Slide 1

What is Six Sigma?

Six Sigma is a highly disciplined approach used to reduce the process variation to such a great extent that the level of defects reduced to less than 3.4ppm. Sigma (), a Greek letter used to describe variability. Defect level is measured in terms of PPM or DPMO.

ObjectivesWhat is 6 Sigma ?Why are we using 6 Sigma ?How deploy 6 Sigma ?2Objective 1What is Sigma ()?The goal of a Six Sigma process is to:Minimize variation Center the process, such that the center of the value delivered is at least six standard deviations away from what is considered non acceptable. or the upper or lower customer specification.The degree of acceptable variation in a process depends on knowing the target.

Do you know where your target is?

3Notice that minimize variation is first. Six Sigma is really about minimizing variation and then targeting. Also, in order to determine if your process is 2 Sigma or 3 Sigma or 6 Sigma, you must know where your process is relative to your customers specification limits. The belt should ask the customer what the acceptable specification limits are for the process metric(s). Too often, we assume we know what the customer wants. Stress that this includes internal customers too. The customer is the person or group who receives something (information, materials, parts, forms, etc.) from the process in question.Demings Philosophy On Processes85% of the reasons for failure to meet customer requirements are related to deficiencies in systems and processesrather than the employee. The role of management is to change the process rather than badgering individuals to do better.- W. Edwards DemingSix Sigma Focuses On Change4Six Sigma is about improving processes not fixing people.What is Six Sigma?Simple:Eliminate variation and defectsEliminate the opportunity for variation and defectsEliminate non-value added activities ComplexVisionMetric (Standard of measurement)BenchmarkPhilosophy MethodTool for: Customer focus, Breakthrough/Continuous improvement, People InvolvementAggressive Goals5Eliminate waste!Reduce variation!Set very aggressive goals!

For belts, Six Sigma is about reducing defects and eliminating non-value-added operations such as inspection and rework. At the management level, Six Sigma is about focusing on the customer, setting aggressive goals to achieve desired financial results and involving people in continuous improvement projects.Changing the Decision-Making ProcessesDecision-MakingGrowth PathTypes of ProblemsWill Normally SolveIntuition, gut feel, I think We have Raw Data and look at it.We make graphs/charts of the data.We use advanced statistical tools to evaluate the data.Simple

Complex

How many times have you heard this? I think the problem is1. We will use intuition of people Process maps C & E Matrix Fishbone diagrams FMEA

2. We will use raw data and look at it Cost Delivery Scrap & Rework Warranty3. We will make graphs/charts of the data Box plots Check sheets Half normal plots Histograms Normal Distribution Normality Plots Pareto charts Run Charts Scatter plots SPC charts Time series plots4. We will use advanced statistical tools to evaluate (Maybe use these types of tools) Analysis of Variance (ANOVA )Capability analysis Descriptive statistics Chi square F tests Fractional factorial experiments Full factorial experiments Hypothesis testing Inferential statistics Central Limit Theorem Confidence intervals Measurement Systems Analysis (MSA) Multi-Vari studies Multiple regression Response surface methods Sample size determination Screening studies Simple regression Transformations t test61. We will use intuition of people 4. We will use advanced statistical tools to evaluate (Maybe use these types of tools) * Analysis of Variance (ANOVA Process maps* Capability analysis C & E Matrix * Descriptive statistics Fishbone diagrams* Chi square FMEA* F tests2. We will use raw data and look at it* Fractional factorial experiments Cost* Full factorial experiments Delivery * Hypothesis testing Scrap & Rework* Inferential statistics WarrantyCentral Limit Theorem3. We will make graphs/charts of the dataConfidence intervals Box plots* Measurement Systems Analysis (MSA) Check sheets* Multi-Vari studies Half normal plots* Multiple regression Histograms* Response surface methods Normal Distribution* Sample size determination Normality Plots* Screening studies Pareto charts* Simple regression Run Charts* Transformations Scatter plots* t test SPC charts Time series plots

What is Six Sigma ?2308,537366,80746,210523363.4sPPMProcessCapabilityDefects per Million Opportunity269.1%393.32%499.379%599.9767%699.99966%s% Non-Defective295.46399.73499.9937599.999943699.9999998s% Non-DefectiveWITH 1.5 SHIFT7The first table represents the actual area under the Normal Distribution curve associated with each Sigma level shown. For example, we would expect 95.46% of the data to fall between +/-2 standard deviations from the mean. We would expect 99.9999998% of the data to fall between +/-6 standard deviations from the mean. When we use the term, %Non-Defective, we are talking about capability to meet spec limits. So, a 6 Sigma process is one in which 99.9999998% of the process data fits between the customer defined spec limits.

In the early days of Six Sigma, Motorola and others believed that the %Non-Defective values in the first chart could only be obtained short-term. They believed that long-term, the distribution for most processes, shifted as much as 1.5 standard deviations. The second (center) table lists the %Non-Defective values assuming the 1.5 sigma shift. So, 2 sigma is really .5 sigma and 6 sigma is really 4.5 sigma if you try to correlate the %Non-Defective back to the area under the Normal Distribution curve.

The third table represents the PPMs associated with the %Non-Defectives in the second table. Think about the difference between giving your customer 3.4 DPM in the long-term and 66,807 DPM in the long-term. Most companies strive to be between 3 and 4 Sigma. Thats a lot of defects. Chrysler is pushing CMEP to get the entire engine to 50 DPM (which is close to 5.5 Sigma).Practical Meaning99% Good (4S)99.99966% Good (6S)Postal System20,000 lost articles of mail/hour7 lost articles/hour

Airline System2 short/long landings/day1 short/long landing/5 years

Medical Profession200,000 wrong drug prescriptions/yr68 wrong drug prescriptions/yr8Using the industry definition of Six Sigma, we assume that 99% Good is a 4 Sigma process and 99.99966% Good is a 6 Sigma process.

Ask the belts to comment on the differences shown above. Is 4 Sigma OK for these processes?7Sigma Level1,000,000100,00010,0001,000100101PPMRestaurant BillsPayroll ProcessingPrescription WritingBaggage Handling AirlineSafety Rate345621What Does 6 Sigma Mean In Your Daily Life?Best in ClassTax Advice9Discuss chart. Most belts are usually surprised about prescription writing.

Manufacturing Variation Causes A "Hidden Factory"Increased Cost - Lost CapacityYield After Inspection or TestEach defect must be detected, repaired and placed back in the process. Each defect costs time and money.ScrapReworkHidden Factory

NOTOKOperationInputsInspectFirst Time YieldOKTime, cost, people90% Customer QualityDefects and the Hidden FactoryProcess Consistency = Reduced Variation = Reduced no. of defects = Reduced need for inspection/rework.10Process Consistency = Reduced Variation = Reduced number of defects = Reduced need for inspection/rework. 66% is not 90% ... why not?Scrap90% Customer QualityReworkHidden Factory

NOTOKYield After Inspection or TestOperationInputsInspectFirst TimeYield=OKUsing Final Test First Time Yield ignores the hidden factory. Final test performance is a function of inspection/test or prevention.Process123Rolled Yield81 %73 %466 %Final Test

=

90%Yield

90%Yield90%Yield90%Yield90%Yield

Rolled Yield Versus First Time Yield11Notice only 66% of the product is exiting this process as correct with regard to First Time Yield (FTY). (.90*.90*.90*.90 = .66)

Variation is the Enemy of Quality! One small instance of variation is not going to hurt much. But, when variation is stacked on top of variation all through the total process, it creates lots of defects to fix. This is what causes rework, scrap, warranty returns, as well as, overtime needed to fix the problems. A hidden factory costs money, time and resources.

Premise for Six Sigma MethodsSources of variation can be:IdentifiedQuantified and prioritizedEliminated by control or preventionLower SpecUpper Specssssssssssss12The Six Sigma methods cover all three points above.LSLUSLDefectsProcess CapabilityInadequate Design MarginInadequate Process CapabilitySupplier VariationInadequate Measurement CapabilityDissecting Process Capability13Sources of variation which cause defects may be derived from any of the major categories below.

ManMethodMachineMaterialsMeasurementsEnvironmentObjectivesWhat is 6 Sigma ?Why are we using 6 Sigma ?How are we deploying 6 Sigma ?14Next topic.Business Impacts of VariabilityPrevention CostsEducation and trainingQuality planningProduct design qualification testsSupplier qualificationCustomer interfaceControlling processesAppraisal CostsIncoming inspectionMaintenance and calibration of equipmentSetup inspection and testsField testingProcess auditsInternal FailureScrap Supplier caused losses ReworkRetest / Re-inspection Unplanned downtimeTrouble shootingEngineering change noticesExternal FailuresProcessing customer inquiriesMaintaining customer field service Retro fit costsIncurring penalties/claims Product warrantyLost sales15PAF categoriesTypical Cost / OccurrencePrevention$0.01 - $1.00Appraisal$1.00 - $10.00FailureInternal$10.00 - $100.00External$100.00 - $1,000.00 +

Occurrences may be tracked for most of these categories, but costs related to the occurrences may not be captured

Most organizations measure the Internal and External failure costs.

Appraisal costs are also known as Hidden Factory costs.ObjectivesWhat is 6 Sigma?Why are we using 6 Sigma?How are we deploying 6 Sigma?16Next TopicA Simple Approach to Breakthrough PerformanceThe right support+The right projects +The right people +The right roadmap and tools = The right results17Note to instructor: each of the elements will be covered in detail on the following slides.A Simple Approach: The Right SupportInfrastructure to drive Six Sigma throughout the company is in placeQuality Champions support specific business units Sponsors are identified and trainedMaster Black Belts are trained, experienced and available18Infrastructure includes the training plan, the database, Sponsor reviews, closed project audits, etc.

Ask participants if they know who their Quality Champion, Sponsor and MBB are. For those who don't know, you may either take the time during class to help them identify their support people or table it for later clarification. .Improve ProfitabilityStrategies$X million profit (+$X million 6S) savings- Increase on-time delivery...GoalsProjects- Improve RTY from __% to __% for HC shop New Product IntroductionDeliver X units of product at less than ___ warranty accrual by year end- Improve BOM accuracy...- Reduce supplier leadtimes..- Reduce failure rates...Growth: Global RentalOpen markets in four new countries- Reduce product variance by market...- Reduce product delivery lead time...- Improve order processing time...A Simple Approach: The Right Projects19Projects are selected to directly link to the company's strategies, goals and initiatives.

The objective of each project is specific and measurable, i.e. delivery time, yield rate, accuracy, failure rate, product variance, etc.

Note that the above projects are only partially described in consideration of slide size. The baseline and goal levels (from X to Z levels ) for the outlined project Ys must be clearly identified in the actual projects, as well as defined the constraining Ys, etc. Refer to the Project Definition module for the complete explanation of an acceptable project objective statement. Critical Mass Of Paradigm ShiftersA Simple Approach: The Right PeopleQuality ChampionsSponsors Master Black Belts Black Belts Green Belts

20Note to instructor: roles and definition of these people are covered in in the next few slides.Six Sigma ProjectsMBBsTechnical ConscienceProject ReviewsMentor BeltsAssist Local LeadersBeltsDMAICLead TeamsSponsorsComplete the CharterBreak BarriersSelect the TeamReview ProjectsMeet Weekly with BeltLocal LeadersEstablish Business GoalsSelect ProjectsEstablish Project PrioritiesSelect BeltsConduct ReviewsVerify SavingsAudit Control PlansBU Quality ChampsEnsure Six Sigma Standards are followedMonitor Six Sigma MeasuresAssist BU leadership

Process OwnerControl Plan Ownership

The Six Sigma Management SystemA Simple Approach: The Right People21The local leadership team consists of the BU leaders, including the quality champion.

The Belt's primary interface, besides the team they develop, are their sponsor (meet weekly) and their Master Black Belt (meet monthly).

Spend some time reading through each of the six primary Six Sigma roles.

Note that sometimes a single person may serve more than one role, i.e. be both a project Sponsor and Process Owner.

Strategic leaders for Six Sigma ImplementationQuality Champion Roles and ResponsibilitiesImplements Six Sigma within the BUTracks number of Belts and projectsSelects Belt candidatesDevelops hopper of Six Sigma project ideasSelects projects to be chartered22Review the slideTactical Leaders for Process ImprovementSponsor Roles & ResponsibilitiesArticulate need for a projectProvide leadership and direction to the BeltBreak down organizational barriersStakeholder - key beneficiary of project improvementReview progress continuously

23Review the slideEnsure business is self sustaining in 6s deploymentMaster Black Belt Roles & ResponsibilitiesExperts in Six Sigma Tools and MethodologyEnsure results achievementAssist in project identification and 6 Sigma administrationBreak down technical barriersCoach and mentor Belts during projectMaintain training materialsConduct Belt trainingContinuously improve Six Sigma processIdentify, share and deploy Best PracticesAn optional Growth Path for BBs24Expectations of a Master Black Belt:successfully performed as a BBdemonstrated ability in statistics and analysiswillingness and ability to teach large groupsadditional 2 year commitmentSix Sigma process improvement driversBlack Belt Roles & ResponsibilitiesLeaders of strategic, high impact process improvement projectsDevelopers of a functional teamExperts in applied quality and statistical toolsTechnical/analytical expertiseChange AgentsHigh energy result gettersLeadership development pool100% DEDICATED to Six Sigma initiative25Expectations of Black Belts:Must approach projects concurrently (2 or more simultaneously)More difficult project assignmentsProject may cross functional boundariesSeven projects completed over 2 years$250K/project average yieldVariable pay, based on successful project completion

Creating Critical Mass with Local FocusGreen Belt Roles & ResponsibilitiesLeaders of process improvement projectsWork in own functional areaDrive continuous process improvement Technical process expertsAppliers of quality and statistical toolsChange Agents 20-40% time allocation to Six Sigma26Expectations of Green Belts:Probably will approach one project at a timeLower to moderate project difficultyProjects are usually within functional boundaries$100K/project average yield No variable pay

A Simple Approach: The Right Roadmap And ToolsProcess Improvement MethodologyProject TrackerAdvanced tools27Process Improvement Methodology : defined on slide #61

Project Tracker on slide #62

Basic/Advanced tools are on slide #73

MeasureAnalyzeImproveControlDefineThe Process Improvement Methodology28Five phases of Process Six Sigma. Each discussed in detail on next slide.

29Explain to the Belts that this is the roadmap that they will use and depend upon as they progress through their projects.

The five phases represent the five rows, but each row includes specific tools or processes that must be completed. Mandatory MBB reviews are included in the Tracker. Top ToolsProcess mapCause and effects matrixMeasurement system analysisCapability analysisDescriptive statisticsGraphical techniquesBox PlotsHistogramsScatterplotsTime series plotsRun chartsPareto chartsCheck sheetsStatistical process control chartsCorrelationSimple and multiple regressionInferential statisticsConfidence intervalsFailure modes and effects analysisMulti-vari studiesFractional factorial experimentsFull factorial experimentsResponse surface methodsTransformationsNormal distributionSample size determinationTest for Equal VariancesFishbone diagramsHypothesis testingF-testT-testChi-square testTests for normalityNon-Parametric TestsAnalysis of Variance30With the exception of response surface methods, all of these tools will be covered during the 3.5 weeks of training. RSM will be mentioned during DOE.

Note: They may be tools from DFSS (for example, customer requirements, KJ, QFD, concept generation, reliability, tolerancing, robust design, value stream/lean tools, monte carlo simulation, etc.) that may also be useful for the belt. The home MBB can help belts identify if any of the DFSS tools could apply to their specific projects.

There may also be other basic or advanced tools (such as Affinity or Force Field) that the MBB may suggest to the belt as appropriate. Overall ApproachPractical ProblemStatistical ProblemStatistical SolutionPractical Solution

HEADSETHEADSETTOOLSET

31Practical ProblemThe YQualitative ProblemStatistical ProblemPossible XsQuantitative ProblemStatistical SolutionThe Critical XsQuantitative SolutionPractical SolutionThe Improved YQualitative Solution

Headset means that more subjective tools are used at this stage, while Toolset suggests objective, statistical tools used at this stage.

Explanation of data:Y = f (X1 + X2 + X3 + Xn)Y is the variable that you want to improve (the output)f is the functionX are the input variables that influence the Y variable (Man, Method, Machine, Material, "mother nature" (environment, etc.)

This is one of the most important slides:describes how a real problem is solved by the 6S statisticsdefines the key concept of a particular metric being a function of many other metrics, in mathematical equation format.

Everyday example: Practical Problem - I want a a washing machine which will complete a load of laundry on average in 10 minutes.Statistical Problem - I will compare the average cycle time to complete a laundry load for Washer Model A to Model B using a statistical tool (ANOVA).Statistical Solution - Using my statistical tool, I determined that there is adifference between the average cycle time of Models A and BPractical Solution - I will choose Washer model B because it meets my requirementof completing a load of laundry in an average of 10 minutes. Our Outputs (Y) are determined by our Inputs (Xs). If we know enough about our Xs we can accurately predict Y. If we dont know much about our Xs, then we have to resort to Inspection and Test (Non Value-Added Operations)How Do We Improve Capability?)x,...,x,x,f(x=Yk321By knowing and Controlling the Xs, we reduce the Variability in Y. We eliminate or reduce inspection, test and rework.Paint Thickness = (Paint Viscosity, Spray Angle, Spray Pressure, Standard Operating Procedures, , Spray Pattern)32Y = f (X1 + X2 + X3 + .. Xn)

The project Y-variable is the key metric that is to be improved during this six sigma project. There will be many inputs that could be significantly affecting the Y-variable. The Six Sigma methods and tools will help the belt identify the critical few inputs (Xs) that are affecting Y and that must be controlled.

Think of some of Cummins manufacturing processes where many (10+) critical metrics are charted each day, week or month. How much attention is paid to each chart? Does anyone notice if a chart shows a trend? If we are overwhelmed with charts and data, we are more likely to ignore them all. Six Sigma is about monitoring the vital few Xs and reacting when there are signals that something has changed.

The Funneling EffectOptimized Process+30 Inputs8 - 104-83-6Found Critical XsControlling Critical Xs10-15All Xs1st Hit ListScreened ListMEASUREANALYZEIMPROVECONTROL33The process may start out with 30+ potentially key inputs. There are some processes which may have 100+ inputs. Six Sigma methods and tools will help the belt logically funnel out the vital few key inputs affecting the Y-variable so that very few must be controlled.A Simple Approach: The Right Results5 Black Belt projects completed with > $10M estimated annual savings, each12 Green Belt projects completed with > $1M estimated annual savings, each$25 million Six Sigma savings in Year 20xx$132M Six Sigma savings in 20xy$397M Six Sigma savings in 20xzAverage completed BB project worth $366K/year, with a median value of $250K/yearOverall, most projects have realized more savings than expected by uncovering unexpected opportunities34Six Sigma MethodsSix Sigma EverywhereMFG.DESIGNSERVICEPURCH.MAINT.ADMIN.QA35Quality EngineeringQuality planSQIEAssemblyTestCustomer needsServiceReliabilityManufacturing EngineeringProcess improvement and designProduct EngineeringProduct design, specifications, standardsMaterialsSupplier interfaceHuman Resourcesretention, hiring cycle timeFinancepayroll processesworking capitalShop OperationsCost, deliveryInformation Technology (IT)

The Six Sigma Steering CommitteeSponsor RatingMBB RaringDefine G GMeasure G GAnalyse G GImprove Y YControl Y YBeltSponsorVishwajay ChakravartyMBBQuality ChampionProject Start date08-10-2007Target Completion date08-03-2008Date of project presentation11-19-2007Throughput Project Open days333 DaysSponsors reviewed proj.?Yes No of team meetings10Project LAUNCHED on Database?NoCurrent Year Savings0$ Annualized Savings0$Phase completedDMAICBelts % time on project20%

Reduce the number of certifications caused due to system error in the Time Attendance System from an average of 82/month to less than 20/month.37Project Description: ________________________________Objective:Benefits:Project Team:Schedule:Belt:Team members:Sponsor:Controller:MBB:Measure: ___________________ Improve: __________________ Analyze: ___________________ Control: __________________ Closeout: ___________________

Reduce/optimize/increase ___________________________ (project y)from ________________________________________(current level)to ___________________________________________ (good level)for ________________________________________ (specific area) while reducing/increasing/holding constant_________________________________________( constraining Ys)383Have a copy of this for each student to use in the charter exercise.Objective:Benefits:Project Team:Schedule:Belt: Team members: Rajeev Ranjan, Mansi Jain, Kaushalendra Kumar, Sanjay Agarwal, Sponsor: Vishwajay ChakravartyController: Happy Mukherjee MBB: Himadhri MukherjeeMeasure: 11/08/2007 Analyze: 12/08/2007 Improve: 1/08/2007 Control: 3/08/2008 Closeout: 3/08/2007Reduce the number of certifications caused due to system error in Time Attendance System. (project y)from an average of 82/month (current level)to less than 20/month (good level)for all TCL employees (specific area) Reduce the number of certifications caused due to system error in the Time Attendance System from an average of 82/month to less than 20/month Reduce the time spent in addressing and filing employee's attendance issues. Time reduction in correction of those errors. Will improve the overall process of Time Attendance System. Improve Employee morale. 3931) The Objective statement should be the same as that on the database. The Y, baseline and target should be stated.2) Benefits should mention a $ figure and where the $ came from3) Project Team should mention names and titles of core team4) Schedule should mention when each phase was completed

40Every presentation is expected to include your project tracker.Update it in Excel with your project progress then paste into your presentationDEFINEWhy this Project

What will be the savings

What is the base line

Why the Target

What is the scope6 Sigma StrategyGain insight into our current operational processeshow good is it (Baseline)?how good could it be (Entitlement)?what is limiting it from getting better?how can it be improved (Closing the Gap)?how much of the gap do we want to close during this project (Goal)?

Six Sigma is the process of quickly closing the gap between process baseline and entitlement.42BaselineCurrent processEntitlementPerfect world, best caseGoalWhat improvement goal makes sense for your project?

1050400350MonthRate1050706555MonthDefects per 100010500.210.200.190.180.170.160.150.14MonthCOPQHow is this plant doing?ScrapUnits per DayGoalActualBaselineDefects per 1000 unitsProcess Excellence: The Right Metrics43How is this plant doing?

Quality (DPU and Scrap) - Favorable trends on defects and scrap.Throughput rate - Favorable rate on productivity.

It is very important to have a Baseline for each metric. If you dont know where you have been, you dont know if you have really made any improvement.

Using Paretos to Select Projects

Stopcock TSIISafety Shield StopHousing 5mmKnife -500x100mmSpring, ResetInner GasketKnife Assembly 50 69 72 74 75128148 8.111.211.712.012.220.824.0100.0 91.9 80.7 69.0 57.0 44.8 24.06005004003002001000100806040200DefectCountPercentCum %PercentCountMajor Part Failure due to ProcessOthersAssemblyTechnicalProcess 34 164 1981328 2.0 9.511.577.0100.0 98.0 88.5 77.0180016001400120010008006004002000100806040200DefectCountPercentCum %PercentCountPareto Chart for CauseKnife Assembly and Inner Gasket are the two major process scrap drivers Two Potential Projects: - Knife Assembly - Inner GasketProcess Related Scrap is the Highest44Pareto is an important tool to aid project scoping. In this case, the belt determined which area was causing the most scrap and then the major defect types causing scrap. Stress the importance of narrowing the project to a manageable amount, especially for green belts.

TanksPaintMaterialsDefects per unit drives plant wide improvement

Process Steps, Defects, Units and DPU45Based upon the Quality measure DPU, these three process steps offer the most opportunity for improvement and should be considered first for Six Sigma projects.MeasureExample Process MapPaintingShell finish Paint typePrimer typeSolvent gradeThicknessHardnessRateCoverageSurface qualityProper schemeInputsOutputsManufacturingQuotingCust nameCust IDBill toShip toCredit statusTime to QuoteNumber of contactsQuote AccuracyInputsOutputsAdministrationThe C&E uses items from the process map. You will need the outputs from the macro process map. These outputs should relate back to your customer requirements, your project Y, and any constraints.PROCESS INPUTS, OUTPUTS AND FEEDBACK

Process StepInputsOutputsManMachineMethodsMother NatureManagementMaterialsMeasurement SystemProductsServicesClassify each input as:C = ControllableNC = Non ControllableProcess Map Example - ManufacturingSurface PrepMake minor repairs Sand surfaceClean SurfaceInspectPrimeAdjust air pressureLoad primerRecord primer lotPrime surfaceInspectPaintAdjust air pressureCheck nozzle typeInspect surfaceRecord paint lotPaint surfaceInspectDrySet oven profileInspect surfaceReworkOutputsOutputsClean surfaceSmooth surfacePrep timeDefect / repair countAir pressureComplete coverageSmooth surfacePrime timePrimer lotPrimer thicknessAir pressureComplete coverageSmooth surfacePaint timePaint lotPaint thicknessCorrect designDry paintSmooth surfaceDry timePaint hardnessDefect / repair countInputsTypeSurface contaminationSurface roughnessCab typeSanding gritAir pressureFiller lotSander RPMSurface contaminationSurface roughnessAir pressurePrimer lotNozzle typePrimer ageAmbient tempRelative humidityN

N

NCCNSN

N

CNCNNNInputsTypeSurface contaminationSurface roughnessAir pressurePaint lotNozzle typePaint ageAmbient tempRelative humidityPaint viscosityCab typeAir pressureAmbient tempRelative humidityOven profileThermocouple calibrationPaint thicknessN

N

CNCNNN

CCNNN

CS

NNOTE: bold denotes Key, Major, Critical Characteristics. They are Key Product or Process Output Variables (KPOVs)

Instructor: point out KPOV's on the slide.

Next StepsProcess Map

Cause and Effects Matrix

FMEA

Initial Assessment of Capabilities and Control PlansOUTPUTSINPUTS

Briefly review how the key tools of the DMAIC road map link together.

This slide is to give the students a brief look at how the information from the Process Map will be used further down the roadmap. Also to emphasize there should be a link between the tools used in DMAIC.

Dont spend much time on this. Just a brief overview/hint. If there are detailed questions, have the students hold them until the appropriate module.

We now start getting a feel for which variables are most important to explain outputs variationExampleReview the slide.Linking the C&E Matrix to Other Tools

C&E MatrixFMEA

Key Inputs exploredCapability Summary

Key Outputs listed and evaluatedOutputsControl Plan Summary

Key Inputs evaluatedInputsThe C&E tool is linked to the:

Process MapProcess FMEACapability StudyControl Plan

Review Questions :

1. (True/False) The purpose of the C&E matrix is to determine the relative importance of input variables, evaluated by their effects upon output variables.2. (True/False) Each key input is given a score using a scale of 1 to 10, depending on its effect upon the output.3. (True/False) Each key output is given a score using a scale of 1 to 10, depending on its importance to the customer.4. (True/False) Cross multiplication of the correlation scores and the output scores gives a measure of an inputs importance.5. (True/False) The focused approach to make a C&E matrix is NOT recommended, because it uses two steps while the general approach uses only one step.6. The results of the C&E Matrix are used by which of the following 6 Sigma tools: (a) control plan summary (b) potential failure mode and effects summary (c) capability summary (d) all of the aboveThe Funneling EffectCritical Input Variables30+ Inputs8 - 104 - 83 - 6Found Critical XsControlling Critical Xs10 - 15All Xs1st Hit ListScreened ListMEASUREANALYZEIMPROVECONTROLProcess MapsFailure Modes and Effects AnalysisMulti-Vari StudiesDesign of Experiments (DOE)Control PlansC&E MatrixSix Sigma is a narrowing down approach by which a search for the root cause of variation is found and then fixed.Analyze Hypothesis Testing

VOC & KJ

Regression Techniques

Normalty Test

ANOVA

Process Analysis Multi Vari Analysis

X DataSingle XMultiple Xs Y DataSingle Y Multiple Ys Multivariate Analysis(Note: This Is Not Multi-Vari Studies)X DataDiscrete Continuous Y DataDiscrete Continuous Chi-SquareLogistic Regressiont-test, ANOVAMeans / Medians TestsRegressionX DataDiscrete Continuous Y DataDiscrete Continuous MultipleRegressionLogistic RegressionMultiple Medians Tests2, 3, 4 way...ANOVALogistic RegressionMultiple Multi-vari Study

561) Clearly state the Xs you are investigating. These should match your FMEA key Xs. If you change the specification of the X, state the relationship to the original. Eg: FMEA key X may have been Tool (high level X), but your multi-vary investigates specific tool features like speed, feed, material etc (low level or measurable X). FMEA key X may have been form (high level X), but your multi-vary investigates specific features of the form like number of fields, date filled, completely filled etc. (low level or measurable X).

2) Clearly state the graphical or quantitative investigative technique used eg: pie chart, control chart, ANOVA, Chi-square, concentration chart etc

3) Clearly state the conclusion and what it was based on

4) If you put graphical or numerical output of the tools, put only what shows the bottom line. Eg: For ANOVA, only show the ANOVA table, not the normality tests, control charts, test for equal variance etc.

5) Use a summary slide for multi vary learningsStepDescriptionFocusDeliverableSample Tools0Project SelectionIdentify project CTQ's, develop team charter, define high-level process map1Select CTQ characteristicsYIdentify and measure customer CTQ'sCustomer, QFD, FMEA2Define Performance StandardsYDefine and confirm specifications for the YCustomer, blueprints3Measurement System AnalysisYMeasurement system is adequate to measure YContinuous Gage R&R, Test/Retest, Attribute R&R4Establish Process CapabilityYBaseline current process; normality testCapability indices5Define Performance ObjectivesYStatisicly define goal of projectTeam, benchmarking6Identify Variation SourcesXList of statistically significant X's based on analysis of historical dataProcess Analysis, Graphical analysis, hypothesis testing7Screen Potential CausesXDetermine vital few X's that cause changes to your YDOE-screening8Discover Variable RelationshipsXDetermine transfer function between Y and vital few X's; Determine optimal settings for vital few X's; Perform confirmation runsFactorial designs9Establish Operating TolerancesY, XSpecify tolerances on the vital few X'sSimulation10Define and Validate Measurement System on X's in actual applicationY, XMeasurement system is adequate to measure X'sContinuous Gage R&R, Test/Retest, Attribute R&R11Determine Process CapabilityY, XDetermine post improvement capability and performanceCapability indices12Implement Process ControlXDevelop and implement process control planControl charts, mistake proof, FMEASix Sigma 12 Step ProcessTool SummaryY'sContinuous DataAttribute DataContinuous DataRegressionScatter plotLogistic regressionTime series plotsMatrix PlotTime series plotGeneral Linear modelFitted lineC chartMulti-Vari plotStep wise RegressionP chartHistogramN chartDOENP chartBest SubsetsImRX'sX-bar RAttribute DataANOVAKruskal-WallisChi SquareBox plotsT-testParetoDot plotsLogistic RegressionMV plotHistogramDOEHomogeneity of varianceGeneral linear modelMatrix plot

ProcessModeling(Taguchi) StatisticalProcessControl(Deming) Pursuit of QualityInspect for Quality19301940195019801960197019902000Design forSix Sigma 601Left to right, we have moved from reactive to proactive/preventive approach to defects and variation over the last 70 years.

Appraisal Inspection for quality

SPC Process control for quality

Design of Experiments (DOE), Design for Six Sigma (DFSS) Quality defect prevention Product and process optimization

DIFF. B/W DMAIC AND DFSSDMAICDFSSDefines a business process.

Measuring current process

Identify root cause of the recurring PROBLEMS

Improvements made to reduce defects

Keep check on future performanceDefine customer needs

Measure customer needs & specification

Analyze options to meet customer satisfaction.

Model is deigned to meet customer needs

Model put through simulation tests for verification

61DMAICDFSSStructured and iterative process improvement methodologyRigorous approach to designing processes that will exceed customer expectationsFocus on defect reductionFocus on defect preventionDMAIC v/s DFSS

DefineDefine the problem.Define the customer(s) and the requirements.Define the current capability.Define the key processes that will have the greatest impact on customer.MeasureIdentify the statistical measures to monitor the key process.Set up the data collection plan.Measure the processAnalyzeDetermine the analysis tools and methods to be used.Summarize the data measured.Run the analysis and determine the root causes, effects, etc.ImproveImprove and Implement.Focus on developing process/technology to improve the root cause.Test the method on sample process and validate the improvement.ControlStandardize and document the process and implement the plan.Monitor the process and feedback the results back to the process for continuous improvement.DMAIC Methodology

IdentifyDevelop a team.Create team charter.Gather VOC.Perform competitive analysis.Develop CTQs and formally tie design to VOC.DesignIdentify functional requirements.Develop alternative concepts.Evaluate alternatives and select a best-fit concept.Deploy CTQs and predict sigma capability.OptimizeDetermine process capability.Develop detailed design elements.Predict performance.Optimize design.VerifyTest and validate the design.Share feedback with manufacturing and sourcing to improve future manufacturing and designs.DFSS Methodology

Six Sigma is not:A standardA certificationAnother metric like percentageRather!It is a Quality Philosophy and the way of improving performance by knowing where you are and where you could be.Methodology to measure and improve companys performance, practices and systems

65Six Sigma is a rigorous and a systematic methodology that utilizes information (management by facts) and statistical analysis to measure and improve a company's operational performance, practices and systems by identifying and preventing 'defects' in manufacturing and service-related processes in order to anticipate and exceed expectations of all stakeholders to accomplish effectiveness.65WHEN SHOULD SIX SIGMA BE USED?Its usage depends on the type of business. In general,

If there are processes that generate a lot of negative customer feedback, whether that customer is internal or external, the components of Six Sigma should be considered as a means to study and rectify the problem.66Lean Six Sigma

Lean aims at increasing speed by eliminating waste in the system. Six Sigma aims at reducing variations to improve process quality. Six Sigma to make processes predictable, processes require high process capability. Poor quality levels increase inventory. 10% scrap can slow down the process by 40%. Only Six Sigma approach may not be able to reduce cycle times or inventories. Defect reduction reduces cycle time but other non value adding activities like set up times, machine breakdowns, transportations etc; must be minimised. To increase process speed. Dramatic improvements are possible with Lean six Sigma implemented together.

Typical Tools used in Lean Six Sigma Projects

Six Sigma Roadmap Lean ToolsDefine Measure Analyze Improve ControlValue stream mappingTact Time

Process Flow Charts

Kan Ban Pull System

Set up time reductionTotal Productive MaintenanceProcess Cycle efficiencyTHANK YOUFrom :TEAM 169Sheet: Sheet1Sheet: Sheet2Sheet: Sheet3Sheet: Sheet4Sheet: Sheet5Sheet: Sheet6Sheet: Sheet7Sheet: Sheet8Sheet: Sheet9Sheet: Sheet10Sheet: Sheet11Sheet: Sheet12Sheet: Sheet13Sheet: Sheet14Sheet: Sheet15Sheet: Sheet16Product: White filmCore Team: White film teamDate (Orig):81295.0Key Contact: Marco GargurevichPhone: 908-558-5134Date (Rev):82295.0AProcessProcess StepInputOutputProcess Specification (Target, LSL, USL)Cpk Mean - SigmaMeasurement Technique%R&R P/TSample SizeSample FrequencyControl MethodReaction PlanWhitefilmCoating Dosage22.5, 22, 231.22UIL-17000.251/hrAuto-timerCross checkCoating Height24,23,251.54Micrometer31%/0.4735 pts per panel1/hrCoating & pump speedAdjust previousCoating width14,12,161.78Laser Measuring Device1/hrNone in placeCoating length36,34,381.43Laser Measuring Device1/hrNone in placeVacuum35" HgVacuum Gauge1/hrMonitorCompare guages, look for blockageCoatingCoating thickness18 , 17.5, 18.52.39, 1.19ATI beta guage480 pts per patchEvery patchCalibrationCorrelate to micrometerDancing0,0,1-0.45Leica 0-3 scale1/hrclean mask, monitor dosageCheck mask, check dosageDelamination0,0,10.3Visual 0-3 scale1/hrDev. time, vacuumClean vacuum grooves & systemDeveloperWash timeDelaminationsee above % flow rateStripesNo stripesN/AN/AN/A100% inspectionEvery patchAdjust wash time & flow rateSheet: Sheet1Sheet: Sheet2Sheet: Sheet3Sheet: Sheet4Sheet: Sheet5Sheet: Sheet6Sheet: Sheet7Sheet: Sheet8Sheet: Sheet9Sheet: Sheet10Sheet: Sheet11Sheet: Sheet12Sheet: Sheet13Sheet: Sheet14Sheet: Sheet15Sheet: Sheet16Customer Requirement (Output Variable)Measurement Technique%R&R or P/T RatioUpper Spec LimitTargetLower Spec LimitCpCpkSample SizeDateActionsGel TimeViscosityCleanlinessColorHomogeneityConsistencyDigets TimeTemperatureSolidsSheet: Batch OxidationInputTypeOutputWax gradeSOPPrep timeAmt waxContrPrepare ReactorAcid numberCharge rateContrViscosityAgit speedContrCharge melted waxReactor tempRxn tempContrBring to reaction tempTemp profilePressureContrHT coeffAir flowContrNoiseViscosityNoiseWax tempNoiseAN targetSOPOxid timeAgit speedContrOxidizeAcid numberTemperatureContrColorPressureContrPut in setpointsViscosityAir flowContrSample hourlyReactor tempNoiseMonitor acid numberTemp profileAir tempContrOffgas flowAir humidityNoiseOffgas compHT coeffResp timeInputTypeOutputAgit speedContrStab timeTemperatureContrStabilizeAcid numberPressureContrColorAir flowContrPut in setpointsViscosityNoiseSlowly reduce pressReactor tempAir tempContrMonitor tempTemp profileOffgas flowOffgas compHT coeffBelt speedContrFinish timeBelt tempContrFinishAcid numberFlow rateContrColorNozzle typeSOPCheck nozzle typeViscosityHole sizeContrPut in setpointsDrop PointRoom tempNoiseMonitor appearanceHardnessAgit speedContrPellet appearTank tempContrSheet: Batch OxidationInputTypeOutputWax gradeSOPPrep timeAmt waxContrPrepare ReactorAcid numberCharge rateContrViscosityAgit speedContrCharge melted waxReactor tempRxn tempContrBring to reaction tempTemp profilePressureContrHT coeffAir flowContrNoiseViscosityNoiseWax tempNoiseAN targetSOPOxid timeAgit speed *ContrOxidizeAcid numberTemperature *ContrColorPressure *ContrPut in setpointsViscosityAir flow *ContrSample hourlyReactor tempNoiseMonitor acid numberTemp profileAir tempContrOffgas flowAir humidityNoiseOffgas compHT coeffResp timeAgit speedContrStab timeTemperatureContrStabilizeAcid numberPressureContrColorAir flowContrPut in setpointsViscosityNoiseSlowly reduce pressReactor tempAir tempContrMonitor tempTemp profileOffgas flowOffgas compHT coeffBelt speedContrFinish timeBelt tempContrFinishAcid numberFlow rateContrColorNozzle typeSOPCheck nozzle typeViscosityHole sizeContrPut in setpointsDrop PointRoom tempNoiseMonitor appearanceHardnessAgit speedContrPellet appearTank tempContrSheet: Sheet2Sheet: Sheet1Sheet: Chart1Sheet: Sheet1 (2)Cause and Effect QuantificationEffects0.0Non-Effect1.02.03.04.05.0Time6.0Customer Receives Low Quantity On Time7.0Customer Receives Low Quantity Late8.0Customer Does Not Receive Product9.0Customer receives bad product , May return or pretreat10.0Customer receives bad product must return to ASQualityGoodTreatableUntreatableQuantityDesired LowNoneTimeOntimeLateNeverCause and Effect Matrix Ranking SystemCausesdetectablepreventable0.0Cause can not result in this effectyesyes1.0Deviation occurs but Does not effect unit outputyes2.0Deviation causes Unit output to change but little consequenceyes3.0Deviation to next unit, non-detection may cause problemyes4.0Deviation Preventable if detected, adjustment required to further unityes5.0Deviation preventable if detected shortly, no further unityes6.0Deviation Preventable if detected immedeatly, no further unityes7.0yes8.0yes9.0no10.0nonoRating of Importance to CustomerProcess StepProcess InputsHeavies in ProductLights in ProductMoisture in ProductAcidity in ProductLow Capacity From UnitExcessive DowntimeMaterial LossesCorrosion of EquipmentPoor Reactor PerformanceTotal1.0ReactorHF Feed3.0113.02.0ReactorOrganic Feed3.07.063.03.0ReactorCatalyst Feed3.0123.04.0ReactorChlorine Feed4.02.0100.05.0ReactorThionyl Feed68.06.0ReactorRXR Agitation3.0113.07.0ReactorRxr Temperature6.05.0149.08.0ReactorHF/Org Ratio6.03.0119.09.0ReactorCat./HF Ratio5.0157.010.0ReactorCatalyst Activity8.07.098.011.0ReactorEquipment Failure2.0104.012.00.013.0Catalyst StripperReflux Ratio3.04.03.076.014.0Catalyst StripperTemperature3.05.093.015.0Catalyst StripperPressure3.05.03.083.016.0Catalyst StripperPluggage137.017.0Catalyst StripperHigh DP3.03.039.018.0Catalyst StripperEquipment Failure2.099.019.00.020.0HCl AbsorberKOH Feed3.07.094.021.0HCl AbsorberBisulfite Feed2.03.046.022.0HCl AbsorberLow pH3.03.03.078.023.0HCl AbsorberLow Temperature3.015.024.0HCl AbsorberHigh Temperature2.02.02.050.025.0HCl AbsorberHigh DP2.04.03.060.026.0HCl AbsorberOrg Solubility3.05.046.027.0HCl AbsorberORP Value2.02.036.028.0HCl AbsorberDecomposition2.02.02.096.029.0HCl AbsorberLeaks2.02.030.030.0HCl AbsorberEquipment Failure74.031.00.032.0Waste TankCapacity2.014.033.00.034.0DrierMolecular Sieve125.035.0DrierNitrogen1.02.020.036.0DrierHeat29.037.0DrierMoisture2.060.038.0DrierPoor Capacity4.02.038.039.0DrierWater Carryover4.06.0132.040.0DrierLogic Failure30.041.0DrierLeaks2.010.042.0DrierDecomposition2.02.066.043.0DrierHold-up2.010.044.0DrierEquipment Failure26.045.00.046.0Storage2.014.047.00.048.0Heavies RemovalCooling3.03.02.01.067.049.0Heavies RemovalHeating3.03.02.01.067.050.0Heavies RemovalTemperature3.02.040.051.0Heavies RemovalPressure2.02.030.052.0Heavies RemovalReflux Ratio3.04.03.073.053.0Heavies RemovalPoor Yield4.03.043.054.0Heavies RemovalLeaks42.055.0Heavies RemovalHigh Flow3.04.02.068.056.0Heavies RemovalCondenser Leak2.04.098.057.0Heavies RemovalReboiler Leak2.04.098.058.0Heavies RemovalEquipment Failure4.07.0102.059.00.060.0Recycle Storage2.014.061.00.062.0Lights RemovalCooling3.02.01.01.055.063.0Lights RemovalHeating3.02.01.049.064.0Lights RemovalTemperature5.01.01.062.065.0Lights RemovalPressure5.01.057.066.0Lights RemovalReflux Ratio4.03.061.067.0Lights RemovalLow Temperature5.01.01.062.068.0Lights RemovalLow Reflux3.03.01.056.069.0Lights RemovalLow Stages4.02.01.059.070.0Lights RemovalPoor Yield3.02.031.071.0Lights RemovalLeaks2.010.072.0Lights RemovalDecomposition2.02.02.096.073.0Lights RemovalCondenser Leak4.0148.074.0Lights RemovalReboiler Leak4.0148.075.0Lights RemovalEquipment Failure74.076.00.077.0PhotochlorinationChlorine Feed2.02.01.01.02.068.078.0PhotochlorinationUV-Light5.03.01.076.079.0PhotochlorinationCooling2.016.080.0PhotochlorinationHigh Unsaturates2.084.081.0PhotochlorinationTemperature1.02.024.082.0PhotochlorinationResidence Time2.03.041.083.0PhotochlorinationLow Chlorine6.060.084.0PhotochlorinationLow Light6.060.085.0PhotochlorinationLow Time6.060.086.0PhotochlorinationHigh Chlorine3.02.02.02.076.087.0PhotochlorinationHigh Light3.02.02.056.088.0PhotochlorinationWet Material3.03.051.089.0PhotochlorinationBad Kinetics4.028.090.0PhotochlorinationBroken Lamp7.056.091.0PhotochlorinationVenting5.025.092.0Photochlorination245fa Reaction3.02.02.056.093.0PhotochlorinationLeaks1.02.018.094.0PhotochlorinationEquipment Failure7.035.095.00.096.0NeutralizationKOH Feed6.03.078.097.0NeutralizationWater Feed0.098.0NeutralizationBisulfite Feed3.024.099.0NeutralizationAgitation3.02.044.0100.0NeutralizationpH Value6.06.03.0138.0101.0NeutralizationORP Value3.03.054.0102.0NeutralizationLeaky Seal2.02.036.0103.0NeutralizationDecomposition2.090.0104.0NeutralizationLow Seal Pressure2.010.0105.0NeutralizationOrg. Solubility2.010.0106.0NeutralizationLow Temperature2.010.0107.0NeutralizationEquipment Failure3.015.0108.00.0109.0DryingMolecular Sieve38.0110.0DryingMoisture Load4.065.0111.0DryingDecomposition3.02.02.0134.0112.0DryingHigh Flow4.028.0113.0DryingFlux Limitation5.035.0114.0DryingPlugged Filter3.021.0115.0DryingLeaks3.015.0116.0DryingSieve Holdup3.015.0117.0DryingEquipment Failure4.020.0118.00.0119.0StorageContamination2.095.0120.0StorageAnalysis75.0121.0StorageLeaks3.015.0122.0StorageEquipment Failure3.015.0123.00.0124.0PurificationCooling5.02.064.0125.0PurificationHeating5.02.064.0126.0PurificationTemperature5.050.0127.0PurificationPressure5.050.0128.0PurificationReflux Ratio5.03.071.0129.0PurificationHigh Temperature5.050.0130.0PurificationLow Reflux5.050.0131.0PurificationLow Stages8.08.0144.0132.0PurificationHigh Flow5.050.0133.0PurificationDecomposition0.0134.0PurificationCondenser Leak6.06.090.0135.0PurificationReboiler Leak6.06.090.0136.0PurificationLeaks63.0137.0PurificationEquipment Failure79.0138.00.0139.0Day TanksAnalysis335.0140.0Day TanksContamination81.0141.0Day TanksLeaks5.025.0142.0Day TanksEquipment Failure8.040.0143.00.0144.0Final StorageContainers140.0145.0Final StorageEquipment Failure0.0146.00.0147.00.0148.00.00.00.0Total1200.01160.0504.0552.00.01120.01136.0755.00.01140.0582.00.00.00.00.0AnalysisDay TanksCat./HF RatioReactorRxr TemperatureReactorCondenser LeakLights RemovalReboiler LeakLights RemovalLow StagesPurificationContainersFinal StoragepH ValueNeutralizationPluggageCatalyst StripperDecompositionDryingWater CarryoverDrierMolecular SieveDrierCatalyst FeedReactorHF/Org RatioReactorHF FeedReactorRXR AgitationReactorEquipment FailureReactorEquipment FailureHeavies RemovalChlorine FeedReactor335.0157.0149.0148.0148.0144.0140.0138.0137.0134.0132.0125.0123.0119.0113.0113.0104.0102.0100.0Rating of Importance to CustomerProcess StepProcess InputsHeavies in ProductLights in ProductMoisture in ProductAcidity in ProductLow Capacity From UnitExcessive DowntimeMaterial LossesCorrosion of EquipmentPoor Reactor PerformanceTotal139.0Day TanksAnalysis335.09.0ReactorCat./HF Ratio5.0157.07.0ReactorRxr Temperature6.05.0149.073.0Lights RemovalCondenser Leak4.0148.074.0Lights RemovalReboiler Leak4.0148.0131.0PurificationLow Stages8.08.0144.0144.0Final StorageContainers140.0100.0NeutralizationpH Value6.06.03.0138.016.0Catalyst StripperPluggage137.0111.0DryingDecomposition3.02.02.0134.039.0DrierWater Carryover4.06.0132.034.0DrierMolecular Sieve125.03.0ReactorCatalyst Feed3.0123.08.0ReactorHF/Org Ratio6.03.0119.01.0ReactorHF Feed3.0113.06.0ReactorRXR Agitation3.0113.011.0ReactorEquipment Failure2.0104.058.0Heavies RemovalEquipment Failure4.07.0102.04.0ReactorChlorine Feed4.02.0100.018.0Catalyst StripperEquipment Failure2.099.010.0ReactorCatalyst Activity8.07.098.056.0Heavies RemovalCondenser Leak2.04.098.057.0Heavies RemovalReboiler Leak2.04.098.028.0HCl AbsorberDecomposition2.02.02.096.072.0Lights RemovalDecomposition2.02.02.096.0119.0StorageContamination2.095.020.0HCl AbsorberKOH Feed3.07.094.014.0Catalyst StripperTemperature3.05.093.0103.0NeutralizationDecomposition2.090.0134.0PurificationCondenser Leak6.06.090.0135.0PurificationReboiler Leak6.06.090.080.0PhotochlorinationHigh Unsaturates2.084.015.0Catalyst StripperPressure3.05.03.083.0140.0Day TanksContamination81.0137.0PurificationEquipment Failure79.022.0HCl AbsorberLow pH3.03.03.078.096.0NeutralizationKOH Feed6.03.078.013.0Catalyst StripperReflux Ratio3.04.03.076.078.0PhotochlorinationUV-Light5.03.01.076.086.0PhotochlorinationHigh Chlorine3.02.02.02.076.0120.0StorageAnalysis75.030.0HCl AbsorberEquipment Failure74.075.0Lights RemovalEquipment Failure74.052.0Heavies RemovalReflux Ratio3.04.03.073.0128.0PurificationReflux Ratio5.03.071.05.0ReactorThionyl Feed68.055.0Heavies RemovalHigh Flow3.04.02.068.077.0PhotochlorinationChlorine Feed2.02.01.01.02.068.048.0Heavies RemovalCooling3.03.02.01.067.049.0Heavies RemovalHeating3.03.02.01.067.042.0DrierDecomposition2.02.066.0110.0DryingMoisture Load4.065.0124.0PurificationCooling5.02.064.0125.0PurificationHeating5.02.064.02.0ReactorOrganic Feed3.07.063.0136.0PurificationLeaks63.064.0Lights RemovalTemperature5.01.01.062.067.0Lights RemovalLow Temperature5.01.01.062.066.0Lights RemovalReflux Ratio4.03.061.025.0HCl AbsorberHigh DP2.04.03.060.037.0DrierMoisture2.060.083.0PhotochlorinationLow Chlorine6.060.084.0PhotochlorinationLow Light6.060.085.0PhotochlorinationLow Time6.060.069.0Lights RemovalLow Stages4.02.01.059.065.0Lights RemovalPressure5.01.057.068.0Lights RemovalLow Reflux3.03.01.056.087.0PhotochlorinationHigh Light3.02.02.056.090.0PhotochlorinationBroken Lamp7.056.092.0Photochlorination245fa Reaction3.02.02.056.062.0Lights RemovalCooling3.02.01.01.055.0101.0NeutralizationORP Value3.03.054.088.0PhotochlorinationWet Material3.03.051.024.0HCl AbsorberHigh Temperature2.02.02.050.0126.0PurificationTemperature5.050.0127.0PurificationPressure5.050.0129.0PurificationHigh Temperature5.050.0130.0PurificationLow Reflux5.050.0132.0PurificationHigh Flow5.050.063.0Lights RemovalHeating3.02.01.049.021.0HCl AbsorberBisulfite Feed2.03.046.026.0HCl AbsorberOrg Solubility3.05.046.099.0NeutralizationAgitation3.02.044.053.0Heavies RemovalPoor Yield4.03.043.054.0Heavies RemovalLeaks42.082.0PhotochlorinationResidence Time2.03.041.050.0Heavies RemovalTemperature3.02.040.0142.0Day TanksEquipment Failure8.040.017.0Catalyst StripperHigh DP3.03.039.038.0DrierPoor Capacity4.02.038.0109.0DryingMolecular Sieve38.027.0HCl AbsorberORP Value2.02.036.0102.0NeutralizationLeaky Seal2.02.036.094.0PhotochlorinationEquipment Failure7.035.0113.0DryingFlux Limitation5.035.070.0Lights RemovalPoor Yield3.02.031.029.0HCl AbsorberLeaks2.02.030.040.0DrierLogic Failure30.051.0Heavies RemovalPressure2.02.030.036.0DrierHeat29.089.0PhotochlorinationBad Kinetics4.028.0112.0DryingHigh Flow4.028.044.0DrierEquipment Failure26.091.0PhotochlorinationVenting5.025.0141.0Day TanksLeaks5.025.081.0PhotochlorinationTemperature1.02.024.098.0NeutralizationBisulfite Feed3.024.0114.0DryingPlugged Filter3.021.035.0DrierNitrogen1.02.020.0117.0DryingEquipment Failure4.020.093.0PhotochlorinationLeaks1.02.018.079.0PhotochlorinationCooling2.016.023.0HCl AbsorberLow Temperature3.015.0107.0NeutralizationEquipment Failure3.015.0115.0DryingLeaks3.015.0116.0DryingSieve Holdup3.015.0121.0StorageLeaks3.015.0122.0StorageEquipment Failure3.015.032.0Waste TankCapacity2.014.046.0Storage2.014.060.0Recycle Storage2.014.041.0DrierLeaks2.010.043.0DrierHold-up2.010.071.0Lights RemovalLeaks2.010.0104.0NeutralizationLow Seal Pressure2.010.0105.0NeutralizationOrg. Solubility2.010.0106.0NeutralizationLow Temperature2.010.012.00.019.00.031.00.033.00.045.00.047.00.059.00.061.00.076.00.095.00.097.0NeutralizationWater Feed0.0108.00.0118.00.0123.00.0133.0PurificationDecomposition0.0138.00.0143.00.0145.0Final StorageEquipment Failure0.0146.00.0147.00.0148.00.00.00.0Total1200.01160.0504.0552.00.01120.01136.0755.00.01140.0582.00.00.00.00.0Sheet: fmea1of3Sheet: fmea2of3Sheet: fmea3of3Sheet: WFPARETO2Sheet: CAUSESProcess or Product Name:WHITE FILM PROCESS Prepared by:Marco GargurevichPage 1 of 3Responsible:WHITE FILM TEAMFMEA Date (Orig) June 3, 1995 (Rev) 08/21/95BProcess Step/Part NumberPotential Failure ModePotential Failure EffectsSEVPotential CausesOCCCurrent ControlsDETRPNActions RecommendedResp.Actions TakenSEVOCCDETRPNWhat is the process step under investigation?What do you see in the factory that tells you the cause has occurred?What is the impact on the Key Output Variables (Customer Requirements)?How Severe is the effect to the cusotmer?what are the potential ways the Key Inputs can vary from desired (Out-of-spec)? Start with Key Inputs from C&E Matrix.How often does cause or FM occur?What are the existing controls and procedures (inspection and test) that prevent either the cause or the Failure Mode?How well can you detect cause or FM?What are the actions for reducing the occurrance of the Cause, or improving detection?What are the completed actions taken with the recalculated RPN?COATING & IMAGINGDIRTY PHOTOMASKMICROCRACKING, DELAMINATION, STREAKS8.0LOW FREQUENCY OF CLEANING8.0SOP, VISUAL INSPECTION7.0448.0INCREASE FREQUENCY TO ONCE EVERY 20 PANELSMGIMPLEMENTATION OF ON-LINE CLEANING METHOD392.0IMPROVE CLEANING METHODPFIMPLEMENTATION OF OFF-LINE CLEANING METHOD USING DI WATER288.0PURCHASE OFF-LINE CLEANING SYSTEMMGSYSTEMS UNDER EVALUATIONTEST ON-LINE MASK REPLACEMENTPFPRELIMINARY TESTING PERFORMED8.0CONTAMINATION INTRODUCED TO PHOTOMASK8.0NONE IN PLACE8.0512.0MAINTAIN STATIC ELIMINATOR, WEB CLEAN ONRLIN PLACE448.0CLEAN PET TESTMGON HOLDIN-LINE IPA FILTERSDIN PROGRESSELIMINATE IPA CONTAMINATIONKB,SDSAMPLING IN PROGRESS TO IDENTIFY SOURCES392.0OVEREXPOSUREHUGGING & DANCING (I.E., POOR CONE ALIGNMENT)9.0HIGH INTENSITY AND/OR HIGH EXPOSURE TIME8.0HOURLY INTENSITY READING5.0360.0PROGRAM TO CALCULATE EXPOSURE TIMEPFDONE, IN PROGRESSREAD INTENSITY AT CENTER OF MASKMG, JNIN PROGRESS, SOP UPDATE REQUIRED288.0PET MOTIONSTRING CHEESE DELAMINATION9.0NOT ENOUGH VACUUM9.0INSUFFICIENT CONTROLS9.0729.0INSTALL SUPPLEMENTAL VACUUMMGELNIK PUMP INSTALLED245.0VACUUM FRAME RETROFITSDVACUUM FIXTURE INSTALLED WITH INDICATOR168.0Process or Product Name:WHITE FILM PROCESS Prepared by:Marco GargurevichPage 2 of 3Responsible:WHITE FILM TEAMFMEA Date (Orig) June 3, 1995 (Rev) 08/21/95BProcess Step/Part NumberPotential Failure ModePotential Failure EffectsSEVPotential CausesOCCCurrent ControlsDETRPNActions RecommendedResp.Actions TakenSEVOCCDETRPNWhat is the process step under investigation?What do you see in the factory that tells you the cause has occurred?What is the impact on the Key Output Variables (Customer Requirements)?How Severe is the effect to the cusotmer?what are the potential ways the Key Inputs can vary from desired (Out-of-spec)? Start with Key Inputs from C&E Matrix.How often does cause or FM occur?What are the existing controls and procedures (inspection and test) that prevent eith the cause or the Failure Mode?How well can you detect cause or FM?What are the actions for reducing the occurrance of the Cause, or improving detection?What are the completed actions taken with the recalculated RPN?COATING & IMAGINGPET MOTIONSTRING CHEESE DELAMINATION9.0MACHINE VIBRATION5.0PERIODIC CHECK8.0360.0CALL IN OUTSIDE SUPPORT TO MEASURE VIBRATIONJNCOMPLETED, REPORT SUBMITTED9.0IPA BUILD-UP IN VACUUM LINES7.0PERIODIC CHECK FOLLOWED BY P.M.6.0378.0iNSTALL IPA TRAP PRIOR TO VACUUMSDVPARTS ON ORDER9.0TOO MUCH IPA, NOT ENOUGH TENSION5.0CHECKLISTS AND SOP'S5.0225.0TENSION, IPA DOEMGCOMPLETED, DOE0089.0UNWIND OSCILLATION8.0NONE8.0576.0VERIFY HYPOTHESISPF/MGCOMPLETED, NOT FOUND TO BE SIGNIFICANT8.0POOR COAT UNIFORMITYBUBBLES, SPOTS, BANDING8.0IMPROPER COATING PARAMETERS (ROLL GAP, DIE SPEED, ETC...)8.0CHECKLISTS AND SOP'S4.0256.0RUN COAT DOEPF/MD/SDPARAMETERS OPTIMIZED FOR 90 SECOND CYCLE TIME192.00.0COATING ASSIST FROM LIBERTYSDCONSULTANTS IN 07/24, REPORT DUE8.0COAT PARAMETERS PLUS ALL OTHER (TENSION, LEVEL, ETC.)8.0ATI ON LINE AND MITUTOYO OFF LINE7.0448.0CHANGE ATI PROGRAMPFCOMPLETED 08/01256.00.0COMPUTERIZE MITUTOYONSCOMPLETED 07/17256.00.0LEVEL RAILSSDCOMPLETED 07/178.0INADEQUATE DELIVERY SYSTEM5.0VISUAL INSPECTION PLUS ATI3.0120.0MODIFY DELIVERY SYSTEMSD1/2" MONOMER LINE INSTALLED 07/220.0DUAL SLOT COATCKIN PROGRESS, ON HOLD 0.0PUMP PLUNGER MATERIALSDSOLID PLUNGER INSTALLED, UNDER EVALUATION96.0Process or Product Name:WHITE FILM PROCESS Prepared by:Marco GargurevichPage 3 of 3Responsible:WHITE FILM TEAMFMEA Date (Orig) June 3, 1995 (Rev) 08/21/95BProcess Step/Part NumberPotential Failure ModePotential Failure EffectsSEVPotential CausesOCCCurrent ControlsDETRPNActions RecommendedResp.Actions TakenSEVOCCDETRPNWhat is the process step under investigation?What do you see in the factory that tells you the cause has occurred?What is the impact on the Key Output Variables (Customer Requirements)?How Severe is the effect to the cusotmer?what are the potential ways the Key Inputs can vary from desired (Out-of-spec)? Start with Key Inputs from C&E Matrix.How often does cause or FM occur?What are the existing controls and procedures (inspection and test) that prevent eith the cause or the Failure Mode?How well can you detect cause or FM?What are the actions for reducing the occurrance of the Cause, or improving detection?What are the completed actions taken with the recalculated RPN?DEVELOPERUNDER DEVELOPED SCREENSFLOWBAR MARKS, STREAKS, SWIRLS9.0FLOWBAR POSITION9.0ON-LINE VISUAL INSPECTION6.0486.0SINGLE STROKE FLOWBAR RETROFITDPRETROFIT SCHEDULED PENDING DOE MULTIPLE SPRAY BAR RETROFITDPSPRAY BARS INSTALLED & TESTEDMICRO-JET/DRIP PAN RETROFITDPMICRO-JETS INSTALLED DOE'S IN WORK FOR PARAMETER OPTIMIZATION378.0OFFSET FLOWBAR TO REDUCE EFFECTDPON HOLD9.0NOT ENOUGH WASH TIME9.0UNABLE TO CONTROL WASH TIME DUE TO DELAMINATION8.0648.0DEVELOPMENT TIME DOERLPROCESS LATTITUDE GAINED WITH VACUUM UPGRADESEE DELAMINATION ACTIONS9.0DEVELOPER TEMPERATURE5.0PLC CONTROLLED5.0225.0HEATED MEOH DOEDP, PF, MGDOE'S COMPLETED RESULTS PUBLISHED9.0HIGH MONOMER CONTENT5.0REPLENISHMENT RATE, DAILY SAMPLING8.0360.0INCREASE REPLENISHMENT RATEDPUNDER EVALUATIONINCREASE DUMPING FREQUENCYON HOLDMONITORING OF MONOMER LOADINGNSSPC PLAN TO BE STARTEDLOW VACUUMINSTALL PUMPRETROFITLOW WASH TIMEUNWIND OSCILLATIONNOT VALIDMASK CONTAMINATIONWEB CLEANERFLOWBAR POSITIONDRIP PANCLEANING FREQUENCYINCREASE FREQUENCYIMPROVE METHODTENSION, LEVELCOMPUTER INSPECTIONIPA BUILD-UP IN VACUUM LINESMACHINE VIBRATIONHIGH MONOMER CONTENTROLL GAP, DIE SPEEDOPTIMIZEDHI IPA LOW TENSIONDEVELOPER TEMPERATUREMONOMER DELIVERYPUMP MODIFICATION729.0245.0168.0648.0576.08.0512.0448.0486.0378.0448.0392.0288.0448.0256.0378.0360.0360.0256.0192.0225.0225.0120.096.0NOT ENOUGH VACUUM729.0ELNIK PUMP INSTALLED245.0LOW VACUUM729.0INSTALL PUMP245.0RETROFIT168.0ELNIK PUMP INSTALLED245.0LOW WASH TIME648.0VACUUM FRAME INSTALLED 168.0UNWIND OSCILLATION576.0NOT VALID8.0NOT ENOUGH WASH TIME648.0VACUUM GAGE INSTALLED 168.0MASK CONTAMINATION512.0WEB CLEANER448.0UNWIND OSCILLATION576.0NOT SIGNIFICANT8.0FLOWBAR POSITION486.0DRIP PAN378.0NOT SIGNIFICANT8.0CLEANING FREQUENCY448.0INCREASE FREQUENCY392.0IMPROVE METHOD288.0FLOWBAR POSITION486.0ATI UPGRADED320.0TENSION, LEVEL448.0COMPUTER INSPECTION256.0COAT UNIFORMITY448.0LEVELED RAILS256.0IPA BUILD-UP IN VACUUM LINES378.0ATI UPGRADED320.0MACHINE VIBRATION360.0LEVELED RAILS256.0HIGH MONOMER CONTENT360.0LOW FREQUENCY OF CLEANING448.0CLEANING EVERY 20 PANELS392.0ROLL GAP, DIE SPEED256.0OPTIMIZED192.0CLEANING EVERY 20 PANELS392.0HI IPA LOW TENSION225.0HIGH MONOMER CONTENT360.0DEVELOPER TEMPERATURE225.0MONOMER DELIVERY120.0MACHINE VIBRATION360.0PUMP MODIFICATION96.0IMPROPER COATING PARAMETERS (ROLL GAP, DIE SPEED, ETC...)256.0DEVELOPER TEMPERATURE225.0TOO MUCH IPA, NOT ENOUGH TENSION225.0INADEQUATE DELIVERY SYSTEM120.0392.0Sheet: fmea1of3Sheet: fmea2of3Sheet: fmea3of3Sheet: WFPARETO2Sheet: CAUSESProcess or Product Name:WHITE FILM PROCESS Prepared by:Marco GargurevichPage 1 of 3Responsible:WHITE FILM TEAMFMEA Date (Orig) June 3, 1995 (Rev) 08/21/95BProcess Step/Part NumberPotential Failure ModePotential Failure EffectsSEVPotential CausesOCCCurrent ControlsDETRPNActions RecommendedResp.Actions TakenSEVOCCDETRPNWhat is the process step under investigation?What do you see in the factory that tells you the cause has occurred?What is the impact on the Key Output Variables (Customer Requirements)?How Severe is the effect to the cusotmer?what are the potential ways the Key Inputs can vary from desired (Out-of-spec)? Start with Key Inputs from C&E Matrix.How often does cause or FM occur?What are the existing controls and procedures (inspection and test) that prevent either the cause or the Failure Mode?How well can you detect cause or FM?What are the actions for reducing the occurrance of the Cause, or improving detection?What are the completed actions taken with the recalculated RPN?COATING & IMAGINGDIRTY PHOTOMASKMICROCRACKING, DELAMINATION, STREAKS8.0LOW FREQUENCY OF CLEANING8.0SOP, VISUAL INSPECTION7.0448.0INCREASE FREQUENCY TO ONCE EVERY 20 PANELSMGIMPLEMENTATION OF ON-LINE CLEANING METHOD392.0IMPROVE CLEANING METHODPFIMPLEMENTATION OF OFF-LINE CLEANING METHOD USING DI WATER288.0PURCHASE OFF-LINE CLEANING SYSTEMMGSYSTEMS UNDER EVALUATIONTEST ON-LINE MASK REPLACEMENTPFPRELIMINARY TESTING PERFORMED8.0CONTAMINATION INTRODUCED TO PHOTOMASK8.0NONE IN PLACE8.0512.0MAINTAIN STATIC ELIMINATOR, WEB CLEAN ONRLIN PLACE448.0CLEAN PET TESTMGON HOLDIN-LINE IPA FILTERSDIN PROGRESSELIMINATE IPA CONTAMINATIONKB,SDSAMPLING IN PROGRESS TO IDENTIFY SOURCES392.0OVEREXPOSUREHUGGING & DANCING (I.E., POOR CONE ALIGNMENT)9.0HIGH INTENSITY AND/OR HIGH EXPOSURE TIME8.0HOURLY INTENSITY READING5.0360.0PROGRAM TO CALCULATE EXPOSURE TIMEPFDONE, IN PROGRESSREAD INTENSITY AT CENTER OF MASKMG, JNIN PROGRESS, SOP UPDATE REQUIRED288.0PET MOTIONSTRING CHEESE DELAMINATION9.0NOT ENOUGH VACUUM9.0INSUFFICIENT CONTROLS9.0729.0INSTALL SUPPLEMENTAL VACUUMMGELNIK PUMP INSTALLED245.0VACUUM FRAME RETROFITSDVACUUM FIXTURE INSTALLED WITH INDICATOR168.0Process or Product Name:WHITE FILM PROCESS Prepared by:Marco GargurevichPage 2 of 3Responsible:WHITE FILM TEAMFMEA Date (Orig) June 3, 1995 (Rev) 08/21/95BProcess Step/Part NumberPotential Failure ModePotential Failure EffectsSEVPotential CausesOCCCurrent ControlsDETRPNActions RecommendedResp.Actions TakenSEVOCCDETRPNWhat is the process step under investigation?What do you see in the factory that tells you the cause has occurred?What is the impact on the Key Output Variables (Customer Requirements)?How Severe is the effect to the cusotmer?what are the potential ways the Key Inputs can vary from desired (Out-of-spec)? Start with Key Inputs from C&E Matrix.How often does cause or FM occur?What are the existing controls and procedures (inspection and test) that prevent eith the cause or the Failure Mode?How well can you detect cause or FM?What are the actions for reducing the occurrance of the Cause, or improving detection?What are the completed actions taken with the recalculated RPN?COATING & IMAGINGPET MOTIONSTRING CHEESE DELAMINATION9.0MACHINE VIBRATION5.0PERIODIC CHECK8.0360.0CALL IN OUTSIDE SUPPORT TO MEASURE VIBRATIONJNCOMPLETED, REPORT SUBMITTED9.0IPA BUILD-UP IN VACUUM LINES7.0PERIODIC CHECK FOLLOWED BY P.M.6.0378.0iNSTALL IPA TRAP PRIOR TO VACUUMSDVPARTS ON ORDER9.0TOO MUCH IPA, NOT ENOUGH TENSION5.0CHECKLISTS AND SOP'S5.0225.0TENSION, IPA DOEMGCOMPLETED, DOE0089.0UNWIND OSCILLATION8.0NONE8.0576.0VERIFY HYPOTHESISPF/MGCOMPLETED, NOT FOUND TO BE SIGNIFICANT8.0POOR COAT UNIFORMITYBUBBLES, SPOTS, BANDING8.0IMPROPER COATING PARAMETERS (ROLL GAP, DIE SPEED, ETC...)8.0CHECKLISTS AND SOP'S4.0256.0RUN COAT DOEPF/MD/SDPARAMETERS OPTIMIZED FOR 90 SECOND CYCLE TIME192.00.0COATING ASSIST FROM LIBERTYSDCONSULTANTS IN 07/24, REPORT DUE8.0COAT PARAMETERS PLUS ALL OTHER (TENSION, LEVEL, ETC.)8.0ATI ON LINE AND MITUTOYO OFF LINE7.0448.0CHANGE ATI PROGRAMPFCOMPLETED 08/01256.00.0COMPUTERIZE MITUTOYONSCOMPLETED 07/17256.00.0LEVEL RAILSSDCOMPLETED 07/178.0INADEQUATE DELIVERY SYSTEM5.0VISUAL INSPECTION PLUS ATI3.0120.0MODIFY DELIVERY SYSTEMSD1/2" MONOMER LINE INSTALLED 07/220.0DUAL SLOT COATCKIN PROGRESS, ON HOLD 0.0PUMP PLUNGER MATERIALSDSOLID PLUNGER INSTALLED, UNDER EVALUATION96.0Process or Product Name:WHITE FILM PROCESS Prepared by:Marco GargurevichPage 3 of 3Responsible:WHITE FILM TEAMFMEA Date (Orig) June 3, 1995 (Rev) 08/21/95BProcess Step/Part NumberPotential Failure ModePotential Failure EffectsSEVPotential CausesOCCCurrent ControlsDETRPNActions RecommendedResp.Actions TakenSEVOCCDETRPNWhat is the process step under investigation?What do you see in the factory that tells you the cause has occurred?What is the impact on the Key Output Variables (Customer Requirements)?How Severe is the effect to the cusotmer?what are the potential ways the Key Inputs can vary from desired (Out-of-spec)? Start with Key Inputs from C&E Matrix.How often does cause or FM occur?What are the existing controls and procedures (inspection and test) that prevent eith the cause or the Failure Mode?How well can you detect cause or FM?What are the actions for reducing the occurrance of the Cause, or improving detection?What are the completed actions taken with the recalculated RPN?DEVELOPERUNDER DEVELOPED SCREENSFLOWBAR MARKS, STREAKS, SWIRLS9.0FLOWBAR POSITION9.0ON-LINE VISUAL INSPECTION6.0486.0SINGLE STROKE FLOWBAR RETROFITDPRETROFIT SCHEDULED PENDING DOE MULTIPLE SPRAY BAR RETROFITDPSPRAY BARS INSTALLED & TESTEDMICRO-JET/DRIP PAN RETROFITDPMICRO-JETS INSTALLED DOE'S IN WORK FOR PARAMETER OPTIMIZATION378.0OFFSET FLOWBAR TO REDUCE EFFECTDPON HOLD9.0NOT ENOUGH WASH TIME9.0UNABLE TO CONTROL WASH TIME DUE TO DELAMINATION8.0648.0DEVELOPMENT TIME DOERLPROCESS LATTITUDE GAINED WITH VACUUM UPGRADESEE DELAMINATION ACTIONS9.0DEVELOPER TEMPERATURE5.0PLC CONTROLLED5.0225.0HEATED MEOH DOEDP, PF, MGDOE'S COMPLETED RESULTS PUBLISHED9.0HIGH MONOMER CONTENT5.0REPLENISHMENT RATE, DAILY SAMPLING8.0360.0INCREASE REPLENISHMENT RATEDPUNDER EVALUATIONINCREASE DUMPING FREQUENCYON HOLDMONITORING OF MONOMER LOADINGNSSPC PLAN TO BE STARTEDLOW VACUUMINSTALL PUMPRETROFITLOW WASH TIMEUNWIND OSCILLATIONNOT VALIDMASK CONTAMINATIONWEB CLEANERFLOWBAR POSITIONDRIP PANCLEANING FREQUENCYINCREASE FREQUENCYIMPROVE METHODTENSION, LEVELCOMPUTER INSPECTIONIPA BUILD-UP IN VACUUM LINESMACHINE VIBRATIONHIGH MONOMER CONTENTROLL GAP, DIE SPEEDOPTIMIZEDHI IPA LOW TENSIONDEVELOPER TEMPERATUREMONOMER DELIVERYPUMP MODIFICATION729.0245.0168.0648.0576.08.0512.0448.0486.0378.0448.0392.0288.0448.0256.0378.0360.0360.0256.0192.0225.0225.0120.096.0NOT ENOUGH VACUUM729.0ELNIK PUMP INSTALLED245.0LOW VACUUM729.0INSTALL PUMP245.0RETROFIT168.0ELNIK PUMP INSTALLED245.0LOW WASH TIME648.0VACUUM FRAME INSTALLED 168.0UNWIND OSCILLATION576.0NOT VALID8.0NOT ENOUGH WASH TIME648.0VACUUM GAGE INSTALLED 168.0MASK CONTAMINATION512.0WEB CLEANER448.0UNWIND OSCILLATION576.0NOT SIGNIFICANT8.0FLOWBAR POSITION486.0DRIP PAN378.0NOT SIGNIFICANT8.0CLEANING FREQUENCY448.0INCREASE FREQUENCY392.0IMPROVE METHOD288.0FLOWBAR POSITION486.0ATI UPGRADED320.0TENSION, LEVEL448.0COMPUTER INSPECTION256.0COAT UNIFORMITY448.0LEVELED RAILS256.0IPA BUILD-UP IN VACUUM LINES378.0ATI UPGRADED320.0MACHINE VIBRATION360.0LEVELED RAILS256.0HIGH MONOMER CONTENT360.0LOW FREQUENCY OF CLEANING448.0CLEANING EVERY 20 PANELS392.0ROLL GAP, DIE SPEED256.0OPTIMIZED192.0CLEANING EVERY 20 PANELS392.0HI IPA LOW TENSION225.0HIGH MONOMER CONTENT360.0DEVELOPER TEMPERATURE225.0MONOMER DELIVERY120.0MACHINE VIBRATION360.0PUMP MODIFICATION96.0IMPROPER COATING PARAMETERS (ROLL GAP, DIE SPEED, ETC...)256.0DEVELOPER TEMPERATURE225.0TOO MUCH IPA, NOT ENOUGH TENSION225.0INADEQUATE DELIVERY SYSTEM120.0392.0Bldg. 75 Pelletizer Process MapProcess Map Building 75 PelletizingProceduresControlsMeasureSpecsInputsProcess StepOutputsSpecsMeasureControlsNote: C=continuous, I=intermittant, N=noneCCTarget +/-2SpeedPumpingPoly RateNCCNOnSeal WaterPoly Temp.NNCC265 +/-2Dow Temp.Poly Press.CNNNGear WearICTarget 36 FAVPolymer Visc.Southern MetalsCI20 Mic. Rand.Filter TypeFiltrationPolymer Temp.NNSouthern MetalsII>5 on bubble testFilter PorosityPolymer PressureCNICFilter FoulingGelsMax 7IICC265+/-10Dow. Temp.DirtMax 1IIII260 ft-lbJackbolt TorqueCC265 +/- 20Die Heater TemperatureExtrusionStrand rateNNIIDie Type (# holes)Strand diameterNNIIDie foulingNNSpin. SprayNC>900Mono Exhaust RateIISweep Steam RateIIInjection Steam RateNNPosition of exhaust chuteCCPolymer rateIIMono Exhaust foulingIIString up water flowString-upStrand rateNNCCQuench water pressureStrand diameterNNIIOverflow water flow(pellet diameter)NNAlignment of Table to DieGelsMax 7CCPolymer rateDirtMax 1NNBlob detector sensitivityIISpray nozzle waterQuenchingStrand TemperatureNNIISpray nozzle foulingCC18.5+/-2QW Temp.NNQW % LactamNNHumidityNNQW % DimerIIBed knife conditionCuttingPellet weight1000+/-150IIIIBed knife toleranceDefects1.0 MaxIIIIFeed roll beltPellet shapeIIIIFeed roll wipe blade conditionPellet LengthIIIIFeed roll conditionIIPressure roll conditionIIPressure roll alignmentIC25 - 32Pressure roll pressureIIAlignment of pelletizerIIBearing conditionsCC110 - 210Cutter SpeedNNCutter wearIC70 +/- 5Instr.Air Press.IIFeed roll angle to bed knifeICTransport water rateTransportPellet LengthIIIITransport chamber foulingPellet ShapeIIIIInDischarge ScreenChip defect1.0 Maxsample 901Chip Wt.1000+/-150IIChip visc.sample 900

&APage &P

Building 75 pelletizer C & ERating of Importance to Customer79410810123456Paint thicknessPaint hardnessRateCoverageSurface qualityProper schemeTotalProcess StepProcess InputPrimeSurface contamination133990208PrimeSurface roughness013390123PrimeAir pressure919990270PrimeLot number130313102PrimeNozzle type903990237PrimePrimer age999390282PrimeAmbient temp13903094PrimeRelative humidity199190206PaintSurface contamination193991272PaintSurface roughness313393174PaintAir pressure939993318PaintLot number010319137PaintNozzle type909999351PaintPaint age999390282PaintAmbient temp199090196PaintPaint viscosity999933324

Cause and Effect Matrix Building 75 Pelletizing

Solid State Process MapSOLID STATE PROCESS MAP(B135ZP)ProceduresControlsMeasureSpecsInputsProcess StepOutputsSpecsMeasureControlsNote: C=continuous, I=intermittant, N=noneCCFeeder SpeedCentrifuge% Chip H20NCIISG N2 injection rateChip TemperatureCNCC70 +/- 5Eductor water pressureICSC seal N2 flowIC5+/-1SC seal N2 pressureNCoffwater spray pressureNCoffwater spray intervalNCoffwater spray durationNNonCentrifuge speedNCTemperatureCCGas TempPre-DryChip % H20IICCGas FlowChip Temp stage 1CNICInner Ring Gas FlowChip Temp stage 2CNICOuter Ring Gas FlowChip Temp stage 3CNICO2 ppmChip Temp stage 4CNCCWet Bulb TempChip FAVNNNNGas Ring FoulingCCLevelCCFeeder RateCCPressureNN1.05-1.25 g/100Chip sizeIIChip H20%CCGas TempFinal DryChip Temp stage 5CNCCGas FlowChip Temp stage 6CNICUpper ring flowChip Temp stage 7CNIClower ring flowChip Temp stage 8CNIC