SIX SIGMA GREEN BELT TRAINING

SIX SIGMA GREEN BELT TRAINING

Shailja ChaudhryNational Institute of Technology, Kurukshetra

SIX SIGMA OVERVIEW AND EVOLUTION

What is Six SigmaA customer focused business improvement processDriven by teamwork, consensus & logical reasoningStructured methodology DMAICFocuses on making the process robust & reduce variationsApplies to any Process

What is Six SigmaSix sigma is a highly disciplined and quantitative strategic business improvement approach that seeks to increase both customer satisfaction and an organizations financial health.Six Sigma helps a company focus on developing and delivering near-perfect products (durable goods or services), to improve customer satisfaction and the bottom line.

What Six Sigma is NOTSix Sigma is NOTA Quality ProgramCure for World HungerOnly for Technical PeopleJust about StatisticsUsed when solution is knownUsed for Firefighting

Six-Sigma A note from Originator of Six SigmaSix Sigma is not an improvement program. It is instead a business philosophy that employs a step by step approach to reducing variation, increasing quality, customer satisfaction, and in time, market share

Overview of Six Sigma

SIX SIGMA AS A PHILOSOPHYSIX SIGMA AS A PROCESSSIX SIGMA AS A STATISTICAL TOOL

What is Six Sigma?Sigma is a measurement that indicates how a process is performingSix sigma stands for Six Standard Deviations (Sigma is the Greek letter used to represent standard deviation in statistics) from mean. Six Sigma methodology provides the techniques and tools to improve the capability and reduce the defects in any process.Six Sigma is structured application of tools and techniques applied on project basis to achieve sustained strategic results.

What is Six SigmaA Vision of a Six Sigma Company

Organizational Issue

Problem ResolutionBehaviorDecision MakingProcess AdjustmentSupplier RelationshipPlanningDesignEmployee TrainingChain-of-commandDirectionManpower

Traditional approach

Fixing (symptoms)ReactiveExperience-basedTweakingCost (piece price)Short-termPerformanceIf Time PermitsHierarchySeat-of-pantsCost

Six Sigma Approach

Preventing (causes)Data-basedControllingCapabilityLong-termProducibilityMandatedEmpowered TeamsBenchmarking and metricsAsset

Traditional approach

Fixing (symptoms)ReactiveExperience-basedTweakingCost (piece price)Short-termPerformanceIf Time PermitsHierarchySeat-of-pantsCost

Six Sigma Approach

Preventing (causes)Data-basedControllingCapabilityLong-termProducibilityMandatedEmpowered TeamsBenchmarking and metricsAsset

Character of 6sTraditional Quality / Six Sigma Quality Method

ISSUETRADITIONAL APPROACHSIX SIGMA APPROACHIndexData

Target

Range

Method

Action% (Defect Rate)Discrete Data

Satisfaction for Mfg. ProcessSpec Outliner

Experience + Job

Bottom UpDiscrete + Continuous DataCustomer SatisfactionVariation improvementExperience + Job + Statistical AbilityTop Down

Aligning The Focus

Six Sigma Journey Started(Traditional)1000 Unassigned ProjectsSix Sigma ProjectStrategic DirectionTactical DirectionIndividual Work Group(Lets do it)(Future)

What is Six Sigma Definition

2Sigma

3Sigma

4Sigma

5Sigma

6Sigma

23456Sigma LevelDefects/ Million Opportunities% Yield308,53766,8076,2102333.469.193.399.499.9899.9997

Six Sigma : The Statistical Way

LSLUSL

LSLUSL

LSLUSLProcess of TargetExcessive VariationReduce Variation % Center ProcessCustomers feel the variation more than the mean

Center ProcessReduce SpreadTarget Target Target

Six Sigma Practical Meaning99% Good (3.8 Sigma)99.99966% Good (6 Sigma)20,000 lost articles of mail per hourUnsafe drinking water for almost 15 minutes each day5,000 incorrect surgical operations per weekTwo short or long landings at most major airports each day200,00 wrong drug prescriptions each yearNo electricity for almost seven hours each monthSeven articles lost per hourOne unsafe minute every seven months

1.7 incorrect operations per weekOne short or long landing every five years

68 wrong prescriptions per yearOne hour without electricity every 34 years

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Philosophy of Six SigmaKnow Whats Important to the Customer (CTQ)

Reduce Defects (DPMO)

Center Around Target (Mean)

Reduce Variation ()

Harvesting the fruit of Six Sigma

HISTORY OF SIX SIGMA

History of Six SigmaQuality tools like SPC, Cost of Quality, Control Charts, Process capability etc. are known to industry for long time, much before birth of Six Sigma.Quality Tools and Quality system implementation was not in conjunction with overall business Goals.Traditional Quality Tools have limitations to orient the efforts on Quality Improvements to the Organizational direction basically due to approach.Motorola was the first Company to initiate the Six Sigma breakthrough Strategy.

A Little Bit Of HistorySix Sigma was developed by Bill Smith, QM at MotorolaIts implementation began at Motorola n 1987It allowed Motorola to win the first Baldrige Award in 1988Motorola recorded more than $16 Billion savings as a result of Six SigmaSeveral of the major companies in the world have adopted Six Sigma since then.Texas Instruments, Asea Brown Boveri, AlliedSignal, General Electric, Bombardier, Nokia Mobile Phones, Lockheed Martin, Sony, Polaroid, Dupont, American Express, Ford Motor,.. The Six Sigma Breakthrough Strategy has become a Competitive Tool

Motorola Case StudyIn early 1980 Motorola was facing a serious competitive challenge from Japanese Companies.Motorola was losing the market share and customer confidence.Motorola had not done any major changes to their products.The competitors from Japan were offering much better product at much lower price with no field failures.

Motorola Case Study.ContinueWhen Motorola studies the competitors products, it was revealed that the variation in key product characteristics is very low.The competitors products were available at lower price.The competitors products has very low warranty failure rate.Motorola was not able to match the competitors price mainly due to high cost of Poor quality largely due to high reject rate, high rework / repair rate, high inspection cost, high warranty failure rate etc.THE TECHNICAL TEAM CONCLUDED THAT THE COPETITORS ARE OFFERING BETTER PRODUCT AT LOWER COST.

Motorola Case StudyMotorola requested to the competitors from Japan to permit the Team from Motorola to visit them fro Study.Motorola sent the team of managers to Japan to study the Magic of Japanese companies.What the team revealed?

Motorola Case StudyWhat Motorola learning was as follows:Motorola was focusing too much on product Quality i.e. Inspection, rework, repair etc.The internal defect rate was very high inside Motorola.The reliability was slow since some of the defects were passing on to the customer as inspection lapses.A dissatisfied customer was shouting loudly and was taking away min 10 potential customers.As an effect of this, customers were lost to the competitors.

Motorola Case StudyWhat was wrong?Japanese were concentrating onCustomersProcesses People Variation in product and process parameters was known and controlledAll people were well trained and highly motivatedAll activities and processes were highly standardized i.e. no person dependenceDefect free lines and robust processesVery less inspectorsYet, very low defect rate, internal rejection and customer complaintsVERY HIGH LEVEL OF CUSTOMER SATISFACTION

Motorola Case StudyWHAT WAS THE SECRET?

THE SECRET WAS CONTROL OVER VARIATION

Success factor:Proactive Vs. Reactive Quality

The Impact Of Added Inspection

3.4 ppm100,000 ppm

6 ppmIf the likelihood of detecting the defect is 70% and we have 10 consecutive inspectors with this level of capability, we would expect about 6 escaping defects out of every 1,000,000 products producedYou can save yourself by producing quality not by inspection

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Motorola Case StudyIn order to address these issues, Motorola devised the Six Sigma methodology.Dr. Mikel Harry and Mr. Bill Smith were pioneers in Developing and implementing the Six Sigma methodology at Motorola.With implementation of Six Sigma, Motorola could achieve:4 level in one and half year time5 level in following year6 level in following year

Six Sigma Progress19851987199219952002Johnson & Johnson, Ford, Nissan, HoneywellGeneral ElectricAllied SignalMotorola Dr Mikel J Harry wrote a Paper relating early failures to quality

What can it do?Motorola:5-Fold growth in SalesProfits climbing by 20% paCumulative savings of $14 billion over 11 yearsGeneral Electric$2 billion savings in just 3 yearsThe no. 1company in the USABechtel Corporation:$200 million savings with investment of $30 millionIt is high time, that Indian Companies also start implementing Six Sigma for making breakthrough improvements and to remain globally competitive.

Quality and Value

Attempting to Define QualityExperts definitions of quality fall into two categories:Level one quality is a simple matter of producing products or delivering services whose measurable characteristics satisfy a fixed set of specifications that are usually numerically defined.Independent of any of their measurable characteristics, level two quantity products and services are simply those that satisfy customer expectations for their use or consumption.

In short, level one quality means get it in the specs, and level two means satisfy the customer.

Quality GapQuality GapUnderstanding of NeedsCustomer Perception of DeliveryCustomer ExpectationsDesign of ProductsCapability to Deliver DesignActual DeliveryDesign GapPerception GapOperations GapProcess GapUnderstanding the Gap

Nine Dimensions of QUALITYAccording to modern management concepts, quality has nine dimensions:

1) Performance: main characteristics of the product/service2) Aesthetics: appearance, feel, smell, taste3) Special features: extra characteristics

Nine Dimensions of QUALITY4) Conformance: how well the product/service conforms to customers expectations5) Safety: risk of injury6) Reliability: consistency of performance

Nine Dimensions of QUALITY7) Durability: useful life of the product/service8) Perceived Quality: indirect evaluation of quality (e.g. reputation)9) Service after Sale: handling of Customer complaints and checking customer satisfaction.

Evolution of QualityHistorically ContemporaryReactive Quality Quality Checks (QC) -Taking the defectives out of what is producedProactive QualityCreate process that will produce less or no defects

Old Concept Of QualityPast concepts of quality focused on conformance to standards. This definition assumed that as long as the company produced quality products and services, their performance standard was correct regardless of how those standards were met. Moreover, setting of standards and measurement of performance was mainly confined to the production areas and the commercial and other service functions were managed through command control.

Value EnrichmentThe term Value Enrichment for the company means that they must strive to produce highest quality products at the lowest possible costs to be competitive in the global markets.For customers, the term Value Enrichment means that they have the right to purchase high quality products/services at the lowest cost.

Concept Of ValuePrice+InconvenienceReal+Perceived

DefinitionsVALUE:

THOSE ACTIVITIES THAT CONVERT MATERIALS OR IDEAS INTO GOODS OR SERVICES THAT GENERATE CASH

Definition

ANYTHING THAT IS NOT VALUE IS WASTE

Six Sigma and Cost Of QualitySix Sigma has a very significant impact on the cost of quality. As the Sigma level moves up, the cost of quality comes down and vice versa. Traditionally recorded quality cost generally account for only 4 to 5 percent of sales which mainly comprise of scrap, re-work and warranty.

There are additional costs of quality which are hidden and do not appear in the account books of the company, as they are intangible and difficult to measure.

Visible And Hidden Costs

Visible CostsHiddenCostsScrapReworkWarranty CostsConversion efficiency of materialsInadequate resources utilizationExcessive use of materialsCost of re-design and reinspectionCost of resolving customer problemsLost customers / GoodwillHigh Inventory

Cost OF Quality At Various Levels Of Sigma63.440%

Sigma Defect Rate (PPM)Cost Of QualityCompetitive Level

World ClassIndustry AverageNon Competitive

What is The Cost Of Quality?Cost of Quality: the cost of ensuring that the job is done right + the cost of not doing the job right.

Cost of Conformance + Cost of Non-Conformance (Prevention and Appraisal) (Internal/External Defects)

Cost Of QualityPrevention Costs

Quality PlanningProcess Evaluation / ImprovementQuality Improvement MeetingsQuality Training

External Failure Costs

Complaint HandlingRework / CorrectionRe-Inspection

Internal Failure Costs

Rework / CorrectionRe-InspectionInternal RejectLoss of Business

Appraisal Costs

Source InspectionIn / End-Process InspectionCalibrationSpecialist Cost

Direct Costs

Prevention Costs

Quality PlanningProcess Evaluation / ImprovementQuality Improvement MeetingsQuality Training

External Failure Costs

Complaint HandlingRework / CorrectionRe-Inspection

Internal Failure Costs

Rework / CorrectionRe-InspectionInternal RejectLoss of Business

Appraisal Costs

Source InspectionIn / End-Process InspectionCalibrationSpecialist Cost

PHASES OF SIX SIGMA

Fundamental StepsThere are 5 fundamental Steps involved in applying the breakthrough strategy for achieving Six Sigma. These steps are :-DefineMeasureAnalyzeImproveControl

Define PhaseThis phase defines the project. It identifies critical customer requirements and links them to business needs. It also defines a project charter and the business processes to be undertaken for Six Sigma.

Define Define DCMAI

Define ActivitiesIdentify Project, Champion and Project OwnerDetermine Customer Requirements and CTQsDefine Problem, Objective, Goals and BenefitsDefine Stakeholders/Resource AnalysisMap the ProcessDevelop Project PlanDefine Quality ToolsProject Charter and PlanEffort/Impact AnalysisProcess MappingTree DiagramVOCKano ModelPareto Analysis

Measurement PhaseThis phase involves selecting product characteristic, mapping respective process, making necessary measurements and recording the results of the process. This is essentially a data collection phase.

Measure Operational DefinitionMeasure MCDAI

Measure ActivitiesDetermine operational DefinitionsEstablish Performance StandardsDevelop Data Collection and Sampling PlanValidate the MeasurementsMeasurement System AnalysisDetermine Process Capability and BaselineMeasure Quality ToolsMeasurement Systems AnalysisCheck SheetProcess CapabilityProcess FMEA

Analysis PhaseIn this phase an action plan is created to close the gap between how things currently work and how the organization would like them to work in order to meet the goals for a particular product or service. This phase also requires organizations to estimate their short term and long term capabilities.

Analyze Analyze ACMDI

Analyze ActivitiesBenchmark the Process or ProductAnalysis of the Process MapBrainstorm for likely causesEstablish Causal Relationships Using DataDetermine Root Cause(s) Using Data Analyze Quality ToolsCause and Effect or Event DiagramGraphical AnalysisStatistical Analysis of DataHypothesis TestingCorrelation RegressionDOE

Improvement PhaseThis phase involves improving processes/product performance characteristics for achieving desired results and goals. This phase involves application of scientific tools and techniques for making tangible improvements in profitability and customer satisfaction.

Improve Improve ICMDA

Improve ActivitiesDevelop Solution AlternativesAssess Risks and Benefits of Solution AlternativesImplement error-proofing solutionsValidate Solution using a PilotImplement SolutionDetermine Solution Effectiveness using Data Improve Quality ToolsBrainstormingFMEARisk AssessmentPoka Yoke

Control PhaseThis Phase requires the process conditions to be properly documented and monitored through statistical process control methods. After a setting in period, the process capability should be reassessed. Depending upon the results of such a follow-up analysis, it may be sometimes necessary to revisit one or more of the preceding phases.

Control Develop Standards Control CIMDA

Control ActivitiesDetermine Needed Controls (measurement, design, etc.)Implement and Validate ControlsDevelop Transfer PlanRealize Benefits of Implementing SolutionInstitutional ChangesClose Project and Communicate ResultsControl Quality ToolsStatistical Process ControlProcess Map and FMEAControl Plans5SControl Charts

Six Sigma Projects

Why Project Selection is Important?High leverage projects lead to largest SavingsLarge returns are expected by management to justify the investment in time and effortDeveloping a Six Sigma culture depend upon successful projects having significant business impact

How To Focus ProjectsProcess Cost Savings FocusProject Quality focusProduct focus (Six Sigma Design)Problem Focus (Least Desirable Use)

Project SelectionAlign with company objectives and business plan (Annual Operating Plan)Voice of Customer/CTs InputsQuality (CTQ)/Cost (CTC)/ Delivery (CTD)PPM / COPQ / RTY / Cycle TimeConsistent with principles of Six SigmaEliminate process defectsConcentrate on Common issues/opportunities not fir-fightingLarge enough to justify the investment

Project DesirabilityEffort Required:- includes time required of team members and expenditure of money.Probability of Success:- An assessment that takes into account various risk factors:Time uncertainty of the completion dateEffort uncertainty of the investment requiredImplementation uncertainty of roadblocks

Project Desirability Matrix

Increased Desirability

HiMedLowHiMedLowHiMedLowIMPACTEFFORT

Additional Project ConsiderationsProjects must serve as a learning experience for Green Belts to use the six Sigma toolsProjects scope should not be too large or take too long to implementProjects scope should be manageable and take at least 255 of the potential Green Belts time.Pareto Chart may be used to Scope the ProjectDesirable to have a measurable variable for the primary project output/metrics

Additional Project ConsiderationsProjects must serve as a learning experience for Green Belts to use the six Sigma toolsProjects scope should not be too large or take too long to implementProjects scope should be manageable and take at least 255 of the potential Green Belts time.Pareto Chart may be used to Scope the ProjectDesirable to have a measurable variable for the primary project output/metrics

DO NOT try to Solve World Hunger

Strategy At Various LevelsAlmost every Organization can be divided into 3 basic levels:-Business levelOperations levelProcess level.It is extremely important that Six Sigma is understood and integrated at every level.

Strategies At Various LevelsExecutives at the business level can use Six Sigma for improving market share, increasing profitability and organizations long term viability.Managers at operations level can use Six Sigma to improve yield and reduce the labor and material cost.At the process level engineers can use Six Sigma to reduce defects and variation and improve process capability leading to better customer satisfaction.

Factors To Control in Improvement ProjectResources Team availabilityThe right toolsScheduleBe realisticBe aggressiveGet buy-inScope of WorkWatch for scope creepStay focusedAnticipate and mitigate risk

Control any two areas, the third floats in response

Meetings Make Them EffectiveDefined goal for meetingNotice and agendaDecision makers prepare and participateAction ItemsRecordsBalance SheetFocused on process, not topicWhat helped us get to our goalWhat could have been betterTake appropriate action

Skills NeededPeopleLeadership behaviorsCommunicationProcessTime ManagementSchedule CoordinationProblem SolvingRisk analysis and mitigationTactical PlanningTechnical Six Sigma / Lean ToolsBusiness Knowledge

Voice of Customer CTP and CTQ

Establishing Customer FocusCustomer Anyone internal or external to the organization who comes in contact with the product or output of workQuality performance to the standard expected by the Customer

Variation is the Enemy in Achieving Customer SatisfactionVariation

UncertaintyUnknownDisbeliefRiskDefect Rate

What is Variation

Variation is any deviation from the expected outcome.

Something more on VariationAny process has variationThere are two kinds of variationCommon cause variationSpecial cause variationVariation is measured in terms of sigma or standard deviation.

Variation and Standard DeviationIf a good deal of variation exists in a process activity, that activity will have a very large standard deviation.As a result, the distribution will be very wide and flat.

Less VariationMore Variation

Types of VariationSpecial Cause: something different happening at a certain time or place

Common Cause: always present to some degree in the process

We tamper with the system if we treat all variations as if it were special cause

Dealing with VariationEliminate special cause variation by recognizing it and dealing with it outside of the processReduce common cause variation by improving the process

Whom would you Prefer?

Operator - 1Operator - 2

Critical To Quality (CTQ) are the key measurable characteristics of a product or process whose performance standards or specification limits must be met in order to satisfy the customer.

They align improvement or design efforts with customer requirements.

Critical To Quality (CTQ)To put it in laymans terms, CTQs are what the customer experts of a product......the spoken needs of the customer.The customer may often express this in plain English, but it is up to us to convert them to measurable terms using tools such as QFD, DFMEA, etc.

Critical To Quality (CTQ)List customer needs.Identify the major drivers for these needs (major means those which will ensure that the need is addressed).Break each driver into greater detail.Stop the breakdown of each level when you have reached sufficient information that enables you to measure whether you meet the customer need or not.

Example CTQ Tree

Ease of OperationEase of MaintenanceEase of Operation and MaintenanceOperator Training Time (hrs.)Setup Time (minutes)Operation Accuracy (errors/1000 ops)Mean Time to Restore (MTTR)# Special Tools RequiredMaintenance Training Time (hrs.)Need CTQsDriversSpecificGeneral Hard to MeasureEasy to Measure

PROJECT CHARTER

Importance of Project CharterA project charter is a written document and works as an agreement between management and the team about what is expected.

The charter:Clarifies what is expected of the team.Keeps the team focused.Keeps the team aligned with organizational priorities.Transfers the project from the champion(s) to the project team.

Team CharterProblem StatementCurrently we carry out reblows to the extent of about 11-15% resulting in lower converter life, lower productivity of converter and increased Ferro-alloy and oxygen consumption.ScopeAll batches and all converters in SMS 1.Project Goal and MeasuresReblows should be less than 7.5% and 9%.Expected Business ResultsWe hope to save Rs. Xxxxx lakhs per year due to this reduction in reblows.

Team CharterTeam MembersSupervisor, two operators, technical services, quality controlSupport RequiredAllow for weekly team meetingsTeam budget for quick winsScheduleMeasure (7wks), Analyze (4wks), Improve (6wks), Check (2wks), Control (1wk), Standardise/Close (1wk)

Usual elements of a Project CharterProject Description Business CaseScope Process/ProductGoals and Measures (Key Indicators)Expected Business ResultsTeam MembersSupport RequiredExpected Customer BenefitsSchedule

MEASURE OVERVIEW

Measurement Objective

The Measure phase aims to set a baseline in terms of process performance through the development of clear and meaningful measurement systems

The Measurement ProcessTOOLS AND TECHNIQUS OF MEASUREDevelop Process MeasuresCollect Process DataCheck Data QualityUnderstand Process BehaviorBaseline Process Capability and PotentialHow do you measure the problem?When and where does the data come from?How does the process currently behave?What is the current performance of the process with respect to the customerDoes the data represent what you think it doesStatistics Operational DefinitionsData WorldsProcess CapabilityCp, CpkDPMO

Distributions First pass yieldShort/long term variationMSAGage R&RData Collection MethodsData Collection PlansSampling

Statistical and Data WorldIf the data isAttribute Count Continuous

Relevant statistical model is Binomial DistributionDefects per Unit (DPU)Always Poisson if process is in controlPoisson DistributionWhen does the statistical model applyCommon statistics areAlways Binomial if process is in controlPercentage (Proportion)Average (mean), Standard Deviation (sigma)Not always validity of normality needs to be checkedNormal Distribution

Basic Statistics

Statistics The science of:Collecting,DescribingAnalyzingInterpreting data...

And Making Decisions

What are Statistics?Descriptive StatisticsSummarize and describe a set of dataMean, median, range, standard deviation, variance, ....Analytical Statistical (or Statistics)Techniques that help us make decisions in the face of uncertaintyUse concepts of descriptive statistics as a baseHypothesis testing, means comparisons, variance comparisons, proportions comparisons, ...

Sample Versus PopulationUsing a small amount of data (Sample)... to make assumptions (inferences)... on a large amount of data (population).Population: the total collection of observations or measurements that are if interest.Sample: A subset of observations and measurements taken form the population.Why do we use samples?Time Cost Destructive testing (need product left to sell !!)Other?

Measures of Central TendencyWhat is the Median value of Distribution?Median What value represents the distribution?ModeWhat value represents the entire distribution?Mean (x ) What is the best measures of central tendency?

Data DistributionsMean: Arithmetic average of a set of valuesReflects the influence of all valuesStrongly influenced of all valuesMedian: Reflects the 50% rank the center number after a set of numbers has been sorted from low to high.Does not include all values in calculationIs robust to extreme scoresMode: The value or item occurring most frequently in a series of observations or statistical data.

Variable Data Location - MeanOur average monthly shipment is 253 unitsPopulationSample

Variable Data Location - MedianMonth # of UnitsJan-1999233Feb-1999281Mar-1999266Apr-1999237May-1999260Jun-1999250Jul-1999237Aug-1999275Sep-1999218Oct-1999279Nov-1999227Dec-1999246Jan-2000258Feb-2000272Mar-2000229Apr-2000240May-2000287Jun-2000260Jul-2000251Aug-2000288Sep-2000256Oct-2000219Nov-2000260Dec-2000249

Month # of UnitsSep-1999218Oct-2000219Nov-1999227Mar-2000229Jan-1999233Jul-1999237Apr-1999237Apr-2000240Dec-1999246Dec-2000249Jun-1999250Jul-2000251Sep-2000256Jan-2000258May-1999260Jun-2000260Nov-2000260Mar-1999266Feb-2000272Aug-1999275Oct-1999279Feb-1999281May-2000287Aug-2000288

Variable Data Location - MedianMonth # of UnitsJan-1999233Feb-1999281Mar-1999266Apr-1999237May-1999260Jun-1999250Jul-1999237Aug-1999275Sep-1999218Oct-1999279Nov-1999227Dec-1999246Jan-2000258Feb-2000272Mar-2000229Apr-2000240May-2000287Jun-2000260Jul-2000251Aug-2000288Sep-2000256Oct-2000219Nov-2000260Dec-2000249

Month # of UnitsSep-1999218Oct-2000219Nov-1999227Mar-2000229Jan-1999233Jul-1999237Apr-1999237Apr-2000240Dec-1999246Dec-2000249Jun-1999250Jul-2000251Sep-2000256Jan-2000258May-1999260Jun-2000260Nov-2000260Mar-1999266Feb-2000272Aug-1999275Oct-1999279Feb-1999281May-2000287Aug-2000288

260 is the mode

Variable Data Location - ModeNotes on meanA measure of central tendencyLimitations:Reflects the influence of all valuesStrongly influenced by extreme valuesMedian (the centre number after sorting high to low) is robust to extreme values.

Variable Data Description Range, Standard DeviationMonth # of UnitsJan-2006233Feb-2006281Mar-2006266Apr-2006237May-2006260Jun-2006250Jul-2006237Aug-2006275Sep-2006218Oct-2006279Nov-2006227Dec-2006246Jan-2007258Feb-2007272Mar-2007229Apr-2007240May-2007287Jun-2007260Jul-2007251Aug-2007288Sep-2007256Oct-2007219Nov-2007260Dec-2007249

Lets use this same data to calculate the statistics for dispersionThese statistics are Range and Standard Deviation

Example commuting timeCommute time (mins)19.522.420.718.818.220.019.619.821.019.820.721.922.022.619.422.818.117.521.319.118.419.821.018.519.219.219.419.324.821.221.218.318.217.419.921.018.916.417.619.519.223.920.621.918.719.520.117.122.119.219.620.320.820.722.419.921.120.416.719.118.322.427.117.618.822.519.921.820.417.721.317.818.715.818.921.720.119.618.421.718.718.820.518.620.922.015.819.420.218.723.621.019.920.118.321.919.721.119.922.9

Collect over a hundred occurrences.

Tabulate in chronological order.

Does the data show variation?

Can you make out anything with this arrangement of data?

Let us try and make some sense of this data

Measure of variation Standard Deviation and Range

152018222425

19.5019.7520.0020.2520.50

..Summary for Commute timeAnderson Darling Normality TestA-Squared P-Value0.420.312What are the relative merits and demerits of standard deviation over range?MeanSt.DevVarianceSkewnessKurtosisNMinimum1st QuartileMedian3rd QuartileMaximum95% Confidence Interval for Mean95% Confidence Interval for Mean95% Confidence Interval for Mean19.63220.0061.8843.5500.544701.3025610015.75418.71419.81921.18627.05420.38019.44820.2631.6542.189MeanMedian

One measure of variation (std. dev)Another Measure of variation (Range)Outlier *

Variable Data Dispersion Standard Deviations or standard deviationWhat does it mean?Standard deviation is a measure of dispersion (or how our data is spread out).Range will tell us the difference between the highest and lowest values in a data set, but nothing about how the data are distributed.We need deviation to statistically describe the distribution of values.

Variable Data Dispersion Standard Deviation PopulationSample

Variable Data Dispersion Standard Deviation CalculationMonth # of UnitsJan-2006233Feb-2006281Mar-2006266Apr-2006237May-2006260Jun-2006250Jul-2006237Aug-2006275Sep-2006218Oct-2006279Nov-2006227Dec-2006246Jan-2007258Feb-2007272Mar-2007229Apr-2007240May-2007287Jun-2007260Jul-2007251Aug-2007288Sep-2007256Oct-2007219Nov-2007260Dec-2007249

-20.2527.7512.75-16.256.75-3.25-16.2521.75-35.2525.75-26.25-7.254.7518.75-24.25-13.2533.756.75-2.2534.752.75-34.256.75-4.25

-20.2527.7512.75-16.256.75-3.25-16.2521.75-35.2525.75-26.25-7.254.7518.75-24.25-13.2533.756.75-2.2534.752.75-34.256.75-4.25

Calculate the MeanCount the Samplesn = 24Square each subtraction resultSubtract the mean from each valueSum the SquaresCalculate the DenominatorComplete the Calculation

Variable Data Dispersion Standard Deviation

Fundamental TopicThe Normal CurveProcesses have natural variationMany processes behave normallyCharacterized by Bell Shaped CurveMean near peakCurve is symmetricMean Standard Deviation

Histogram of Diameter, with Normal CurveDiameterFrequency

Measures of VariabilityThe Range is the distance between the extreme values of data set. (Highest Lowest)The Variance(S ) is the Average Squared Deviation of each data point from the Mean.The Standard Deviation (s) is the Square Root of the Variance.The range is more sensitive to outliners than the variance.The most common and useful measure of variation is the Standard Deviation.

Sample of Statistics versus Population Parameters

EstimateStatisticsParameters = Population Means = Sample Standard DeviationX = Sample Mean = Population Standard Deviation

Statistical Calculation (Sample)Standard DeviationStandard DeviationVariance Meann2346

Statistical Calculation (Population)Standard DeviationVarianceMean

Normal Distribution

Description of a NORMAL DISTRIBUTIONLOCATION:The Central TendencyIt is usually expressed as the AVERAGESPREAD:The dispersionIt is usually expressed as standard deviation (Sigma)LOCATIONSPREAD

Properties of Normal Distribution

Normal Distribution is SymmetricHas equal number of points on both sidesMean Median and Mode CoincideNormal Distribution is InfiniteThe chance of finding a point anywhere on the plus and minus side (around the mean) is not absolutely Zero.

Properties Of Normal DistributionNormal Curve & Probability Areas

068%95%99.73%

Lets Summarize

We need data study, predict and improve the processes.Data may be Variable or Attribute.To understand a data distribution, we need to know its Center, Spread and Shape.Normal Distribution is the most common but not the only shape.

Standard Deviation - GraphicallyFrequencyMonth # of UnitsJan-1999233Feb-1999281Mar-1999266Apr-1999237May-1999260Jun-1999250Jul-1999237Aug-1999275Sep-1999218Oct-1999279Nov-1999227Dec-1999246Jan-2000258Feb-2000272Mar-2000229Apr-2000240May-2000287Jun-2000260Jul-2000251Aug-2000288Sep-2000256Oct-2000219Nov-2000260Dec-2000249

Lets take our demand data and develop a histogram

Set up the scale and limits per subdivisionPlot the count of values that fall within each subdivision on the scale

Standard Deviation - GraphicallyMonthly Demand in UnitsFrequency

If my data is normal

Standard Deviation - GraphicallyMonthly Demand in UnitsFrequency

If my data is normal

Standard Deviation Simple Application

Frequency 800600400200I have a process with mean of 43 and a standard deviation of 3120014001000374342414039384847464544493536515068.3% of the data lies between what points?

95.4% of the data lies between what points?

99.7% of the area lies between what points?

Standard Deviation Simple Application

Frequency 800600400200I have a process with mean of 43 and a standard deviation of 31200140010003743424140393848474645444935365150

Standard Deviation Class ExerciseWhat is the probability that a random sample taken from this process

Will have a value between 40 and 45?Will have a value between 36 and 48?Will have a value between 33 and 51?

68.3%95.4%99.7%

Probability theory And Probability Distribution

Probability What is the role of Probability in Statistics?Any conclusion we reach on a population, based on what we know about a sample, is subject to uncertainty.This uncertainty is calculated and described using probability theoryEvery output (response) from a process adds up to 100% of the outputs of the process.

Probability MeasureEvery event (=set of outcomes) is assigned a probability measure.The probability of every set is between 0 and 1, inclusive.The probability of the whole set outcomes is 1.If A and B are two event with no common outcomes, then the probability of their union is the sum of their probabilities.

Probability Measure

Probability Building an UnderstandingWell start with a pair of diceOur customer will only accept combinations that equal 3,4,5,6,7,8,9,10 and 11.What is the probability of meeting his requirement?

ProbabilityBuilding an UnderstandingThe customer defines a response of 2 or 12 as a defectDie 1 Roll123456123456723456783456789456789105678910116789101112

Die 2 RollCalculate all possible responses from the combinations of inputsHow many total combinations exist?How many times is my response a 2?What is the probability of a response of 2?How many times is my response a 12?What is the probability of a response of 12?What is the probability of a defect? (2 or 12)

ProbabilityBuilding an UnderstandingDie 1 Roll123456123456723456783456789456789105678910116789101112

Die 2 RollAnother exampleWhat is the probability of rolling a 7 using a fair pair of dice?Die 1Die 2Probability160.0278250.0278340.0278430.0278520.0278610.0278Total 0.1668

The probability of each roll is included in each block16.68% Probability

ProbabilityValue (Response)Frequency Probability

210.0278320.0556430.0833540.1111650.1389760.1667850.1389940.11111030.08331120.05561210.0278Total 1.0000

Probability of any given value on Die 1Probability of any given value on Die 2Probability of any given combination

ProbabilityResponse (Dice Total) Probability0.02780.02780.13890.11110.08330.05560.16670.05560.08330.11110.1389

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ProbabilityOur customer will only accept combinations that equal 3,4,5,6,7,8,9,10,11We have a 99.44% probability of meeting the customers specificationThe curve of this distribution becomes its Probability Density Function

ProbabilityResponse (Dice Total) Value(Response)FrequencyProbability210.0278320.0556430.0833540.1111650.1389760.1667850.1389940.11111030.08331120.05561210.0278

ProbabilityOur customer will only accept combinations that equal 3,4,5,6,7,8,9,10,11We have a 99.44% probability of meeting the customers specificationThe curve of this distribution becomes its Probability Density Function

ProbabilityResponse (Dice Total) Value(Response)FrequencyProbability210.0278320.0556430.0833540.1111650.1389760.1667850.1389940.11111030.08331120.05561210.0278

Probability TheoryWhat is a Probability Density Function?A Mathematical FunctionIt models the probability density reflected in a histogramWith more observationsClass intervals become narrower and more numerousThe histogram of the variable takes on the appearance of a smooth curveThe total area under the curve must equal 1.The probability that a random variable will assume a value between any two points is equal in value to the area under the random variables probability density function between these two points.

h

What does this mean to us?

Probability TheoryFrequency This histogram has 24 points distributed over 12 intervals

Probability Theory

Response IntervalsFrequency 400300200100As the number of data increase, the intervals get smallerWhen we do this, the curve outlining the data gets smoother

Probability TheoryWhat do we know about Probability Distribution?The area under the curve always equals 1We can determine the probability that a value of a random variable will fall between 2 points on the curve by calculating the area under the curve between the two points

Why would we want to do this?How do we do this?

Using Probability Distribution

Using Probability Distribution

Standard Normal Curve CharacteristicsThe Standard Normal Distribution

It has a standard deviation of 1.0It has a mean of 0.0The area under the curve equals 1The curve is symmetricalAfter the Z TransformThe Original Distribution

Using Probability DistributionThe Standard Normal DistributionThe HowFind the points on the Standard Normal Distribution that correspond to your valuesDetermine the area under the standard normal curve that is between the points you have found

If our data is normal, we can use the Standard Normal Distribution

This saves us from having to do the calculation for each specific situation!

Using Probability DistributionThe Standard Normal DistributionA Why Example:The unit sales of Product A follows a normal distribution and has a monthly average of 253 units with a standard deviation of 21 units

= 253 S = 21What is the probability that next months sales will be greater than 300 units?

Using Probability DistributionThis is telling us that 300 is 2.24 standard deviations from the mean

Using Probability DistributionThe Standard Normal Distribution2) Determine the area under the standard normal curve that is to the right of 2.24How?Use the Table of the Standard Normal Distribution

2.24

Standard Normal TableZ was 2.24

Using Probability DistributionThe Standard Normal DistributionThis table shows the area between 0 (the mean of a standard normal table) and ZBecause the curve is symmetricThe area of each is 0.500The area to the right of a positive value is 0.500 minus the area between 0 and the Z valueFor Z = 2.24 (the equivalent of 300) Locate the row labeled .04The area is 0.4875Subtract this area from 0.500 0.500 0.4875 = 0.0125

I have a 1.25% probability that my unit sales next month will be greater than 300 units

2.24

Normal DistributionIf you know your average value ( ) and your standard deviation (s) then for a given specification limit, it is possible to predict rejections (if any), that will occur even if you keep your process in control.Example: = 2.85, s = 0.02 (The dimensions relate to a punched part).Lat us find the percentage rejection if the specified value is 2.850.04 i.e. the part is acceptable between 2.81-2.89

Normal Distribution

Applicable in real life:AcceptableRange Rejections Rejections 2.812.852.89

150

Normal DistributionLet A, B and C represent the areas under the curve for the following conditions:A rejections for undersizeB acceptable rangeC rejections for oversizeTotal Area = A+B+CTotal Rejections = A+C

BAC2.852.892.81

Normal DistributionWe will introduce a concept called Z which we can use with a one-sided distribution to determine the area under A, B and C and thus the percentage rejections and acceptable components.

BAC2.852.892.81

Normal DistributionThe area from Normal table corresponding to 2 is 0.02275Hence Rejection for Over size (Area C) = 2.275%Similarly one can find the rejection for undersize

Discrete Probability Distributions

Binomial DistributionWhen applicable: When the variable is in terms of attribute data and in binary alternatives such as good or bad, defective or non-defective, success or failure etc.Conditions: The experiment consists of n identical trialsThere are only two possible outcomes on each trial. We denote as Success(S) and Failure(F).The probability of S remains the same from trial to trial and is denoted by p and the probability of F is q.p+q = 1The trials are independent

Binomial Distribution

Binomial Distribution

Poisson DistributionWhen applicable:

No. of accidents in a specified period of timeNo. of errors per 100 invoicesNo. of telephone calls in a specified period of timeNo. of surface defects in a castingNo. of faults of insulation in a specified length of cableNo. of visual defects in a bolt of clothNo. of spare parts required over a specified period of timeThe no. of absenteeism in a specified no. of timeThe number of death claims in a hospital per dayThe number of breakdowns of a computer per monthThe PPM of Toxicant found in water or air emission from a manufacturing plant

Poisson DistributionTwo Properties of a Poisson Experiment1) The Probability of an occurrence is he same for any two intervals of equal length. 2) The occurrence or nonoccurrence in any interval is independent of the occurrence or nonoccurrence in any other interval.

Poisson DistributionConditions:The experimental consists of counting the number of times a particular event occurs during a given unit of time or in a given area or volume or weight or distance etc.The probability that an event occurs in a given unit of time is same for all the units.The no. of events that occur in one unit of time is independent of the number that occur in other units.The mean no. of events in each unit will be denoted by .

Poisson Distribution

Poisson DistributionSuppose the number of breakdowns of machines in a day follows Poisson Distribution with an average number of breakdowns is 3.Find the probability that there will be no breakdowns tomorrow.

Poisson Distribution

Patients arrive at the emergencyroom of Mercy Hospital at the average rate of 6 per hour on weekend evenings.What is the probability of 4 arrivals in 30 minutes on a weekend evening?Example: Mercy Hospital

Control Charts

Process Accuracy And PrecisionWe have curves that describe our processSome questions we may askIs my process accurate?Is my process precise?

Process Accuracy And Precision

Accuracy describes centeringIs my process mean at my target mean?

LSLUSLTarget

Process Accuracy And Precision

LSLTargetUSLPrecision describes spreadHow does the spread of my process compare to the customers specification limits?

Inaccurate and Imprecise

Accurate and Imprecise

Precise But Inaccurate

Accurate And Precise

Capability In Statistic Terms

LSLUSL

LSLUSL

LSLUSL

LSLUSLMean is not centered in SpecificationMean is centered in SpecificationSmall Standard DeviationLarge Standard DeviationBetter Capability

SPCPROCESSThe combination of people, equipment, materials, methods, measurement and environment that produce output a given product or service.

Process is transformation of given inputs into outputs

SPCVARIATIONThe inevitable differences among individual outputs of a process.

The sources of variation can be grouped into two major classes,

Common Causes & Special Causes

SPC

SPC

SPCCOMMON CAUSEA source of variation that affects all the individual values of the process output being studied

This is the source of the inherent process variation.

SPCCommon Causes:Plenty in NumbersResults in less VariationPart of the ProcessResults in constant VariationPredictableManagement ControllableStatistics shall apply

SPCExamples of Common Causes,

MANMACHINEMATERIALDifferences in Competency (setting, operating & inspection) of Employees working in shifts.Difference in Quality of Product when Production of same Part is being carried out as per plan. UPS provided for Electricity SupplyDifference in Mechanical & Chemical Properties in 2 different lots of Material of same grade received from suppliers (Raw Material Manufacturers)

SPCSPECIAL CAUSE:A source of variation that affects only some of the output of the process; it is often intermittent and unpredictable. A special cause is some times called assignable cause. It is signaled by one or more points beyond the control limits or a non-random pattern of points within the control limits.

SPCSpecial Causes:Few in numbersResults in large variationVisitors to the processVariation due to external factorsFluctuating VariationUnpredictableControllable by Operating personnelStatistics shall not apply

Recognize and deal with special causes outside the (Six Sigma) process Implement Corrective and Preventive Action (CAPC)

SPCExamples of Special Causes,

MANMACHINEMATERIALMETHODMEASUREMENTUntrained Employee working on the MachineProduction of Product on Conventional Lathe machine where Product Run out requirement is 2 microns. Major & frequent breakdowns of Machine. Frequent Power Failures. Use of different grade of raw materialSetting of process Parameters which are not proven.Tool breakageUse of Micrometer having range of 0-25 mm to check O.D. of 25 mm 0.1 mm.

Types of Control ChartsATTRIBUTE

pnpcu

Control ChartsOverviewThe first step for control charting is to identify the CTQs of the process which is required to be brought under control

Types of Control ChartsDepends on the nature of the variable needed to control:Variable Control ChartsAttribute Control Charts

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Variable Control ChartCONTROL CHARTS

Variable Control ChartXbar Rbar

When to use:When studying the behavior of a single measurable characteristic produced in relatively high volumes.

How:By plotting sample averages (X-bar) and ranges (R) on separate charts. This allows for independent monitoring of the process average and the variation about that average.

Conditions:Constant sample size.One characteristic per chart.Should have no less than 20 samples before calculating control limits.

Variable Control ChartXbar RbarMost common type of control chart for analyzing continuous variables.The xbar part of the chart notes the variation between the averages of consecutive sub-groups of data points.The R part of the chart notes the changes of variation within each of the consecutive sub-groups.

Variable Control ChartRATIONAL SUBGROUP CONCEPTSubgroups or samples should be selected so that if assignable causes are present, the chance for differences between subgroups will be maximized, while the chance for differences due to these assignable causes within a subgroup will be minimized.Time order is frequently a good basis for forming subgroups because it allows to detect assignable causes that occur over time.Two general approaches for constructing rational subgroups:Construction units of productionRandom sample of all process output over the sampling interval

Control ChartReviewing plots & Analysis of trends:Ensure that all points of both X and R charts within control limits.If any point touching to any of the control limits, review process related remark corresponding to particular sub-group.This is assignable cause.Study particular trends if anyCase study:Consider process of side member sub-assembly where critical dimensional characteristics i.e. concentricity of mounting holes is controlled.

Control ChartTRENDS ANALYSIS IN SPC CHARTSALL POINTS WITHIN CONTROL LIMIT

S.NOTrend TypeMeaningPrecautions for better process control1.All points within control limits with zigzag patternProcess under control, variation due to random causes.Zigzag pattern changing with each point over judgmentLet process continue. Try to make it a natural process2.7 more consecutive points on one side of center lineProcess Centre shifted towards one of the specification limitDo changes to bring process to Centre3.Cyclic trendsAssignable cause happening periodicallyStudy assignable cause and reason. Study to prevent4.Continuous inclination towards one of the control limitsAssignable cause for process drift. If not prevented, product may go out of controlStudy assignable cause, set process to prevent drifting

Control ChartTRENDS ANALYSIS IN SPC CHARTSALL POINTS WITHIN CONTROL LIMIT

S.NOTrend TypeMeaningPrecautions for better process control1.All points suddenly going out of control limitsAssignable cause present, study specific process event associated with period of specific pointStudy probable causes for assignable cause taking place try to resolve the same2.Any point going out of control limits with definite trendProcess going out of control due to assignable causeStudy the trend type & establish controls to prevent the assignable cause occurring

Typical Out-Of-Control PatternsPoint outside control limitsSudden shift in process averageCyclesTrendsHugging the center lineHugging the control limitsInstability

Shift in Process Average

Cycles

Trend

Control ChartsPURPOSE OF CONDUCTING SPC STUDIES:To study and analyze process variationTo find out trends in processesTo identify random & sporadic causesTo manufacture products of consistent qualityTo prevent wastage of material

Process Capability For Continuous Data

Capability vs StabilityCapability has a meaning only when a process is stable.If a process is out of control, first we need to stabilize the process.Improvement in the inherent variation can be made only when the process is stable.Control Charts are used to study stability.The first job of Six Sigma practitioner is to identify and remove Special Causes of Variation.Once the process is made predictable, the next job is to identify the causes of inherent variation and remove them.

Calculating Capability

LSLUSL

Calculating Capability

LSLUSL

Calculating Capability

LSLUSLSix Sigma Capability

Calculating Capability

02468101214161820

Calculating Performance

02468101214161820

Calculating PerformanceIf the formulae are same, what is the difference?The difference is in Sigma Calculation!Sigma in Capability covers Short Term Variation.Sigma in performance covers Long term Variation.How is the Data Collection Different?

Process Capability Ratios

Continuous Improvement

LSLUSLLSLUSL

Increased Number of DefectsProcess CapabilityReal Capability

Capability IndicesExample

0.3060.3080.3100.3120.3140.3160.3180.320LSLUSL

Capability IndicesExample

0.3000.3050.3100.3150.320LSLUSL

Lets SummarizeA process cannot be improved till it is Stabilized.Capability data should be utilized for stable processesSubgroups should contain consecutive data, not random data.Performance calculations should be done based on large amount of data representing Long Term Variation.

Process Capability For Attribute Data

Discrete Data CapabilityA discrete defect is an attribute, which can be counted.Such as:Scratches, Spots, Dent Marks, Cracks etc.In these cases does not make sense.A defect is non conformance to the standards.A defective unit can have more than one defect.A sample of 100, may have 2 defectives but 5 defects.

Discrete Data CapabilityDefect Opportunities:Defect opportunities are various types of defects, that may occur.These creates dissatisfaction to the customers.This is different than defects that occur.Example : 12 type of defects that can occur on painted part.However, on a part produced, we may observe 0 to up to 12 defects.Thus a part may be defect free or may have1to 12 defects.

Discrete Data CapabilityExample:A sample of 100 nos have been taken.Following are the results of inspection:

No of Defectives 3No of defects 10No of Opportunities - 12

Discrete Data CapabilityExample:The capability can be calculated as follows:No of units = U =100Defects = D =10No of Opportunities = O = 12Total defect opportunities = UxO = 100x12 =1200DPO = Defects per opportunity = 10/1200 =1/2 = 0.0083

Discrete Data CapabilityExample:Defect per million opportunities (DPMO)=DPO x 1,000,000=0.0083 x 1,000,000=8300 DPMOFrom the tables, the corresponding sigma level is 3.9.

Discrete Data Capability

The same formula also can be expressed as

Discrete Data Capability Example of DPMOSo, for every only million letters delivered this citys postal managers can expect to have 1,000 letters incorrectly sent to the wrong address.What is the Six Sigma Level for this Process?

DPMO ExampleIRS tax form adviceSurvey of responses indicates predicted error rateIf 40% then:DPO = 0.40DPMO = 0.40 defects/opportunity * 1,000,000 opportunities/million opportunities400,000 DPMO = 1.75 Sigma

DPMO ExampleProcess No.Yield in %1902993954965100

DPMO - ExerciseYou have 100 documentsYou take a sample of 10 documentsThere are 10 opportunities for defect on each document.5 defects were found.What is DPMO

Attendance PolicyJune 23, 2000Crane OperationalExcellence ProgramAll Operational Excellence Leaders should be aware.

Complexity and CapabilityPayroll and Labor Tracking ProcessDoes complexity have an important impact on process capability and quality?There are many opportunities for defectsStep 197.4%Read and record daily start and stop time

Step 699.9%Create payroll checks

Step 595.5%Transfer hour totals to payroll generation system

Step 491.8%Total weekly work hours and job accounts. Submit time cardStep 398.0%Total daily work hours

Step 294.6%Read and record daily start and stop time

Rolled Throughput Yield Example

=

Complexity and CapabilityPayroll and Labor Tracking ProcessOur goal, reduce the total number of opportunities and increase the capability of remaining opportunities

Step 197.4%

Output79.1%

Step 699.9%

Step 595.5%

Step 491.8%

Step 398.0%

Step 294.6%Rolled Throughput Yield Example=Step 399.9%Print payroll checks from computer generated database

Step 299.4%Scan employee badge and job card for labor start and stop time

Step 199.6%Scan employee badge for start and stop time

=

Complexity and CapabilityNotice any Difference?

Step 193.32%

Output81.26%

Step 393.32%

Step 293.32%

Step 299.999997%Rolled Throughput Yield Example==

Output79.1%

Step 299.999997%Step 299.999997%xxxxA Three Sigma ProcessA Six Sigma Process

Sigma Levels

SIGMADefect per Million Opportunities (DPMO)1690,0002308,537366,80746,210523363.4

Introduction To Hypothesis Testing

Hypothesis Testing ConceptHypothesis testing is one of the most scientific ways of decision making.It works very much like a court case.We have a suspect, we have to take decision whether He / She is innocent or guilty.Suppose there is person charged with murder, and both sides (defense and prosecution) do not have any evidence, what would be decision?Innocent unless proven guilty?Guilty unless proven Innocent?

Null Hypothesis

Null HypothesisNull hypothesis is represented by HoIt is statement of Innocence.It is something that has to be assumed if you cannot prove otherwise.It is statement of No Change or No Difference.

Null Hypothesis A Court CaseJust Like a court case, we first assume the accused (X) is innocent and then try to prove it otherwise based on evidence (Data).If evidence (Data) does not show sufficient difference, we cannot reject the innocence(Ho)But if Evidence (Data) is strong enough, we reject the Innocence (Ho) and pronounce the suspect Guilty (Ha).The statement that will be considered valid if null hypothesis is rejected is called Alternate Hypothesis (Ha)

Null hypothesis A ConceptHypothesis testing is a philosophy that real life situations.You cannot prove two things equal.You cannot prove two things different by proving only one differenceIf you cannot prove 2 things different, you have to assume that they are equal.But if you cannot prove them Different, are they really Equal?What is the RISK involved?

Hypothesis Testing ConceptIn Truth, the Defendant is:

Correct Decision

Innocent individual goes FreeIncorrect Decision

Guilty Individual Goes FreeIncorrect Decision

Innocent Individual Is DisciplinedCorrect Decision

Guilty Individual Is Disciplined

Verdict Innocent Guilty

Hypothesis Testing ConceptDecisionTrue, But Unknown State of the World

Hypothesis Testing ConceptHypothesis testing Justice SystemState the Opposing Conjectures, Ho and HA.Determine the amount of evidence required, n, and the risk of committing a type error,What sort of evaluation of the evidence is required and what is the justification for this? (type of test)What are the conditions which proclaim guilt and those which proclaim innocence/ (Decision Rule)Gather & Evaluate the evidence.What is the verdict? (Ho or HA?)Determine Zone of Belief : Confidence Interval.What is appropriate justice? Conclusions

Hypothesis TestingNull Hypothesis (Ho) statement of no change or difference. The statement is assumed true until sufficient evidence is presented to reject it.Alternate Hypothesis (Ha) statement of change or difference. This statement is considered true if Ho is rejected.True I Error the error in rejecting Ho when it is in true fact, there is no difference.Alpha Risk the maximum risk or probability of making a Type I Error. This Probability is always greater then zero, and is usually established at 5%. The researcher makes the decision to the greatest level of risk that is acceptable for a rejection of Ho. Also known as significant level.Type II Error The error in failing to reject Ho when it in fact false, or saying there is no difference when there really is a differerence.

Hypothesis Testing Concept6) Beta Risk The risk probability or making a Type II Error, or overlooking an effective treatment or solution to the problem.7) Significant Difference The term where a difference is too large to be reasonably attributed to chance.

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Steps in Hypothesis Testing

Define Ho/Ha For following CasesTo find if a distribution is normal or not.Ho =>Ha =?To find if the defects from three machines are same or differentHo =>Ha =>To find if 2 groups of students from different streams have differing IQHo =>Ha =>

Basic Concepts Statistical Error

Statistical Error DefinitionsNull Hypothesis Ho:Status quoNothing is differentEquality We fail to reject Ho based on statistical evidenceAlternate Hypothesis Ha:Something is differentStatement about the population that requires strong evidence to proveIf we reject Ho, we in practice accept Ha.

Statistical Error

The Central Limit Theorem NormallyWhy are distributions normal?When all factors are randomSome measurements are actually averages over time of micro-measurements In other words, what we see as a measurement is actually an averageThe Central Limit Theorem explains why a distribution of averages tends to be normal

Confidence Sample statistics estimate the mean or standard deviation of a populationThe True population mean and standard are unknown

Confidence limits, levels, and intervals are used to determine the population statisticsFor meansWe use t distribution to calculate limits, levels, and intervals

Definition Confidence Level:The level of risk we are willing to takeHow sure we want to be that the population mean or standard deviation falls between the confidence level is typical95% confidence level is typical.95% chance that the population mean or standard deviation falls between the limits.5% chance (alpha risk) that the population mean or standard deviation isnt contained within the calculated limits.

Definition Confidence LimitUpper and Lower limits that bracket the true mean or standard deviation of a populationCalculation from the sample data and the appropriate test statistic.Test statistic is dependent on the risk we accept that our results will be wrong.

Definition Confidence IntervalThe interval defined by the upper and lower confidence limits.A range of values based onSample mean or sample standard deviationSample sizeConfidence levelAppropriate test statisticContainsPopulation mean orPopulation standard deviation

Basic Concepts Confidence Limits For Means

Confidence Limit Formulas Means

Lower Confidence Limit

Upper Confidence Limit

Confidence Limit Formulas Means

Confidence Limit - ExampleSet 1 Mean: 0.250Standard Deviation: 0.005Sample Size: 25Set 2 Mean: 0.250Standard Deviation: 0.005Sample Size: 100We are 95% confident that the interval 0.2479 to 0.2521 brackets the true process standard deviation (0.0042 width)We are 95% confident that the interval 0.2490 to 0.2510 brackets the true process standard deviation (0.0020 width)

Basic ConceptsConfidence Limits For Standard Deviation

Confidence Limit Formulas VariationPopulation Standard Deviation

Confidence Limit Formulas VariationSet 1 Mean: 0.250Standard Deviation: 0.005Sample Size: 25Set 2 Mean: 0.250Standard Deviation: 0.005Sample Size: 100We are 95% confident that the interval 0.0039 to 0.0070 brackets the true process standard deviation (0.0031 width)We are 95% confident that the interval 0.0044 to 0.0058 brackets the true process standard deviation (0.0014 width)

TEST OF HYPOTHESIS - roadmapYou want to compare the averages/ medians of samples of data to decide if they are statistically differentAre samples normally distributedCompare median values instead if averageHow many samples do you wan to compareKruskall Wallis TestFor samples that do not have any outlinersOne-way ANOVAFor comparing averages of three or more samples against one another1 Sample t-testComparing av. of one sample against targetPaired t-testFor comparing averages of two samples that contain data that is linked in pairsTwo Sample t-testFor comparing averages of two samples against each otherMoods Median TestFor samples that have some outlinersTransform Data

Yes 2 1 or 3 or moreNo Noor

Design of Experiments

EXERCISERepresent the following data in graphical form:

Temperature100100120120Response275285270325Pressure 250300250300

EXERCISE - continuedDetermine what parameter settings yield the largest response.Determine what parameter settings of pressure would be bets if it were important to reduce the variability of the responses that results from frequent temperature variations between two extremes.

EXERCISE - continuedResponseTemperature

Design Of ExperimentsDesign of Experiments (DOE) is a valuable tool to optimize product and process designs, to accelerate the development cycle, to reduce development costs, to improve the transition of products from research and development to manufacturing and to effectively trouble shoot manufacturing problems. Today, Design of Experiments is viewed as a quality technology to achieve product excellence at lowest possible overall cost.

Design of ExperimentsGeneral CommentsKeep your experiments simpleDont try to answer all the questions in one studyUse 2 level designs to startTry potential business results to the projectThe best time to design an experiment is after the previous one is finishedAlways verify results in a follow-up study (verification)Be ready for changesA final report is a must to share the knowledgeAvoid DoE infatuationdo your homework first!Measure & Analyze to reduce potential variablesUse Graphical AnalysisUse the basic tools of Operational Execllence

Design of ExperimentsBe ProactiveDOE is a proactive toolIf DOE output is inconclusive:You may be working with the wrong variablesYour measurement system may not be capableThe range between high and low levels may be sufficientThere is no such thing as a failed experimentSomething is always learnedNew data prompts us to ask new questions and generates follow-up studiesRemember to keep an open mindLet the data/output guide your conclusionsDebunk or validate tribal knowledgeDont let yourself be confused by the facts.

Design Of ExperimentsTypes of Experiments

Traditional ApproachSix Sigma ApproachVery InformalVery FormalTrial and Error MethodsIntroduce a change and see what happensRunning Special Lots or BatchesProduced under controlled conditionsPilot RunsSet up to produce a desired effect.One-Factor-at a-Time ExperimentsVary one factor and keep all other factors constantPlanned Comparisons of Two to Four FactorsStudy separate effects and interactionsExperiment With 5 to 20 FactorsScreening StudiesComprehensive Experimental Plan With Many PhasesModeling, multiple factor levels, optimization

Very InformalVery FormalTrial and Error MethodsIntroduce a change and see what happensRunning Special Lots or BatchesProduced under controlled conditionsPilot RunsSet up to produce a desired effect.One-Factor-at a-Time ExperimentsVary one factor and keep all other factors constantPlanned Comparisons of Two to Four FactorsStudy separate effects and interactionsExperiment With 5 to 20 FactorsScreening StudiesComprehensive Experimental Plan With Many PhasesModeling, multiple factor levels, optimization

Design Of ExperimentsBarriers to Successful DoEsProblem or objective unclearResults of the experiments unclearBe present during the DoEIdentify and record unexpected noise or other variablesMeasurement ErrorLack of Management SupportLack of Experimental DisciplineDont use a DoE as the first pass to identify key XsManage the constants and the noiseProcess map, C&E, Constant or Noise or ExperimentalUnstable process prior to running DoEProcess map, C&E, Constant or Noise or Experimental, Manage the Cs and Ns to reduce extraneous variation

Design Of ExperimentsObjective Establish the objective for the experimentIt should be stated in such a way to provide guidance to those involved in designing the experiment.

What is the purpose of the Experiment?

Design Of ExperimentsPlanning the Experiment

Team in involvement Maximize prior knowledgePursue measurable objectivesPlan the execution of all phasesRigorous sample size determinationAllocate sufficient resources for data collection and analysis.

Design Of ExperimentsThe following are some of the objectives of experimentation in an industry:Improving efficiency or yieldFinding optimum process settingsLocating sources of variablesCorrelating process variables with product characteristicsComparing different processes, machines, materials etc.Designing new processes and products.

Various Terms Used In ExperimentationFactor:One of the controlled or uncontrolled variables whose influence on the response is being studied. May ne variable or classification data.Level:The values or the factor being studied usually high(+) and low(-)Treatment Combination:An experiment run using a set of the specific levels or each input variableResponse Variable:The variable that is being studied. Y factor in the study. Measured output variable.Interaction:The combined effect of two or more factors that is observed which is in addition to the main effect of each factor individually.

Various Terms Used In ExperimentationConfounding: One or more effects that can not unambiguously be attributed to a single factor or interaction.Main effect:Change in the average response observed during a change from one level to another for a single factor.Replication:Replication of the entire experiment. Treatment combinations are not repeated consequently.Test run:A single combination of factors that yields one or more observation of the response.Treatment:A single level assigned to a single factor during an experiment.

Trial And ErrorPerhaps the most well known and used methodology.The objective is to provide a quick fix to a specific problem.The quick fix occurs by randomly and no-randomly making changes to process parameters.Often changing two or more parameters at the same time.The result often is a Band-Aid fix as the symptoms of the problem are removed, but the cause of the problem goes undetected.In trial and error experimentation, knowledge is not expanded but hindered.Implement multiple expensive fixes are not necessary.

One-Factor-At-A-Time (OFAT)The old dogma in experimentation is to hold everything constant and vary only one-factor-at-a-time.Assumes any changes in the response would be due only to the manipulated factor.But are they?Is it reasonable to assume that one can hold all variables constant while manipulating one?Experience tells us this is virtually impossible.Imagine there area large number of possible factors affecting the response variable:How long would OFAT take to identify critical factors and where they should be run for best results?How much confidence would you have that the knowledge gained would apply in the real world?

OFATAlthough OFAT may simplify the analysis of results, the experiment efficiency given up is significant:Dont know the effects of changing one factor while other factors are changing (a reality).Unnecessary experiments may be run.Time to find casual factors (factors that affect the response) is significant.

Classification Of FactorsExperimental Factors are those which we really experiment with by varying them at various levels.Control Factors are those which are kept at a constant (controlled) level throughout experimentation.Error or Noise factors are those which can neither be changed at our will nor can be fixed at one particular level. Effect of these factors causes the error component in the experiment and as such these factors are termed as error or noise factors.

Experimental DesignHigh Factor 1Low

Experimental Design

High Factor 1High Factor 2Low Low

Experimental Design

High Factor 1High Factor 2Low Low High Factor 3Low

Experimental Design

Factor 4Low High

Experimental Design

Factor 4Low High

Factor 4High

Factor 5Low

Here is where its time to stop drawing but it represents the complexity associated with a 5 factor design.

Experimental DesignThree Factorial Design, without interaction

Experimental DesignThree Factorial Design, with interaction

Design Of Experiments (DOE)

(a) Only Interaction, no main effectCancel Amount

DOE: Continued(b) Only Main Effect, no Interaction

(c) Main Effect with Slight Interaction

Example : Full Factorial Cookie DOECompany HillsBerry produces premium cookies.The company needs to increase throughput in the bake process by 20% in order to keep up with demand.Market research indicates customers require the cookies have a taste index rating greater than 45.They are interested in understanding the effects of two factors, bake time and bake temperature:1) Bake Time (A)2) Bake Temperature (B)The current bake process is 10 minutes at 375 F per batch.

Full Factorial Cookie DoE

Full Factorial Cookie DoEDOE Design and Results

Questions?Which Factors are important?How should the important factors be set?

Bake Time (min)A610610

Taste

41504735

Bake Temp (F)B375375450450

Full FactorialDOE Design and Results

Sort by taste and look for trends in the factorsPractically speaking, what observations can be made about the data if:A change of 10 is required?A change of 50 is required?

What preliminary actions would you recommend right now?

Bake Time (min)A10 (+)6 (-) 6 (+)10 (-)

Taste

50474135

Bake Temp (F)B375 (-)450 (+)375 (-)450 (+)

Full FactorialDOE Design and ResultsTo begin analyzing the importance of each factor, code the levels (+) and (-).

Using the (+) and (-) designation will be helpful in understanding how designs are generated and allow simple analysis of the DOE.

Bake Time (min)A10 (+)6 (-) 6 (+)10 (-)

Taste

50474135

Bake Temp (F)B375 (-)450 (+)375 (-)450 (+)

Analyzing the Full FactorialBakeTimeABakeTimeBABTaste6 (-)375 (-)+4110 (+)375 (-)-506 (-)450 (+)-4710 (+)450 (+)+35

Notice how the effects compare to the graphs

Interactions Interaction: 2 factors (input variables) interact if one factors effect on the response is dependent upon the level of other factor.

For Example: Time and temperature in Baking Cookies

BakeTimeABakeTimeBABTaste6 (-)375 (-)+4110 (+)375 (-)-506 (-)450 (+)-4710 (+)450 (+)+35

Strong interactions are indicated by lines crossing at nearly 90 (perpendicular). As the lines become more parallel, the interactions are weaker.Data Set:

Interactions The (+) & (-) coding for the interaction can be created by multiplying columns A and B:

Now we can calculate the importance (effect) of the interaction of time and temperature on taste for baking cookies:To determine the interaction effect use the +,- column to group the data just like before.In this case: AB = (41+35)/2 (50+47)/2 = -10.5

AxB=AB1-x-=+2+x-=-3-x+=-4+x+=+

BakeTimeABakeTimeBABTaste6 (-)375 (-)+4110 (+)375 (-)-506 (-)450 (+)-4710 (+)450 (+)+35

Notice how the effects compare to the graphs

Analysis SummaryNow that we have calculated the effect for time, temperature and the time/temperature interaction we can compare the relative importance of each:

In this experiment, the Time x Temperature Interaction has the largest effect and is therefore the most important.Practical Significance:Controlling Time or Temperature alone will not ensure good tasting cookies. Time and Temperature should be analyzed and set together to ensure the best tasting cookies.

Factor Effect Time 1.5Temperature 4.5Time x Temperature10.5

Analysis Summary

Y = Grand average + ((effect A)/2) *A+ ((effect B)/2)*B + ((effect AB)/2)*ABTaste = 43.24 ((1.5)/2)*A ((4.5)/2)*B ((10.5)/2)*AB

What is the optimum setting of time and temperature that will yield a 20% improvement in throughput and maintain the taste index above 45?

Time (factor A) needs to be 8 minutes and Y (taste) needs to be > 45Plug into predictive equation, and solve for temperature Remember the equation is based on -1 to +1 coded dataFactor Effect Time (A)-1.5Temperature (B)- 4.5Time x Temperature-10.5

Creating The Predictive EquationSolve for time reduction at 80% of 10 minutes = 8 minutesActual time = (hi + lo)/2 + ((hi lo)/2)*Coded value(A)Actual time = (10+ 6)/2 + ((10 6)/2)*Coded value(A)8 = 8 + 2*(0) coded valueThe coded value for 8 minutes is 0Y = 45A = 0Use the predictive equation and solve for B (temperature) -45 = 43.24 ((1.5)/2)*((4.5)/2)*B ((10.5)/2)*(0)*B Solve for B Coded value for B = -.78Converting back to actual temperatureActual temp = (hi + lo)/2 + ((hi lo)/2)*Coded Value(B)Actual temp = (450 + 375)/2 + ((450 375)/2)*(-.78)Actual temp = 383 F

8 minutes at 383F will achieve the desired throughput and delight the customersDetermine the optimal settings for the bake process

3 factors at 2 levels (Time-A, Temperature-B & Pressure-C)8 runs minimum (2x2x2)3 factor columns:

3 factor columns:

#ABCABACBCABC1---++2+----3-+--+4++-+- 5--++-6+-+-+7-++--8+++++

Complete the interaction columnsHow would you calculate the importance (effect) of each factor?

Full FactorialThe limitations of a full factorial experiment are not in theory but bin practically. The resources (i.e. time, cost) necessary to run full factorial designs can be significant.Full factorials can be used when investigating a small number of variables (2-4), but are not recommended when investigating a larger number of factors (5 or more)The number of runs needed increases exponentially with the number of factors. Therefore, the time and costs involved in running the experiment become prohibitive.This does not mean that full factorials are not useful, but that they should be used at the proper time (see experiment strategies).

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DOEDefine goals/objectives:Specific Measurable Determine response variables(s):Investigate measurement systemConsider multiple responsesSelect factors:Basic logical problem solving techniques (Process map, FMEA, C/E, ranking etc.)Consider the strategyIdentify factor levels:Realistic (would you want to run at the selected levels?)Move the response variable

Choose appropriate design and assign factors:Increase resolution if possible by properly assigning factorsConsider where interactive effects will show upPlan experiment:Sampling, replication, repetitionData collection methodsPerform Experiment:Note observations during the experimentAnalyze results: Six Sigma rules of AnalysisPracticalGraphicalAnalytical Make decisions:Goals met?Plan next experiment?Management presentation?

Summary Learning requires two necessary elements: a significant event and a perceptive observer.Learning is accelerated by creating significant events and observing them. Designed experiments are most effective.Learning is an iterative process.We make hypothesis based on our current knowledgeWe test these hypothesis by collecting data and informationWe make deductions based on information collected, and form new hypothesis.Repeat collecting data until we are confident we have learned what we need toFactorial designs helpThey are efficient testsInformation from factorials may be combined to get better results faster

Bench Marking

Benchmarking Benchmarking is Quality by Comparison for achieving better standards. In the global movement today, the competition is improving at a faster rate, and the only way to improve your relative quality and move upwards to find out and implement the best industry practices.

Benchmarking Benchmarking :-Stages of ImprovementWorld ClassRecognized as the best Benchmarked by othersEfficientMeets all internal requirements for cost margins, asset utilization, cycle-time and measures of excellenceEffectiveSatisfies all customer requirementsIncapableIs inefficient, ineffective and at the risk of failing. Needs major redesignBest in ClassExceeds customer expectations, outperforms all competitors and has clear competitive edge

Benchmarking MethodologyBenchmarking can be of various types Competitive benchmarkingProduct benchmarkingProcess benchmarkingBest practices benchmarking

Whatever be the category chosen by the organization, the benchmarking methodology remains the same in each case.

A. Identify Processes To BenchmarkSelect Processes to BenchmarkMeasure current process capability and define goal.Understand detailed process which needs improvement.

B. Select Organization To BenchmarkSelect organizations which perform your process.Compile a list of world class process parameters.

C. Compile the required InformationDevelop a detailed questionnaire to obtain detailed information.Obtain the desired information. The information can be obtained from various sources viz: internet, trade journals, professional associates, industry publications, industry experts, libraries etc.

D. Analyze GapsToo much information is as bad as too little.Gather and analyze only the information you need to make a direct comparison of performance.Compare like with it.Identify the performance gaps and develop an action plan to close the gaps.Also highlight and quantify the consequences of not closing the gap.

E. Develop An Action PlanReview observations of the gap analysis.Set new performance standards.Develop an action plan for meeting the new performance standards, identify process owners with their responsibilities and move to improvement phase.

Mistake Proofing

Mistake ProofingMistake proofing is a scientific technique for improvement of operating systems including materials, machines and methods with an aim of preventing problems due to human error. The term error means a sporadic deviation from standard procedures resulting from loss of memory, perception or motion.

Defect Vs ErrorsIt is important to understand that defects and error s are not the same thing. A defect is the result of an error, or an error is the cause of defects as explained below.

ERRORDEFECTCauseResult

Prevention of DefectsMachine or human error

Zero defect

Take action feedbackDetect error

Cause Stop errors from turning into defectsEnd ResultIntermediate Result

Type of ErrorsError in memory of PLAN: Error of forgetting the sequence/ contents operations required or restricted in standard procedures.Error in memory of EXECUTION : Errors of forgetting the sequence/ contents of operations having been finished.Error in PERCEPTION of type : Error of selecting the wrong object in type of quantity.

Types of ErrorError in MOVEMENT : Error in misunderstanding/ misjudging the shape, position, direction or other characteristics of the objects.

Error in motion of HOLDING : Error in failing to hold objects.

Error in motion of CHANGING : Errors of failing to change the shape, position, direction or other characteristics of object according to the standard.

Are Errors Unavoidable?Traditional view: Errors are inevitablePeople are only humanThere is variation in everythingLack of standard operating procedures result in each person having their own way to do thingsInspection is necessarySix Sigma View: Errors can be eliminatedNot all errors can be eliminated, but many can and others can be reducedThe more errors we can eliminate, the better our qualityThe need for inspection can be reduced or eliminated

Basic Functions of Poka YokeDEFECTSWARNINGFLOW CHARTSHUTDOWNWARNINGCONTROLSHUTDOWNOCCURREDABOUT TO OCCURSTATENormal functions stopped when defect predictedFUNCTIONEven intentional errors are impossibleSignals that abnormality or errors are about to occurNormal functions stopped when defect detectedDefective items cannot pass on to next stageSignals that defects have occurred

Principles of Poka YokeRespect the intelligence of workers.Take over repetitive tasks or actions that depend on constantly being alert (vigilance) or memory.Free a workers time and mind to pursue more creative and value-adding activities.It is not acceptable to produce even a small number of defects or defective products.The objective is zero defects.

Ten Types of Human ErrorsForgetfulness (not concentrating)Errors due to misunderstanding (jump to conclusions)Errors in identification (view incorrectlytoo far away)Errors made by untrained workersWillful errors (ignore rules)Inadvertent errors (distraction, fatigue)Errors due to slowness (delay in judgment)Errors due to lack of standards (written & visual)Surprise errors (machine not capable, malfunctions)Intentional errors (sabotage least common)

Use Mistake Proofing to Eliminate these Human Errors

Human Error Provoking ConditionsAdjustmentsTooling/ tooling changeDimensionality/ specification/ critical conditionMany parts/ mixed partsMultiple StepsInfrequent ProductionLack of, or ineffective standardsSymmetry Asymmetry Rapid RepetitionHigh volume/ extremely high volumeEnvironment conditionsMaterial/process handlingHousekeepingForeign matterPoor lighting

Three levels of Poka YokeCatching errors before they create defects,Catching errors during the process of creating defects,Catching errors that have created defects and keeping the defects from further in the process.

Poka Yoke How to use it?Step by step process in applying Poka-YokeIdentify the operation or process based on a Pareto.Analyze the 5-whys and understand the ways a process can fail.Decide the right poka-yoke approach, such as using a shut out type (preventing an error being made), or an attention type (highlighting that an error has been made) poka-yoke.Take a more comprehensive approach instead of merely thinking of poka-yoke as limit switches, or automatic shutoffs. A poka-yoke can be electrical, mechanical, procedural, visual, human or any other from that prevents incorrect execution of a process step.

Where Poka Yoke works wellWhere manual operations and thereby worker vigilance is needed.Where mis-positioning can occur.Where adjustment is required.Where teams need common-sense tools and not another buzz-word.Where SPC is difficult to apply or apparently ineffective.Where training cost and employee turnover are high.Where customers make mistakes and blame the service provider.Where special causes can reoccur.Where external failure costs dramatically exceed failure costs.

Poka Yoke advantagesNo formal training programs required.Eliminates many inspection operations.Relieves operators from repetitive tasks.Promotes creativity and value adding activities.Results in defect-free work.Provides immediate action when problems arise.

Human Error Provoking SituationsInadequate written standardsToo many partsMix upToo many stepsSpecifications or critical conditionsToo many adjustmentsTooling changeFrequent changeEnvironment factors

Principles Of Mistake Proofing

The principles of mistake proofing can be categorized into groups.Prevention of occurrenceMinimization of effects

A. Prevention Of Occurrence

Methods under this principle aim to prevent the occurrence of human errors from all stages of operations and make corrections unnecessary. This can be done through the following 3 methods :-Elimination ReplacementFacilitation

Elimination Elimination method aims to remove the system properties which generate operations/restrictions susceptible to human errors so as to make them unnecessary. Consider the error of an operator touching a high temperature pipe and getting burnt. One method of preventing this error is to make the pipe safe by covering it with insulating material. This improvement removes the prope