color beads are tallied.vInspector#1 and#2 show their tallies to the inspection supervisor.If they agree,the inspection supervisor announces the count and the tally keeper will record the result.If they do not agree,the inspection supervisor will dir
29、ect the inspectors to recount the paddle.vWhen the count is complete,the operator will return all the beads to the bowl and hand the paddle to the next operator.INCENTIVE PROGRAMvLow bead counts will be rewarded with a bonus.vHigh bead counts will be punished with a reprimand.vYour performance will
30、be based solely on your ability to produce white beads.vYour defect quota is no more than 7 off color beads allowed per paddle.PLANT RESTRUCTUREvDefect counts remain too high for the plant to be profitable.vThe two best performing production workers will be retained and the two worst performing prod
31、uction workers will be laid off.vYour performance will be based solely on your ability to produce white beads.vYour defect quota is no more than 10 off color beads allowed per paddle.OBSERVATIONS.WHAT OBSERVATIONS DID YOU MAKE ABOUT THIS PROCESS.?The Focus of Six Sigma.Y=f(x)All critical characteris
32、tics(Y)are driven by factors(x)which are“downstream”from the results.Attempting to manage results(Y)only causes increased costs due to rework,test and inspectionUnderstanding and controlling the causative factors(x)is the real key to high quality at low cost.Learning ObjectivesvHave an understanding
33、 of the difference between random variation and a statistically significant event.vUnderstand the difference between attempting to manage an outcome(Y)as opposed to managing upstream effects(xs).vUnderstand how the concept of statistical significance can be used to improve business processes.INTRODU
34、CTION TO PROBABILITY DISTRIBUTIONSLearning ObjectivesvHave a broad understanding of what probability distributions are and why they are important.vUnderstand the role that probability distributions play in determining whether an event is a random occurrence or significantly different.vUnderstand the
35、 common measures used to characterize a population central tendency and dispersion.vUnderstand the concept of Shift&Drift.vUnderstand the concept of significance testing.Why do we Care?An understanding of Probability Distributions is necessary to:Understand the concept and use of statistical tools.U
36、nderstand the significance of random variation in everyday measures.Understand the impact of significance on the successful resolution of a project.IMPROVEMENT ROADMAPUses of Probability DistributionsBreakthroughStrategyCharacterizationPhase 1:MeasurementPhase 2:AnalysisOptimizationPhase 3:Improveme
37、ntPhase 4:ControlEstablish baseline data characteristics.Project UsesIdentify and isolate sources of variation.Use the concept of shift&drift to establish project expectations.Demonstrate before and after results are not random chance.Focus on understanding the conceptsVisualize the conceptDont get
38、lost in the math.KEYS TO SUCCESSMeasurements are critical.If we cant accurately measure something,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.Types of MeasuresvMeasures where the metric is composed of a classi
39、fication in one of two(or more)categories is called Attribute data.This data is usually presented as a“count”or“percent”.Good/BadYes/NoHit/Miss etc.vMeasures where the metric consists of a number which indicates a precise value is called Variable data.TimeMiles/HrCOIN TOSS EXAMPLEvTake a coin from y
40、our pocket and toss it 200 times.vKeep track of the number of times the coin falls as“heads”.vWhen complete,the instructor will ask you for your“head”count.COIN TOSS EXAMPLE1301201101009080701000050000Cumulative FrequencyResults from 10,000 people doing a coin toss 200 times.Cumulative Count13012011
41、01009080706005004003002001000Head CountFrequencyResults from 10,000 people doing a coin toss 200 times.Count Frequency130120110100908070100500Head CountCumulative PercentResults from 10,000 people doing a coin toss 200 times.Cumulative PercentCumulative FrequencyCumulative PercentCumulative count is
42、 simply the total frequency count accumulated as you move from left to right until we account for the total population of 10,000 people.Since we know how many people were in this population(ie 10,000),we can divide each of the cumulative counts by 10,000 to give us a curve with the cumulative percen
43、t of population.COIN TOSS PROBABILITY EXAMPLE130120110100908070100500Cumulative PercentResults from 10,000 people doing a coin toss 200 timesCumulative PercentThis means that we can now predict the change that certain values can occur based on these percentages.Note here that 50%of the values are le
44、ss than our expected value of 100.This means that in a future experiment set up the same way,we would expect 50%of the values to be less than 100.COIN TOSS EXAMPLE1301201101009080706005004003002001000Head CountFrequencyResults from 10,000 people doing a coin toss 200 times.Count Frequency13012011010
45、0908070100500Head CountCumulative PercentResults from 10,000 people doing a coin toss 200 times.Cumulative PercentWe can now equate a probability to the occurrence of specific values or groups of values.For example,we can see that the occurrence of a“Head count”of less than 74 or greater than 124 ou
46、t of 200 tosses is so rare that a single occurrence was not registered out of 10,000 tries.On the other hand,we can see that the chance of getting a count near(or at)100 is much higher.With the data that we now have,we can actually predict each of these values.COIN TOSS PROBABILITY DISTRIBUTION-6-5-
47、4-3-2-10123456NUMBER OF HEADSPROCESS CENTERED ON EXPECTED VALUE s sSIGMA(s s)IS A MEASURE OF“SCATTER”FROM THE EXPECTED VALUE THAT CAN BE USED TO CALCULATE A PROBABILITY OF OCCURRENCESIGMA VALUE(Z)CUM%OF POPULATION586572798693100107114121128135142.003.1352.27515.8750.084.197.799.8699.9971301201101009
48、080706005004003002001000FrequencyIf we know where we are in the population we can equate that to a probability value.This is the purpose of the sigma value(normal data).%of population=probability of occurrencem Common Occurrence m Rare EventWHAT DOES IT MEAN?What are the chances that this“just happe
49、ned”If they are small,chances are that an external influence is at work that can be used to our benefit.Probability and Statistics“the odds of Colorado University winning the national title are 3 to 1”“Drew Bledsoes pass completion percentage for the last 6 games is.58%versus.78%for the first 5 game
50、s”“The Senator will win the election with 54%of the popular vote with a margin of+/-3%”Probability and Statistics influence our lives daily Statistics is the universal lanuage for science Statistics is the art of collecting,classifying,presenting,interpreting and analyzing numerical data,as well as
