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Six Sigma and/For Software Engineering

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  • 1. CSN2501 RTSE PresentationSix Sigma and/for Software Engineering Anshuman Biswal PT 2012 Batch, Reg. No.: CJB0412001 M. Sc. (Engg.) in Computer Science and Networking Module Leader: N. D. Gangadhar Module Name: Real-Time Software Engineering Module Code : ESD2525 M. S. Ramaiah School of Advanced Studies 1
  • 2. Marking Head Maximum ScoreTechnical Content 10Grasp and Understanding 10Delivery – Technical and 10General AspectsHandling Questions 10 Total 40 M. S. Ramaiah School of Advanced Studies 2
  • 3. Presentation Outline• What is Six Sigma ?• Evolution of Concepts behind Six Sigma• Six Sigma at Motorola• History of Six Sigma• Statistics behind Six Sigma• What is Six Sigma Performance?• Relationship between Sigma Level and defect• Six Sigma methodologies• Define• Measure• Analyze• Improve• Control• Conclusion• References M. S. Ramaiah School of Advanced Studies 3
  • 4. What is Six Sigma A Metric ? A APhilosophy What is Methodology ? Six Sigma ? A Management System M. S. Ramaiah School of Advanced Studies 4
  • 5. Six Sigma- A MetricA metric that encourages measurements of process performance. Sigma is the Greek letter representing a statistical unit of measurement that defines the standard deviation of a population. It measures the variability or spread of the data. 6 sigma is also the measure of variability. It’s a name given to indicate how much of the data falls within the customers requirements. The higher the process sigma, the more of the process outputs, products and services, meets customers requirements – or fewer the defects The term sigma is often used as the scale for levels of “goodness” or quality. Using this scale, “ Six Sigma” equates to 3.4 defects per million opportunities (DPMO). M. S. Ramaiah School of Advanced Studies 5
  • 6. Six Sigma – A business methodology A methodology that focuses on the following Utilizing Driving rapidUnderstandin Aligning key rigorous data andg and business analysis to sustainablemanaging processes to minimize improvementcustomer achieve those variation in to businessrequirements requirements those processes processes M. S. Ramaiah School of Advanced Studies 6
  • 7. Six Sigma – A management systemSix Sigma is management system for executing business strategy. Six Sigma is asolution to help organizations to:Align their Govern efforts tobusiness strategy Mobilize teams Accelerate ensureto critical to attack high improved business improvements areimprovement impact projects results sustainedefforts M. S. Ramaiah School of Advanced Studies 7
  • 8. Six Sigma – A Philosophy Business ExcellenceInferential Statistics Customer Focus Transition fromBasic statistical intuition towardsanalysis inferential statistics in decision makingBasic Charts and First time right atGraphs source Zero defectBrainstorming Tools Extra ordinary processes that deliverIntuition or Gut predictable resultsfeeling M. S. Ramaiah School of Advanced Studies 8
  • 9. Evolution of concepts behind Six Sigma FMEA was formally introduced in 1940’s for military usage by US armed forces. Later it was used for aerospace/rocket Ronal Fisher development. Example of introduced Design this is Apollo space In 1920’s , Walter A of Experiment program. The primaryCarl Friedrich Shewart showed that push came during 1960’s 3 sigma, from the through a book inGauss (1777-1855) ,while developing means mean is the point 1935. This was aintroduced the to put a man on the moon where a process result of series of and return him backconcept of Normal requires correction. studies that started safely. In 1970 FordCurve This finally led to with study of introduced FMEA in Control Charts variation in crop automotive industry yieldAutomotive Industry Action Group ( AIAG) published the most accepted document onMeasurement Systems Analysis (MSA). MSA is an essential step in 6 sigma methodologiesand is used to ensure reliability of data. M. S. Ramaiah School of Advanced Studies 9
  • 10. Six Sigma at MotorolaIn the late 1970’s Dr. Mikel Harry, a senior staff engineer at Motorola’sGovernment Electronics Group ( GEG),experimented problem solving throughstatistical analysis. Using this approach, GEG’s product were being designed andproduced at a faster rate and at lower cost.Subsequently Dr Harry began too formulate a method for applying six sigmathrough out Motorola. M. S. Ramaiah School of Advanced Studies 10
  • 11. Six Sigma at MotorolaIn 1987 when Bob Galvin was the chairman, Six Sigma was started as amethodology in Motorola.Bill Smith, an engineer and Dr. Mikel Harry together devised a 6 stepmethodology with the focus on defect reduction and improvement in yieldthrough statistics. M. S. Ramaiah School of Advanced Studies 11
  • 12. Six Sigma at MotorolaThe term Six Sigma was coined by Bill Smith, who is now calledthe Father of Six Sigma. Terms such as Black belt and Green belt were coined by MikelHarry in relation to martial artsUsing this methodology the company saved $16 billion in 10years M. S. Ramaiah School of Advanced Studies 12
  • 13. History of Six Sigma1987 Motorola Strength in Manufacturing1994 Allied Signal Linked to Financial Returns General Electric Linked to Design and Service1996 3M / Phillips Linked to Sales and Product Commercialization Sony, Seagate, Raytheon, Toshiba and many other2000 companies 2006 Motorola Begins Lean Transformation Uses Lean concepts in 6 Sigma 2008 Motorola methodologies and termed it as Lean 6 sigma M. S. Ramaiah School of Advanced Studies 13
  • 14. Statistics behind Six Sigma pizza santas pizza hut 21 17  If two pizza delivery outlets have 9 18 same average delivery time of 20 11 21 minutes, against the promised 12 22 29 23 delivery time of 30 minutes then 14 16 would you say that they are equally 28 24 good? 26 23 20 19 24 18 22 21 23 16 23 21 7 19 29 22average 19.8666667 20 M. S. Ramaiah School of Advanced Studies 14
  • 15. Statistics behind Six Sigma santas pizza pizza hut 21 17 9 18  To comment on which one is 11 21 better would you like to consider 12 22 variation in delivery time? If yes 29 23 how would you like to measure 14 16 28 24 variation? 26 23 20 19 24 18 22 21 23 16 23 21 7 19 29 22 2.618614standard deviation 7.42454103 7 M. S. Ramaiah School of Advanced Studies 15
  • 16. Statistics behind Six Sigma santas pizza pizza hut 21 17 9 18 11 21  Can we find a single measure for 12 22 process performance that 29 23 14 16 incorporates the central tendency, 28 24 variation and specification limits? 26 23 20 19 24 18 22 21 23 16 23 21 7 19 29 22 average 19.8666667 20standard deviation 7.42454103 2.6186147Upper Specification Limit 30 30sigma level 1.36484306 3.8188131Probability 91.384881% 99.99330% M. S. Ramaiah School of Advanced Studies 16
  • 17. What is Six Sigma performance ? Your process is performing at sigma level of six if the differencebetween the mean and the specification limit is six times the standarddeviation. To get a six sigma performance the variation should be as small aspossible so that we can fit six standard deviations between the meanand the USL. M. S. Ramaiah School of Advanced Studies 17
  • 18. Sigma level vs. DefectDPMO = [ D ( N * O ) ] * 1 Million Sigma DPMO 2 308770where: 2.25 226716– D = total number of defects 2.5 158687counted in the sample – 2.75 105660 3 66811a defect is defined as failure to meet 3.25 40060a Critical Customer 3.5 22750Requirement or CCR 3.75 12225– N = number of units of product or 4 6210 4.25 2980service 4.5 1350– O = number of opportunities per 4.75 577unit of product or service for 5 233 5.25 88a customer defect to occur 5.5 32– M = million 5.75 11 6 3.4 M. S. Ramaiah School of Advanced Studies 18
  • 19. Six Sigma methodologies DMAIC Define – Measure – Analyze – Improve – ControlStructured and repeated process improvement methodologyFocuses on Defect reductionImproves existing products and processes DMADV Define – Measure – Analyze – Define – Verify Strict approach to design so as to exceed customer expectations Focuses on preventing errors and defects Develop new product/process or redesign existing product/process M. S. Ramaiah School of Advanced Studies 19
  • 20. Six Sigma methodologies DMAIC Define – Measure – Analyze – Improve – ControlStructured and repeated process improvement methodologyFocuses on Defect reductionImproves existing products and processes DMADV Define – Measure – Analyze – Define – Verify Strict approach to design so as to exceed customer expectations Focuses on preventing errors and defects Develop new product/process or redesign existing product/process M. S. Ramaiah School of Advanced Studies 20
  • 21. DMAIC and Problem Solving Flow Define Measure Improve Control AnalyzeOpportunities Performance Performance Performance Opportunity (Establish (Characterize (Modify) (Ensure (Decompose)Requirements) Performance) Consistency) Practical Quantitative Quantifiable Practical Problem Problem Analysis Conclusion Conclusion statement StatementKey Questions to Ask How do we What is How are we What is What needs to guarantee important? doing? wrong? be done? performance? M. S. Ramaiah School of Advanced Studies 21
  • 22. Define – The improvement Opportunity Select the Define and Develop the Map the improvement scope the team process opportunity project charter•Business leaders identify Six Sigma projects with Master BlackBelts supporting the project portfolio process.• Black Belts, Green Belts and Six Sigma Teams with Sponsors &Champions are other sources of projects in the portfolio.• A continuous review of Scorecard Goals, dashboard metricresults, and audit results suggest possible projects/opportunities forImprovement. M. S. Ramaiah School of Advanced Studies 22
  • 23. Define – The improvement Opportunity Select the Define and Develop the Map the improvement scope the team process opportunity project charterTranslating Voice of the Customer (VOCs) toCritical Customer Requirements (CCRs)Often the voices of customers (VOC) are not clear and not intechnical language. Therefore VOC needs to be translated to theCritical Customer Requirements (CCR) which can be translatedinto the technical requirements known as Critical to Quality (CTQ),and Critical to Process (CTP)Tools: QFD/ House of QualityInput Process Output Measures M. S. Ramaiah School of Advanced Studies 23
  • 24. Define – The improvement Opportunity Example of Input Process Output measures CTQ CCR CUSTOMER ISSUES VOC Defect 1> Defect back log Defect back log Higher Reduction needs to be cleared by increased so more staff number of June 2012. count were needed defects 2> Not more than 100 and also some defects found in XXXVOC: Voice Of Customer defects in XXXX r3.0 were pushed to next R1.0CCR: Critical Customer Requirement 3> Try to attain virtual release byCTQ: Critical To Quality zero goal at the end of compromising with the release of the certain features that product. got pushed to next release BUSINESS VOB ISSUES CBR CTP Quality of To clear the Staff count can not Minimize product back log increase which can the rework needs to be more staff increase cost cost byimproved i.e. counts reducing Defects needed and Identify defects in early the defects Backlog more effort phase before build going by 80% needs to be needs to be to BT cleared put. without Reduce number of defects increasing in Some ,so it reduces the rework staff count features had cost to be pushed to next release. VOB: Voice Of Business CBR: Critical Business Requirement CTP: Critical To Process M. S. Ramaiah School of Advanced Studies 24
  • 25. Define – The improvement Opportunity Select the Define and Develop the Map the improvement scope the team process opportunity project charterTeam Charter is a business document that communicates leadershipexpectations, expected business outcomes and personal relevancyof the project in order to focus and motivate team.Purpose of the Team Charter• Communicate leadership expectations, expected businessoutcomes and personal relevancy of the project in order to motivateand focus team.• Clarify the scope, resources and deliverables of improvementproject. M. S. Ramaiah School of Advanced Studies 25
  • 26. Define – The improvement OpportunityProject Title: XXX R3.0 Defect Reduction Business Case Opportunity StatementThe number of defects ,raised by the Box Test team in the XXX R1.0 is In the past seven months ( April 2011 to Novemberon the higher side resulting in massive rework and need of more 2011), which was the XXX R1.0 phase had a total of 532resource working to clear the backlog. This accounts to USD 618K of BT SR out of which UI SR were 169, which wascosts and out of which UI defects accounts to USD 196K. We need to approximately 80% greater than expected.reduce the number of defects by at least 80% in XXX R3.0 which will Improvement Opportunity is targeted at the front endhelp us to achieve virtual zero goal for future releases phase of the UI Dev ( Req., Design, Code and Unit test phases ) to achieve defect reduction from 169 to 32. Estimated Savings = USD 37K( Cost per defect taken as USD 1162). So a total of USD 138K can be saved if the UI defects is controlled to 32 numbers or less in future releases. Goal statement Project scopeReduce the BT defects against UI development in XXX R3.0 at least This project will involve review of main reasons for UIby 80% by November 2012. development bugs raised during Box test. It will not examine phase after the box test like system tests and also it will not examine the bugs raised in the stack and middle ware development. Project plan Team SelectionPhase Start End Remarks Sponsor: Mr. XDefine 4/30/2012 5/18/2012 Champion: Mr. YMeasure 5/21/2012 7/27/2012 MBB: Mr ZAnalyze 7/30/ 2012 9/28/2012 MBB : Mr. A Black Belt Mentor : Mr. BImprove 10/1/2012 10/31/2012 Project Manager: Mr. C Project Lead: Mr. DControl 11/1/2012 11/30/2012 GB Candidate : Mr. E Team Members: Mr. F M. S. Ramaiah School of Advanced Studies 26
  • 27. Define – The improvement Opportunity Select the Define and Develop the Map the improvement scope the team process opportunity project charterProcess MappingPurpose– Understand the relationships among inputs and outputs of aprocess– Identify non-value added activities so they can be eliminatedTypes of Process Maps– SIPOC– Top-down chart– Functional Deployment Chart– Value Stream Mapping– Spaghetti Diagram M. S. Ramaiah School of Advanced Studies 27
  • 28. Define – The improvement OpportunityExample of SIPOC- Development Process SUPPLIER 1. INPUT OUTPUT CUSTOMERS 1. PDM Raw Requirement s Development Process CAB file Marketing Team Release 1. Notes Box Test Team 1. L3 Requirement Development Design Team Documents SUB PROCESSES Requirement Design Coding Testing (BT) Release Analysis Analysis Start Boundary: End Boundary: Requirement Gathering by PDM CAB file delivered to BT team M. S. Ramaiah School of Advanced Studies 28
  • 29. Define – The improvement OpportunityTOP DOWN CHART Start Boundary: End Boundary: Requirement Gathering by PDM CAB file delivered to BT team Process: Software Development Requirement Design Coding Testing (BT) Release Analysis Analysis Design Write source code Collect architecture Develop test casesrequirements from Write comments for Perform impact each source code Release test results field, customer, competitor analysis as per standards Develop test scripts product, external Prepare design Achieve in clear and automation demos doc case Perform code Prepare L3 Collect internal reviews document Review test cases and customer defects Review and Perform Unit Testing Raise defects if any Update L3 Req to fix in next build Perform Tests Review and Perform integration Update Testing Release L3 req.requirement spec, and Design doc Review test results UB Spec Prepare build M. S. Ramaiah School of Advanced Studies 29
  • 30. Define – The improvement Opportunity Key Take Away INPUTS PROCESS OUTPUTS • Select the• Strategic priorities • Team charter improvement• Scorecard development • Project Plan opportunity• Core process selections • Prepared team • Define and scope the• Improvement • Critical to customer projectexpectations requirements • Develop the team• Improvement project • Process Map (As-Is) charterteam sponsor, • Risk and mitigation plan • Map the processchampion, team leader • Quick win opportunities • Identify quick wins M. S. Ramaiah School of Advanced Studies 30
  • 31. Measure – Measure The Process Performance Create Collect EstablishSelect Define MSA- MSA- Measu and BaselineMeasur Measu Variabl Attribu rement validat performan es res e Data te data plan e Data ce Example of Input Process Output Indicators Process Output Input Indicators Indicators Indicators Number of cust omer Number of FTR comment s on requirement s Document reviews Reduce t he number of SR Number of change requirement Availabilit y of resources Cust omer sat isfact ion Lines of Code Types of int ernal defect s Reduce t he Rework effort s Number of BT Srs raised in R1.0 Project Schedule R1.0 in each module Planned Effort Act ual Effort s Number of design review comment s Number of code review comment s Act ual size of each module Number of int ernal Defect s M. S. Ramaiah School of Advanced Studies 31
  • 32. Measure – Measure The Process PerformanceCause and Effect Diagram M. S. Ramaiah School of Advanced Studies 32
  • 33. Measure – Measure The Process Performance Cause and Effect MatrixHigh priority indicators for analysisa. Number of internal defectsb. Types of internal defectsc. Number of change requirementsd. Number of BT Sr’s M. S. Ramaiah School of Advanced Studies 33
  • 34. Measure – Measure The Process Performance Create Collect EstablishSelect Define MSA- MSA- Measu and BaselineMeasur Measu Variabl Attribu rement validat performan es res e Data te data plan e Data ceOnce the team knows what to measure, they need to further definethe measurement.• This definition is called an operational definition.( A operational definition is a precise description ofWhat ? The specific criteria used for measurementHow? The methodology to collect the dataHow much? The amount of data to be collectedWho? Who has responsibility to collect data )• The operation definition helps assure the data is:– Collected and measured in a consistent way– Representative of the process M. S. Ramaiah School of Advanced Studies 34
  • 35. Measure – Measure The Process Performance Create Collect EstablishSelect Define MSA- MSA- Measu and BaselineMeasur Measu Variabl Attribu rement validat performan es res e Data te data plan e Data ce•Determining current process performance usually requires the collection of data.When developing a measurement plan ensure that:– The data collected is meaningful– The data collected is valid– All relevant data is collected concurrently• Before data collection starts, classify the data into different types: continuous ordiscrete. This is important because it will:– Provide a choice of data display and analysis tools– Dictate sample size calculation– Provide performance or cause information– Determine the appropriate control chart to use– Determine the appropriate method for calculation of Sigma Level M. S. Ramaiah School of Advanced Studies 35
  • 36. Measure – Measure The Process PerformanceMeasurement Plan M. S. Ramaiah School of Advanced Studies 36
  • 37. Measure – Measure The Process Performance Create Collect EstablishSelect Define MSA- MSA- Measu and BaselineMeasur Measu Variabl Attribu rement validat performan es res e Data te data plan e Data ceWhat is MSA?The study of the extent to which systematic and random factors are affecting ourability to correctly measure some phenomenon.When MSA is done?Before data collection. For variable data use Gage R and R test, Bias and Linearity Test andRepeatability and reproducibility testFor attribute data use Kappa analysis M. S. Ramaiah School of Advanced Studies 37
  • 38. Measure – Measure The Process Performance Create Collect EstablishSelect Define MSA- MSA- Measu and BaselineMeasur Measu Variabl Attribu rement validat performan es res e Data te data plan e Data ceP value is 0.095 which is greater than 0.05 hence we conclude thatdata is normal M. S. Ramaiah School of Advanced Studies 38
  • 39. Measure – Measure The Process Performance Create Collect EstablishSelect Define MSA- MSA- Measu and BaselineMeasur Measu Variabl Attribu rement validat performan es res e Data te data plan e Data ceWhat is Process Performance?• Process performance is defined as the ability of a process toproduce outputs that meet engineering and/or customerspecifications.• For continuous data we measure the process capability bycalculating Cp and Cpk•For attributes data we calculate the process capability by DPMOmethod M. S. Ramaiah School of Advanced Studies 39
  • 40. Measure – Measure The Process PerformanceProcess capability analysis M. S. Ramaiah School of Advanced Studies 40
  • 41. Measure – Measure The Process PerformanceDPMO – For attribute data Total number Defects- UI D 283 SR’s caught internally in R1.0 in different phases( code review, Unit Test, Design) Number of Units N 11 processed- Modules Number of defect O 164 opportunities- Test cases DPMO = 1M *[D/(N*O)] = 1000000*[283/(11*164)] = 156874 Current Sigma level = 2.51 M. S. Ramaiah School of Advanced Studies 41
  • 42. Measure – Measure The Process Performance Key Take Away INPUTS PROCESS OUTPUTS• Team charter •Select Measures • Input, Output, and• Project Plan •Define Measures Process Measures• Prepared team •Create Measurement Plan • Operational Definitions• Critical to customer •Conduct MSA Collect and Validate Data • Data collection planrequirements •Establish Baseline • Capable measurement• Process Map (As-Is) Performance system• Risk and mitigation plan • Baseline Performance• Quick win opportunities M. S. Ramaiah School of Advanced Studies 42
  • 43. Analyze – Analyze the Opportunity Validate Root Map the process Focus on Vital Few Cause•During the measurement phase, the team learned about the criticalx’s and collected measurements on these x’s.• During the analysis phase, we need to understand the root cause(s)of the variation affecting the critical x’s. – Requires a more detailed level of investigation.• A detailed process map helps the team identify all the x’s and usethis as a guide to get to root cause(s). M. S. Ramaiah School of Advanced Studies 43
  • 44. Analyze – Analyze the OpportunityA detailed process map example M. S. Ramaiah School of Advanced Studies 44
  • 45. Analyze – Analyze the OpportunityA detailed process map example M. S. Ramaiah School of Advanced Studies 45
  • 46. Analyze – Analyze the OpportunityA detailed process map example M. S. Ramaiah School of Advanced Studies 46
  • 47. Analyze – Analyze the OpportunityA detailed process map example M. S. Ramaiah School of Advanced Studies 47
  • 48. Analyze – Analyze the Opportunity Validate Root Map the process Focus on Vital Few CauseDetermine critical inputs– Cause and Effect Matrix– 5 Why’s– FMEA• Stratify data and narrow the focus– Pareto– Sources of Variation• Validate root cause statistically– Comparative Methods– Regression– DOE M. S. Ramaiah School of Advanced Studies 48
  • 49. Analyze – Analyze the OpportunityPareto Chart M. S. Ramaiah School of Advanced Studies 49
  • 50. Analyze – Analyze the Opportunity5 why M. S. Ramaiah School of Advanced Studies 50
  • 51. Analyze – Analyze the Opportunity Validate RootMap the process Focus on Vital Few CauseComparative methods M. S. Ramaiah School of Advanced Studies 51
  • 52. Analyze – Analyze the OpportunityRegression Analysis : Scatter Plot interpretation M. S. Ramaiah School of Advanced Studies 52
  • 53. Analyze – Analyze the OpportunityRegression Analysis : Residual Plot M. S. Ramaiah School of Advanced Studies 53
  • 54. Analyze – Analyze the OpportunityRegression Analysis : Residual Plot Normality plot for the residuals. Points (residuals) falling along a reasonably straight line indicates that residuals are normally distributed. • Residuals versus fitted values showing no pattern, funnelling out, curvature, etc. indicating constant variance assumption is not violated. A curvature in this plot indicates that the model is not good and higher orders in the model may be needed. A funnelling out indicates variation is not constant and needs a transformation (square root, log, etc.) • Histogram is skewed to the left. However, this is not uncommon with a small number of observations. As long as the other three charts look okay, the skewing is probably acceptable. • Residuals versus order of the observation showing no pattern, funnelling out, curvature, etc. No obvious violation of the independence assumption. M. S. Ramaiah School of Advanced Studies 54
  • 55. Analyze – Analyze the OpportunityRegression Analysis M. S. Ramaiah School of Advanced Studies 55
  • 56. Analyze – Analyze the Opportunity Key Take Away INPUTS PROCESS OUTPUTS• Input, Output, andProcess Measures • Develop Detailed Process • Data analysis• Operational Definitions Map • Process FMEA • Determine critical inputs• Data collection plan • Stratify the data • Sources of Variation• Capable measurement • Identify and Validate • Standardized Worksystem Root Causes • Validated root causes• Baseline Performance M. S. Ramaiah School of Advanced Studies 56
  • 57. Improve – Improve Performance Identify and Full scale Select Best implementation SolutionBrain StormingAFFINITY DiagramSolution Mapping M. S. Ramaiah School of Advanced Studies 57
  • 58. Improve – Improve Performance Identify and Full scale Select Best implementation SolutionBrainstorming : Problem1 : Long Approval TimeSuggestions from Brainstorming Session:Use informal meeting procedure than formal for review meetingNot all test cases should be given for review to Test leadPeer review should be encouragedDefine review frequency and time for final approvalUse review tool to monitor and track review comments statusAutomatic assignment of test cases on completion using review toolIndependent reviews by test leadCommunicate review time to customer and hence include in project planProvide training on using review tool and to perform reviewsImprove review checklists and guidelinesApprove test cases in batch and not all at onceCommunicate product critical review results to customerReview tool should email review results on review completionImprove Review tool interface for user friendly M. S. Ramaiah School of Advanced Studies 58
  • 59. Improve – Improve PerformanceAffinity Diagram: Problem1 : LongApproval Time M. S. Ramaiah School of Advanced Studies 59
  • 60. Improve – Improve PerformanceAffinity Diagram: Problem1 : LongApproval Time M. S. Ramaiah School of Advanced Studies 60
  • 61. Improve – Improve PerformanceSolution Mapping M. S. Ramaiah School of Advanced Studies 61
  • 62. Improve – Improve PerformanceSolution Selection Matrix M. S. Ramaiah School of Advanced Studies 62
  • 63. Improve – Improve Performance Identify and Full scale Select Best implementation Solution using the best solutions change the process and draw the newprocess map Do a pilot solution planRun the project and collect data again.Calculate the capability index and sigma level (DPMO) of thecurrent process. Nevertheless the current process should be betterthan the previous process. Else do Analyze phase again and find foranother set of root causes and repeat the improve phase. Keep onrepeating this step until the new process is not improved . M. S. Ramaiah School of Advanced Studies 63
  • 64. Improve – Improve Performance Key Take Away INPUTS PROCESS OUTPUTS• Data analysis • Solutions• Process FMEA • Identify and Select the • Process maps and• Sources of Variation Best Solution documentation• Standardized Work • Plan for Full-Scale • Cost/benefit analysis• Validated root causes Implementation • Improvement impacts and benefits M. S. Ramaiah School of Advanced Studies 64
  • 65. Control – Control Performance Develop Evaluate Transfer integrated project Ownership control plan resultsA Control Plan documents the data needed to accurately measure theprocess on an ongoing basis and react to out of control processes.• Based on the work performed during the improve phase, your organizationimplemented your team’s recommendations and verified the impact they actuallymade on the process you targeted.• At this point, the objective is to sustain the gains (i.e., to make sure that thechanges you helped to make become an integral part of the way your organizationoperates).• This is done by making sure we have a thorough control plan, which shouldaddress four different areas: error proof, measure and monitor, communicate, anddocument M. S. Ramaiah School of Advanced Studies 65
  • 66. Control – Control Performance Develop Evaluate Transfer integrated project Ownershipcontrol plan results M. S. Ramaiah School of Advanced Studies 66
  • 67. Control – Control Performance Develop Evaluate Transfer integrated project Ownership control plan resultsChecklist for Transition to Original Owner(s)• Has the original owner been made aware of the changes and the needed controlsto maintain the process?• How will the owner monitor these improvements over time?• Is there a recommended audit plan for follow-up by the team / experts to assurethat the process is still being maintained?• Have employees effected by the change been trained?• Did we have a good change plan?• Has the change been embraced by the owner and employees effected by theprocess? M. S. Ramaiah School of Advanced Studies 67
  • 68. Control – Control Performance Key Take Away INPUTS PROCESS OUTPUTS •Develop a Detailed•Solution process maps and •Process Control Plan Control Plandocumentation •Updated Standardized •Document final•Cost/benefit analysis Work implementation results•Improvement impacts and •Project Final Report / •Transfer ownershipbenefits Closure back to M. S. Ramaiah School of Advanced Studies 68
  • 69. Conclusion• What is Six Sigma?• History of Six Sigma?• Methodologies of Six Sigma?• DMAIC process with examples from the real case studies M. S. Ramaiah School of Advanced Studies 69
  • 70. References[1] Pyzdek,T., The Six Sigma Hand Book, Revised and Expanded,New Delhi:McGraw-Hill[2] CIC2000: Six Sigma Green Belt Training, Motorola University[3] Benbow,W.D.,Kubiak,T.M.,The Certified Six Sigma Blackbelt Handbook,2005,USA,ASQ Quality Press M. S. Ramaiah School of Advanced Studies 70