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SixSigma and CMMI Everything You Wanted to Know, But Didn’t Know Who to Ask

Dive into Six Sigma methodology and its connection to CMMI within 90 minutes. Learn how to apply Six Sigma tools, reduce variation, and improve processes for greater efficiency and customer satisfaction.

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SixSigma and CMMI Everything You Wanted to Know, But Didn’t Know Who to Ask

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  1. SixSigmaand CMMI Everything You Wanted to Know, But Didn’t Know Who to Ask Tom Lienhard Six Sigma BlackBelt

  2. Today’s Mission I have 90 Minutes BlackBelt training is 160 hrs Intro to CMMI is 24 hrs • QUICK Introduction to Six Sigma • Relationship of Six Sigma to the CMMI • Applying Six Sigma • Examples of Six Sigma Tool Usage to Improve

  3. What Do You Think Six SigmaIs All About? Benchmark? Metric? Method? Philosophy? Vision? Pipe Dream? Disease? Stretch Goal?

  4. What Is Six Sigma? A customer-driven management methodology to significantly increase customer satisfaction through reducing and eliminating variation and thus defects. A concept developed by Motorola to achieve a desired level of quality, virtually defect-free products and processes With this definition, six sigma is applicable to any development or manufacturing process

  5. The Many Facets of Sigma Sigmais a letter in the Greek alphabet “Sigma” is used to designate the distribution about the mean of any process or product characteristic (standard deviation) “Sigma value” indicates how often defects are likely to occur “Sigma level” is the number of standard deviations between the process center and the CLOSEST spec limit

  6. Premise for Six Sigma • Sources of variationcan be • Identified • Quantified • Mitigated by control or prevention • The elements of a process responsible for variation are called the “6 M’s” • Man (people) • Machine • Material • Method • Measurement • Mother Nature

  7. Variation is the Enemy • Variation exists in everything • If it is assumed that everything is a result of some process, then it is safe to conclude that the process introduces product variation • Variation in the product is due to • variation in the process • Variation comes in two forms - • common and special cause We Must Slay the Enemy

  8. Common and Special Causes Common causes Special causes 1 1 1 0 3 . 0 S L = 9 .2 . 9 8 X = 7 .4 7 6 - 3 . 0 S L = 5. .6 5 4 3 2 1 2 3 4 5 6 7 8 • Common Causes • Continuously active in the process • Predictable • Special Cause • Extraordinary events • Can assign a reason • Can do something about it The control limits allow predictions about the future to be made Control limits reflect the built in or natural process variation Control limits are not related to standards or specification

  9. Reading a Control Chart Tests for out-of-control conditions in a control chart One point more than three sigmas from the mean Two out of three points more than 2 sigmas from the mean 1 5 Nine points in a row on the same side of the mean Four out of five points more than 1 sigma from the mean 2 6 Six points in a row all increasing or decreasing Fifteen points in a row within 1 sigma of the mean 3 7 Fourteen points in a row, alternating up and down Eight points in a row more than 1 sigma from the mean 4 8 Control limits are calculated based on the week-to-week variation of the demonstrated output Not shown 7 1 Historical output to stores in standard hours 99.7% probability that output of an “in-control” process will fall between the upper control limit (UCL) and the lower control limit (LCL). 8 5 2 95% probability that output of an “in-control” process will fall between the 2 sigma limits. 6 Average historical output Shows the difference between planned and actual for a given week 68% probability that output of an “in-control” process will fall between the 1 sigma limits. 4 Planned requirements in standard hours 3 The control limits are used as a predictor of future output if the historical output is in control (i.e. does not fail any of the 8 tests below) and the process has not changed significantly Violation of any of the 8 tests below by the planned requirements indicates a nearly zero probability that the actual output can meet the plan. A major process change or re-plan is required.

  10. Six Sigma is All About Variation 1 1 0 1 1 1 0 0 3 . 0 S L = 9 2 . 3 6 9 0 Common causes 8 0 X = 7 4 . 4 6 7 0 6 0 - 3 . 0 S L = 5 6 . 5 5 5 0 6s Special causes 4 0 3 0 1 2 0 1 2 3 4 5 6 7 8 • Six Sigma tools help identify • types of variation • (control chart) • largest source of variation • (components of variation, analysis of variance) • factors which have the greatest influence on variation • (Process Map, FMEA, Screening Design of Experiments) • where to set factors to reduce variation and maximize the output • (Optimizing Design of Experiments)

  11. Six Sigma Tool Box – Example Usage 6s • Thought Process Map • Process Map • Failure Modes and Effect Analysis • Pareto Chart • Control Chart • Confidence Intervals • Product Scorecard • Postmortem • Measurement System Evaluation • Regression Analysis • Design of Experiments • Main Effects Plot • Interaction Plot

  12. CustomerSatisfaction Through Reducing Defects Identify variation Characterize variation Reduce or eliminate variation Maximize output and deliver less defects Satisfied Customer

  13. Six Sigma is Based on Statistics Mean (Process Center) 1 Standard Deviation Remember - no statistics today

  14. States of a Process Lower Specification Limit Upper Specification Limit Process Center • Incapable Process • We have REWORK because some of what we make is waste • Process is WIDER than the specification limits Determined by the customer Determined by the customer The distribution of Our Process output REWORK GOOD GOOD REWORK Process Center Lower Specification Limit Upper Specification Limit • Capable Process • We make as much as the customer needs and have very little waste • Process FITS within the specification limits Determined by the customer Determined by the customer GOOD GOOD

  15. X Bar What? Standard Who? UCL PCB s X LCL X : Sample Mean = arithmetic average of a set of values (process center) s : Standard Deviation = the square root of the variance UCL : Upper Control Limit = +3s from the Sample Mean LCL : Lower Control Limit = -3s from the Sample Mean PCB : Process Capability Baseline = “normal” variation for the sample If you take the bell shaped curve and turn it on its side, you have a control chart

  16. Four Possible States of a Process Threshold Ideal Stable but not Capable Stable and Capable Chaos Brink of Chaos Unstable and not Capable Capable but Unstable Control Limit Spec. limit

  17. So Long As We Satisfy the Requirements, Right? Pilot A Pilot B Both within spec, same average landing Which pilot would you want to fly with? Why?

  18. Moving Towards Six Sigma 1 Standard Deviation 1 Standard Deviation s s s s s s s s s s 6s 4s Pilot A’s Distribution Pilot B’s Distribution Target Target Lower Spec Lower Spec Upper Spec Upper Spec Process Center Process Center In order to be six sigma, like Pilot B, the variation in Pilot A’s landings must be reduced Pilot A is at 4 sigma

  19. Pilot A and B at Sky Harbor Airport Pilot B 0.005 Pilot A Wouldn’t stay in business too long...

  20. Higher Sigma Equates toMajor Reduction in Defects

  21. Six Sigma -The Other Story 1s- 170 misspelled words per page in a book 5s - 1 misspelled word in a set of encyclopedias 6s - 1 misspelled word in all the books in a library 2s - 25 misspelled words per page 3s- 1.5 misspelled words per page 4s - 1 misspelled word per 30 pages Understand you customers’ needs And balance with cost

  22. CMMI Meets Six Sigma

  23. CMMI Reminder Optimizing Time/$/Quality/... Time/$/Quality/... Quantitatively Managed Defined Time/$/Quality/... Time/$/Quality/... Predicted Performance Level the organization concentrates on improving the process to gain an advantage in quality, productivity, and timeliness all the process assets accumulated from Level 2 and 3 are used to quantitatively understand the project the organization has a defined common process which includes the collection of product and process metrics the best practices reside in the projects Managed

  24. Level 2 - Measure the Project Measurement and Analysis (and GP 2.8) Monitor and control the [PA Name] process against the plan for performing the process and take corrective action The initial focus for measurement activities is at the project level. As the organization moves toward Level 3, the measures prove useful for addressing organization and/or enterprise-wide information needs.

  25. Level 3 – Understand the Organizational Practices • At Level 3, project data is collected and analyzed by the organization. Thresholds are set based on good engineering judgment which trigger corrective action • Level 2/3 set the stage for quantitative management (statistical process control). • Repeatable, Consistent Process • Adequate Resources • Defined, Consistent Process Measures • Data Collected From Above Process • Assurance of Process Compliance

  26. Level 4 - Stabilizing the Process 1 1 0 1 1 1 0 0 3 . 0 S L = 9 2 . 3 6 9 0 8 0 X = 7 4 . 4 6 7 0 6 0 - 3 . 0 S L = 5 6 . 5 5 5 0 Special causes 4 0 3 0 1 2 0 1 2 3 4 5 6 7 8 Total # of Defects Req’ts Peer Review Design Peer Review Code Peer Review Test Peer Review Formal Test Peer Review Customer Before Delivery Life Cycle Phase At maturity level 4, an organization applies quantitative and statistical techniques to manage process performance and product quality. Quality and process performance is understood in statistical terms. Special causes of process variation are identified and, where appropriate, the sources of special causes are corrected to prevent future occurrences.

  27. Level 4 - Stabilizing the Process • When performance falls outside normal range of process performance • Identify the reason • Take corrective action when appropriate A critical distinction between maturity level 3 and 4 is the predictability of process performance. At maturity level 4, the performance of processes is controlled using statistical and other quantitative techniques, and is quantitatively predictable.

  28. Level 5 - Optimizing the Process 1 5 0 3 . 0 S L = 1 3 6 . 0 Common causes 1 0 0 X = 7 4 . 4 6 5 0 - 3 . 0 S L = 1 2 . 9 1 0 1 2 3 4 5 6 7 8 • At maturity level 5, an organization continually improves its processes based on a quantitative understanding of the common causes of variation inherent in processes. • When performance is within normal range of process performance but unable to meet the established objectives • Narrow the performance limits • Shift the performance limits

  29. Am I Acting 4 or 5? • A critical distinction between levels 4 and 5 is the type of process variation addressed. • At maturity level 4, the organization is concerned with addressing special causes of process variation and providing statistical predictability of the results. Although processes may produce predictable results, the results may be insufficient to achieve the established objectives. • At maturity level 5, the organization is concerned with addressing common causes of process variation and changing the process (to shift the mean of the process performance or reduce the inherent process variation experienced) to improve process performance and to achieve the established quantitative process-improvement objectives

  30. Level 5-Statistical Process Improvement Six Sigma allows the organization to statistically analyze whether a process change was actually an improvement to the normal range of process performance Cycle Time New Process Old Process Level 5involves continuous improvement to the processes with the goal of improving quality, productivity and decreasing cycle time

  31. Six Sigma Tools Enable Levels 4&5 6s • The Six Sigma methodology provides the tools that implement • Quantitative Project Management, • Organization Process Performance • Causal Analysis and Resolution • Organizational Performance Management • A Level 5 organization is one that has implemented, among • other things, statistical process control (SPC) SPC

  32. Applying Six Sigma Develop Infrastructure PROCESS Continuous Measurable Improvement Quantitatively Understand Process Performance

  33. Develop Infrastructure • Commitment to Process Improvement • Repeatable, Consistent Process • Adequate Resources • Defined, Consistent Process Measures • Data Collected From Above Process • Assurance of Process Compliance Only required for the window you are looking through

  34. Quantitatively Understand Process Performance Measurementsof process performance on project Use Six Sigma tools to identify sources of variation (quantitatively understand process performance) and then act to eliminate or reduce those sources Quantitatively understand the process performance Discover sources of variation Implement improvements • Determine the magnitude of the • effects due to process changes Monitor the impact

  35. Six Sigma – Real Life Example Measurements Statistically understand 1 1 1 1 0 0 1 1 1 1 1 1 0 0 0 0 n n 3 3 . . 0 0 S S L L = = 9 9 2 2 . . 3 3 6 6 a a 9 9 0 0 e e 8 8 0 0 M M X X = = 7 7 4 4 . . 4 4 6 6 7 7 0 0 e e l l p p 6 6 0 0 Planning Customer Rqmts. Design Implement Test Formal Customer - - 3 3 . . 0 0 S S L L = = 5 5 6 6 . . 5 5 5 5 TOTAL Leaked m m Analysis ation Test Before 5 5 0 0 a a Planning S S 4 4 0 0 2.03 0 0 0 0 0 0 0 2.03 0 3 3 0 0 Customer 1 1 0 1.8 1.4 0 4.06 0 16.15 0 23.41 21.61 2 2 0 0 Rqmts. 1 1 S S u u b b g g r r o o u u p p 2 2 3 3 4 4 5 5 6 6 7 7 8 8 0 0 7.32 12.04 32.77 41.6 56.09 0.79 150.61 143.29 Analysis Determine the magnitude Design Monitor the impact 0 0 0.13 41.99 8.2 23.2 118.94 5.28 197.74 155.75 Implement 0 0 0.17 0.5 154 90.3 88.88 23.3 357.15 203.15 ation Test 0 0 0 0.16 0.03 19.92 4.5 0 24.61 4.69 Formal 0 0 0 0 0 2.34 149.25 0 151.59 2.34 Test Customer 0 0 0 0 0 0 2.7 13.6 16.3 13.6 Before TOTAL 2.03 1.8 9.02 54.69 199.06 177.36 436.51 42.97 923.44 544.43

  36. Continuous Measurable Improvement Measurements of process performance of project • Can be next biggest hitter • Can be next quickest • Can be next logical problem • Can be anything the org. determines Statistically understand the process capability Discover sources of variation Determine the magnitude of the effects due to process changes Implement improvements Monitor the impact

  37. Six Sigma Tool Box – Example Usage 6s • Thought Process Map • Process Map • Failure Modes and Effect Analysis • Pareto Chart • Control Chart • Confidence Intervals • Product Scorecard • Postmortem • Measurement System Evaluation • Regression Analysis • Design of Experiments • Main Effects Plot • Interaction Plot

  38. Thought Process Map Look for different Involve multiple projects requirements Multiple disciplines Question to answer Different customers Multiple perspective Is there one? What are How Process Map FMEA/CE How Determine? Problem to be solved What are the x's, y's? tell? What is the current process Postmortems possible process? weaknesses? BARRIER- BARRIER- People's time People's time How do we How well is it measure the working now? process? What is the current Gather data What data? data ? BARRIER- Lack of data? What about older projects? No, data not available Refine estimate as site data becomes available What data? Data for Estimate rework What want to know? Data from FMEA What is data by phase Organize data to New projects showing us? effectively analyze BARRIER- Lack of data? Identify and Validate Measurement Not Remove Special Update process to MSE(KAPPA/ICC System Adequate Causes Is the data any ensure data has or Nested Design) good? higher confidence rate/ train No Adequate Stable process Characterize Set up control Did change cause Determine action Yes Optimize plan and Use improvement? Drill down into the DOE Based on data Make and Process Control Charts to data/NEM/Control communicate BARRIER- Monitor Charts improvements BARRIER- What are Newsletters Projects not Effectiveness of People's time important factors? Liaison Meetings required to follow? Improvements Choice of factors Underlined - Barrier Red - Question or expected result Blue - Answer or actual result

  39. Process Map 50,000 ft level Activity/Process Inputs Outputs 500 ft level Output Output Output Output Output Output Output Output Rework No No Rework Activity/ Sub-process Activity/ Sub-process Activity/ Sub-process Activity/ Sub-process Defects? Defects? C - Input C - Input X - Input C - Input Rework N - Input N - Input N - Input N - Input Rework S - Input S - Input S - Input S - Input X - Input C - Input X - Input X - Input Output Output Output Activity/ Sub-process X - Critical (statistical proven critical) N - Noise (can't or choose not to control) C - Input S - Input S - SOP (the standard way to do it) S - Input C - Controllable (can be changed to see effect) S - Input

  40. FMEA 1 3 Severity 9 - Defects go to customer 6 - Defects cause rework 3 - Data not collected 1 - No harm/no foul Occurrence: 9 - Regular occurrence 6 - Occurs more than occasionally 3 - Occurs occasionally 1 - Rare occurrence Detection: 9 - Nothing in place 6 - Based on individuals 3 - Check in place, usually works 1 - Check in place & working

  41. What Was Learned • Potential causes (factors) which kept showing up over and over on the FMEA: • Inappropriate review team (“wrong” moderator, dominant, inexperienced, or yes-people made up the team) • Lack of process awareness (both unintentional and deliberate) • No or inadequate review criteria (review what is there not what is missing, biased review based on experience with phase) • Plan to minimize the occurrence and increase the detection : • Update the process to highlight required participants, their roles and responsibilities on the Notice and Agenda • Roll-out Peer Review training • Perform peer review process evaluations • Generate common evaluation criteria for all work products that can be used across the entire organization Use what was learned about factors as an input into DOE:

  42. Cost of Rework due to defects-in days Planning Customer Rqmts. Design Implement Test Formal Customer TOTAL Leaked Analysis ation Test Before Planning 2.03 0 0 0 0 0 0 0 2.03 0 Customer 0 1.8 1.4 0 4.06 0 16.15 0 23.41 21.61 Rqmts. 0 0 7.32 12.04 32.77 41.6 56.09 0.79 150.61 143.29 Analysis Design 0 0 0.13 41.99 8.2 23.2 118.94 5.28 197.74 155.75 Implement 0 0 0.17 0.5 154 90.3 88.88 23.3 357.15 203.15 ation Test 0 0 0 0.16 0.03 19.92 4.5 0 24.61 4.69 Formal 0 0 0 0 0 2.34 149.25 0 151.59 2.34 Test Customer 0 0 0 0 0 0 2.7 13.6 16.3 13.6 Before TOTAL 2.03 1.8 9.02 54.69 199.06 177.36 436.51 42.97 923.44 544.43 Phase Detected PhaseIntroduced Data has been changed to protect the innocent

  43. Cost Savings Goal IMPACT ON BOTTOM LINE Same number of total defects introduced in the same phases

  44. Distribution Characteristics of Data These 3 phases account for > 92% of rework 1 0 0 9 0 Goal 8 0 7 0 Percent Defects Found In Phase 6 0 5 0 4 0 3 0 2 0 Test Req’ts Design Implem. Planning Cust. Plan Customer Formal Test

  45. P Chart – Determines Stability But we sampled only 3 projects - what does the population look like?

  46. Confidence Intervals

  47. …Gives Us Population Range 1 4 0 9 0 t n e c r e p 4 0 0 P l a n n i n g C u s t . R e q s D e s i g n I m p l e m T e s t F o r m a l C u s t . T s t p h a s e If I were a betting man…. the true population means are within these intervals 134 139 101 100 100 88 96 78 Goal 74 59 37 42 21 7 14

  48. ANOVA Tells Us the Variation is Between Subgroups Analysis of Variance for percent Source DF SS MS F P phase 7 10841.9583 1548.8512 8.701 0.000 project 16 2848.0000 178.0000 Total 23 13689.9583 Variance Components Source Var Comp. % of Total StDev phase 456.950 71.97 21.376 project 178.000 28.03 13.342 Total 634.950 25.198 Project variation Phase variation

  49. What Was Learned 100 % 80 % World Class Green -customer process Red - < 92 5 of rework Blue - testing process SW Phase • If no action is taken 95% confident that • the Requirements Phase will find between 21% - 37% of defects in phase • the Design Phase will find between 42% - 88% of defects in phase • the Implementation Phase will find 59% - 78% of defects in phase • Variation between the phases (72%) is greater than variation between projects (28%) • need to work largest source of variation - what changed between, what didn’t, etc.

  50. Design of Experiment (DOE) Fractional Factorial Design Factors: 5 Base Design: 5, 16 Resolution: V Runs: 16 Replicates: 1 Fraction: 1/2 Blocks: none Center pts (total): 0 StdOrder RunOrder Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 % Match 1 1 -1 1 -1 -1 -1 70 5 2 -1 -1 -1 -1 1 55 3 3 -1 -1 -1 1 -1 30 7 4 -1 1 -1 1 1 60 2 5 -1 -1 1 -1 -1 70 6 6 -1 1 1 -1 1 80 4 7 -1 1 1 1 -1 65 8 8 -1 -1 1 1 1 60 9 9 1 -1 -1 -1 -1 45 13 10 1 1 -1 -1 1 55 15 11 1 -1 -1 1 1 35 11 12 1 1 -1 1 -1 55 14 13 1 -1 1 -1 1 80 10 14 1 1 1 -1 -1 80 12 15 1 -1 1 1 -1 55 16 16 1 1 1 1 1 70

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