Dmaic Lean Six Sigma

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A methodology of Business Process Improving, which is essential for organizations want to use Six Sigma

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Dmaic Lean Six Sigma

  1. 1. CSI Singapore Following the Chain of Evidence (the Facts) in Lean Six Sigma Process Improvement Projects (DMAIC) Robert Johnston, Ph.D. Executive Director, Six Sigma International Institute for Learning, Inc. SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  2. 2. 2 IIL Expertise Premier solution provider of training and consulting in: Project, Program and Portfolio Management Business Analysis Lean Six Sigma Microsoft® Office Project 2007 Interpersonal & Leadership Skills Innovative learning methods Project management methodology Competency mapping, training and career paths © 2010 International Institute for Learning, Inc.
  3. 3. 3 Global Presence Europe / M-East Americas Asia IIL Headquarters IIL France IIL Singapore New York Hub Asie Pacifique (Europe – Middle East - Africa) IIL Finland IIL China IIL Canada IIL Spain IIL India IIL Germany IIL Japan IIL Mexico IIL United Kingdom IIL Hong Kong IIL Hungary IIL Brazil IIL Dubai IIL Australia © 2010 International Institute for Learning, Inc.
  4. 4. GS-4 Who Am I? Robert Johnston, Ph.D. Statistics, MBB Philosophy: practicality trumps theory • Utility = (Perfection of idea) * (Probability people will use it) Experience Animal Feed Products, Pharmaceuticals, GE Capital Allstate, Coca-Cola, Carlson (Radisson), Caterpillar, Deutsche Bank, DHL, FDMS, Intuit, TRW, Schreiber Foods, StarHub, U.S. Navy Trained/Coached several hundred Lean Six Sigma practitioners/projects SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  5. 5. GS-5 What is Lean Six Sigma? “SIX SIGMA: A comprehensive and flexible system for achieving, sustaining, and maximizing business success. Six Sigma is uniquely driven by close understanding of customer needs, disciplined use of facts, data, and statistical analysis, and diligent attention to managing, improving, and reinventing business processes.” - “The Six Sigma Way” – Pande p. xi SCS ingapore © 2010 International Institute for Learning, Inc. Version 1.0
  6. 6. GS-6 What is Lean Six Sigma? SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  7. 7. GS-7 Lean Six Sigma Triad Main Focus SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  8. 8. GS-8 Process Design – DMADV? DMADV is the recipe for designing new processes/products. Usually more complex/longer than DMAIC, so companies often implement DMADV after successfully completing some DMAIC projects. Define the process/product and the business case Verify D Drive Customer Requirements Through V process/product performance Entire Design Cycle Measure: Define the FMEA QFD M customer requirements and prioritize them Manage Risk Develop detailed design D A Analyze functional requirements, create high-level design © 2010 International Institute for Learning, Inc.
  9. 9. GS-9 What is DMAIC? DMAIC is the recipe or methodology for improving existing processes; it is the backbone of Six Sigma and the starting point for most companies beginning the Six Sigma journey. Where’s the PAIN to the Customer? The Business? Monitor & Take Action If Root Cause Re-appears t tcu Measure or Performance & Sh Focus on Critical Areas 80% 20% Pull It Out by the Roots Drill Down for Root Cause SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  10. 10. GS-10 Use of Data in DMAIC: “It’s all about the evidence” Data is the bedrock of Six Sigma & DMAIC; it helps separate fact from fiction. Real-time Voice of Customer, Monitoring Data Financials 14 12 UCL=12.28 10 8 Cost _ 6 X=5.84 4 10 2 9 0 LCL=-0.61 8 2 4 6 8 10 12 14 16 18 20 22 24 7 Observation Errors 6 6 5 4 3 2 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Baseline data, focusing data Before / After (Pareto Principle) Data Before After 70 60 50 100 80 18 60 Percent 40 Count 16 16 14 30 40 14 12 20 Cycle Time 12 20 10 10 8 UCL=7.71 Cycle Time 10 0 0 6 Location NW W S MW Other _ Count 50 10 5 3 1 X=4.50 8 4 Percent 72.5 14.5 7.2 4.3 1.4 Cum % 72.5 87.0 94.2 98.6 100.0 2 6 LCL=1.29 0 2 4 6 8 10 12 Observation 14 16 18 20 Cause & Effect Data 4 2 2 3 4 5 6 Experience 7 8 9 SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  11. 11. GS-11 Six Sigma & Lean (It’s like Chocolate and Peanut Butter) Six Sigma Focus on Quality Customer Requirements Variation & Defect Reduction Six Sigma Data Based Support Infrastructure Lean Focus on Speed Lean Cycle Time Reduction Elimination of Waste Rapid Project Execution SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  12. 12. GS-12 Why is it called Six Sigma? (optional) Sigma (σ, standard deviation ) measures process variation (VOP) Customer Customer Requirement Requirement σ σ σ σ σ σ Mean Bad Good Bad Compared to Customer Requirements (VOC) shows the % Defects SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  13. 13. GS-13 Why is it called Six Sigma? (optional) Reducing variation means reducing the number of defects 3.4 Defects per Million Customer Customer Requirement Requirement σ σ σ σ σ σ σ σ σ σ σ σ Mean Bad Good Bad Six Sigma represents 6 standard deviations from the mean to the upper or lower specification limits of the customer SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  14. 14. GS-14 DMAIC: Following the Chain of Evidence Improving Processes SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  15. 15. GS-15 Define: Houston, we have a problem! D M A I C ID the Process Including Supplier, Inputs, Outputs, Customer ID the Customer ,his/her Requirements, and the Performance Gap Critical To Quality (CTQ) Make them Measureable Define a Defect Input Output SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  16. 16. GS-16 Define: CTQ Identification Example D M A I C You’ve just ordered a pizza from a local pizza delivery shop. What are your CTQs ? 4-5 oz cheese… 40-50oC on delivery <30 min More specific and measureable … Not very specific or measureable … SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  17. 17. GS-17 Measure: So, how bad is it? D M A I C Map Process in detail Establish data collection plan Output data (y) Stratification data (x’s) Check Measurement System Collect Data Baseline Process Performance Focus- stratify SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  18. 18. 1-18 Process Focus What is supposed to happen… SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  19. 19. 1-19 Process Focus What really happens… “Hidden Factory” Rework … Inspection … Delays … Work-a-rounds … SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  20. 20. GS-20 Impact of Hidden Factory on Cycle Time Process Lead Time (PLT) From Customer request to customer receipt Value Add Process Time (VAPT) Time spent on tasks customer is willing to pay for Process Cycle Efficiency (PCE) PCE = VAPT / PLT What is a typical value for PCE? SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  21. 21. 2-21 WIP & Little’s Law: What is WIP? WIP stands for Work in Process (or Progress). If we have too much WIP: Cycle times grow and are unpredictable. Resources are spent handling it. Processes are cluttered so it’s hard to expedite something if necessary. SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  22. 22. 2-22 Little’s Law Little’s Law states: Like the line at an amusement park: WIP PLT = Exit Rate IN Exit Rate: Where… OUT 2 people minute PLT = Process Cycle Time WIP = Work In Process Exit Rate = Units/Time 12 People PLT = People 2 Minute = 6 Minutes SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  23. 23. 2-23 Little’s Law: WIP (1 of 2) If WIP is reduced, then Lead Time is reduced: IN 6 People PLT = People Exit Rate: 2 Minute OUT 2 people minute = 3 Minutes While this is common sense, it is not usually how processes are run. We keep throwing more “stuff” into the process (as fast as orders come) increasing WIP and Lead Time. But if we don’t throw the orders into the process, what do we do with them and why? SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  24. 24. 2-24 Little’s Law: WIP (2 of 2) Have a “triage” or waiting area. Waiting orders can be reprioritized (expedited). Orders in the process can be found and expedited more easily. We know exactly how long it will take an order to be processed once it enters the queue. …but don’t forget, the Customer experiences Waiting Time + PLT Waiting Room IN 6 People PLT = People Exit Rate: 2 Minute OUT 2 people minute = 3 Minutes This one can be expedited if necessary (can be done in 3 minutes instead of the original 6 minutes). SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  25. 25. 2-25 General Application of Little’s Law to Projects/Initiatives/Work Work many things at once Project W1 W2 W3 W4 W5 A $ $ B $ $ C $ $ Focus on a few things at a time Project W1 W2 W3 W4 W5 A $ $ $ $ B $ $ $ C D $ $ Increased Value Increased Flexibility SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  26. 26. GS-26 A Word on Planning Data Collection: Avoid a Port-Mortem D M A I C 1. What is the question? 3. Collect data to go from 1. to 2. 2. What Graph/Summary will answer it? SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  27. 27. Check the Measurement System – 2-27 Is Our Data Any Good? D M A I C Measurement System X X Process SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  28. 28. 2-28 Measurement Systems Analysis (MSA) Exercise D M A I C M&M Company wants to improve the quality of their output. It’s a Good M&M if… Clear/Legible Logo, and Uniform/Consistent Color, and No Cracks in Shell Otherwise, it’s a Bad M&M. SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  29. 29. 2-29 Measurement Systems Analysis (MSA) Exercise D M A I C A B C D E Teams of 5 or 6 1 Make a Team grid, 5x5, place 25 M&Ms in the 2 grid (flip chart paper) 3 Each team member makes a 5x5 score sheet 4 (8.5x11 or A4) Independently grade 5 each M&M as Good (G) or Bad (B). No talking, 1 A B C GG B D E G B sounds of amazement, 2 G B B GG etc. 3 B B G B G 4 B B B G B 5 B GG G B SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  30. 30. 2-30 Measurement Systems Analysis (MSA) Exercise Answers D M A I C When done, choose a spokesperson to read through score sheet one item 1 A B C GG B D E G B at a time. 2 3 G B B B B G GG B G 4 B B B G B If all Team Members agree, 5 B GG G B then they get a point. Report Team Point Total. SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  31. 31. 2-31 Measurement Systems Analysis (MSA) Exercise Answers D M A I C 100 Desired Results % Agreement 75 50 Typical Results! 25 0 1 2 3 4 5 6… Team SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  32. 32. GS-32 MSA Examples Banking IT Manufacturing SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  33. 33. 2-33 Existing Data Sources There is a lot of data out there Review whatever you can find Guidelines for using existing data How was the data created? – Using which operational definition? (Yours?) – For which purpose/intention? – Under which circumstances? (Rush, end of the shift, …?) If the data does not follow your operational definition can it be reformatted to fit your needs? (maybe they collected more data than they showed) SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  34. 34. GS-34 Looking at Data Which Regions/Teams are better? Worse? Fooled you! It’s all generated from an identical source … the differences are just random…not real. Summaries – like averages or totals – may not tell the whole story SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  35. 35. GS-35 Look at the Data Need to start looking at the raw data – not just summaries of the data – variation is important! SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  36. 36. GS-36 Look at the Data: Another Example Company complaint resolution process: Goal: Resolution <50 days Actual: Average Resolution = 97 days! CEO decides need major/fundamental process change Requires fundamental process change Fundamentally process OK – it’s the exceptions Which is it? Both have average of 97! SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  37. 37. GS-37 Analyze: Find the Root Cause: y=f(x) D M A I C SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  38. 38. GS-38 Analyze: Verify Cause & Effect Relationship D M A I C Dotplot of Approval Time vs Location Scatterplot of Cycle Time vs Loan $ Stratified 65 Scatterplot Location Cycle Time London •Dotplot 55 Continuous •Boxplot 45 NY 40 50 Approval Time 60 70 •Histogram 35 100000 125000 150000 175000 Each symbol represents up to 2 observations. Loan $ •t-test •Regression •ANOVA / ANOM •Multiple Regression •Test of Equal Variance Y: Effect •DOE Pareto Chart of Sale by Region Dotplot of Face Time vs Sale 25 Region = E NO YES Region = W Sale NO YES Stratified Stratified •Pareto •Dotplot Sale 20 Count YES 15 10 or •Boxplot 5 Table •Histogram NO 40 50 60 70 Discrete 0 NO YES Face Time Sale Each symbol represents up to 2 observations. •Test of Two Proportions •Logistic Regression •Chi-square Discrete Continuous X: Potential Cause or Stratification Factor SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  39. 39. GS-39 Analyze: Verify Cause & Effect Relationship- YY/NN D M A I C Effect (Y) Present? YES Y/Y NO N/N NO YES Potential Cause (X) Present? SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  40. 40. GS-40 Causal Relationships- Lurking Variables D M A I C Lurking Variables are ones you did not measure, or even consider, that impact your process/data 0 5 10 20 25 # Drownings 0 500 1000 # Ice-cream Sales What’s the Lurking Variable? SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  41. 41. GS-41 Causal Relationships- Lurking Variables D M A I C The number of people at the beach which is a function of Temperature! 1000 0 5 10 20 25 # Ice-Cream Sales # Drownings 0 500 50 70 90 50 70 90 Temperature Temperature SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  42. 42. GS-42 Examples of Lurking Variables Number of Damaged Cartons per shift Training didn’t solve the problem… It was the fork-trucks! New employees got the old fork-trucks – they had a design flaw SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  43. 43. GS-43 Lurking Variables: Aggregated Data D M A I C Death Rates in Hospitals A B Deaths 450 130 (15%) (11.8%) Patients 3000 1100 What if account for Patient Condition? Good Condition Poor Condition A B A B Deaths 50 100 Deaths 400 30 (5%) (10%) (20%) (30%) Patients 1000 1000 Patients 2000 100 Watch out for Lurking Variables in Causal Analysis! SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  44. 44. GS-44 Improve: Fix It! D M A I C Eliminate the Brainstorm solutions Root Cause Evaluate Solutions and Select best Manage Risk Pilot Solution Verify Results SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  45. 45. GS-45 Before & After Many solutions don’t actually help How will you know if yours did? SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  46. 46. GS-46 Control: Make it Stay Fixed D M A I C Standardize Process Train on the new Process On-going Process Monitoring SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  47. 47. 2-47 Responding to Variation Inappropriately Rule 1: Do Nothing – Start Funnel at 50 – Drop 24 Balls Rule 2: Compensate – Start Funnel at 50 – Drop – Adjust: e.g., if ball drops 3 below target, adjust funnel 3 up, etc. – Repeat Drop & Adjust cycle 24 times SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  48. 48. 2-48 Responding to Variation Inappropriately Rule 1: Do Nothing – Start Funnel at 50 – Drop 24 Balls Rule 2: Compensate – Start Funnel at 50 – Drop – Adjust: e.g., if ball drops 3 below target, adjust funnel 3 up, etc. – Repeat Drop & Adjust cycle 24 times Rule 2 Results 41% increase Rule 1 in variation! Results SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  49. 49. GS-49 Control: Two Kinds of Variation D M A I C Special Cause – events Common Cause – events only happen sometimes to happen sometimes to some people/processes everyone SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  50. 50. 2-50 Exercise: Two Kinds of Variation Sign your name 3 times Common Cause Now with other hand Special Cause Common Cause (just more of it than with the other hand) SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  51. 51. 2-51 Understanding Variation Why it matters Variation exists in all processes There are two fundamental kinds of variation: Special Cause and Common Cause The correct response depends on whether it is Special or Common Cause… SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  52. 52. 2-52 Responding to Variation Type of Variation? Common Special Meets Respond to individual data points, determine cause, take corrective action Requirements? 3. Yes No Use all the data to understand cause of Do Nothing variation. Make fundamental process change. 1. 2. Common Cause Variation Customer or Internal Requirement SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  53. 53. Introduction to Control Charts Distinguishing Common & Special Cause Variation Example of Standard Business Reporting SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  54. 54. 2-54 Business Performance Report: Sales Same Year- This Last Month To- Month Month Last Date Year 101 108 102 98 Please assess our recent performance • Last month’s performance (108) is better than this month’s (101). • This month’s performance (101) is about the same as YTD’s (102). • But this month’s performance (101) is better than the performance the same month last year (98). Let’s see if our interpretation changes when we plot our data over time, where variation can be seen and taken into account… SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  55. 55. 2-55 Scenario 1 Same Year- This Last Month To- Month Month Last Date Year 101 108 102 98 Time Series Plot of Scenario 1 110 105 Scenario 1 100 97.61 95 90 Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Month This chart supports an interpretation of a significant change last month – a special cause. SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  56. 56. 2-56 Scenario 2 Same Year- This Last Month To- Month Month Last Date Year 101 108 102 98 Time Series Plot of Scenario 2 110 105 Scenario 2 100 97.61 95 90 Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Month Last month’s result doesn’t appear unusual – just common cause variation. SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  57. 57. 2-57 Control Chart for Scenario 1 Same Year- This Last Month To- Month Month Last Date Year 101 108 102 98 I Chart of Scenario 1 115 110 1 105 UCL=104.96 Control Charts are based Individual Value 100 _ 95 X=97.61 on the data and show 90 LCL=90.26 Common Cause variation 85 80 Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Month Last month’s performance is Special Cause variation SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  58. 58. 2-58 Control Chart Scenario 2 Same Year- This Last Month To- Month Month Last Date Year 101 108 102 98 I Chart of Scenario 2 115 UCL=114.49 110 105 Individual Value 100 _ X=97.61 95 90 85 80 LCL=80.73 Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Month Last month’s performance is Common Cause variation SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  59. 59. 2-59 Control Chart Scenario 2: Tampering Same Year- This Last Month To- Month Month Last Date Year 101 108 102 98 I Chart of Scenario 2 115 UCL=114.49 110 105 Individual Value 100 _ X=97.61 95 Minimum Requirement 90 85 80 LCL=80.73 Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Month If a process with Common Cause variation is adjusted based on individual data points (tampering) then process variation will increase! SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  60. 60. 2-60 Conclusions: Standard Business Reporting Two radically different processes, requiring Year- Same different management approaches, both produce This Month Last Month To- Month Last the same standard management report … this Date Year should concern you! 101 108 102 98 Charting data over time gives context. Can see patterns and variation in the data Control Charts plot data over time and use I Chart of S cenario 1 I Chart of S cenario 2 115 115 UCL=114.49 110 110 1 Control Limits to detect Special Cause variation 105 UCL=104.96 105 Individual Value Individual Value 100 100 _ _ X=97.61 X=97.61 95 95 so appropriate action can be taken. 90 LC L=90.26 90 85 85 80 LC L=80.73 80 Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Mont h Mont h Do managers and workers in your company understand the difference between common and special cause variation? If not, then tampering is occurring. SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  61. 61. GS-61 Two Kinds of Variation: Responding Appropriately D M A I C Management takes a big step forward when it stops asking workers to explain randomness. SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  62. 62. GS-62 Summary: What is DMAIC? DMAIC is the recipe or methodology for improving existing processes; it is the backbone of Six Sigma and the starting point for most companies beginning the Six Sigma journey. Where’s the PAIN to the Customer? The Business? Monitor & Take Action If Root Cause Re-appears t tcu Measure or Performance & Sh Focus on Critical Areas 80% 20% Pull It Out by the Roots Drill Down for Root Cause SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  63. 63. GS-63 “It’s all about the evidence” Data is the bedrock of Six Sigma & DMAIC; it helps separate fact from fiction. Real-time Voice of Customer, Monitoring Data Financials 14 12 UCL=12.28 10 8 Cost _ 6 X=5.84 4 10 2 9 0 LCL=-0.61 8 2 4 6 8 10 12 14 16 18 20 22 24 7 Observation 6 6 Errors 5 4 3 2 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Baseline data, focusing data Before / After (Pareto Principle) Data Before After 70 60 50 100 80 18 60 Percent 40 Count 16 16 14 30 40 14 12 20 Cycle Time 12 20 10 10 8 UCL=7.71 Cycle Time 10 0 0 6 Location NW W S MW Other _ Count 50 10 5 3 1 X=4.50 8 4 Percent 72.5 14.5 7.2 4.3 1.4 Cum % 72.5 87.0 94.2 98.6 100.0 2 6 LCL=1.29 0 2 4 6 8 10 12 Observation 14 16 18 20 Cause & Effect Data 4 2 2 3 4 5 6 Experience 7 8 9 SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  64. 64. GS-64 Data Specific Concepts (“It’s all about the evidence”) Define Scoping Projects Understanding Customer Requirements Measure Seeing the Process The Devil’s in the Details (PCE<5%) Impact of Multitasking The State of Data MSA Look at the Data (not just summaries of the data) Analyze Causal Reasoning (YY/NN) Lurking Variables Improve Verify Solutions (Before/After) Control Responding to Variation (Special/Common Cause) SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  65. 65. GS-65 The Games afoot! If you love… a mystery, and the thrill of discovery, and the satisfaction of verifiable, positive, enduring change Then Lean Six Sigma will add a powerful new dimension to your skills! SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
  66. 66. GS-66 About IIL Member of SCS's Corporate Council IIL Worldwide Locations The Corporate Council is designed to IIL has regional offices throughout the US and in major provide corporations the opportunity cities in Europe, Canada, Latin America and Asia. We can to support and associate with SCS deliver the corporate solution that’s just right for your directly and to develop synergies global needs. Our training materials can be delivered to you between SCS and senior executives at in different languages, and the experience of our subject leading corporations in the global matter professionals is international in scope. community. SCS Registered Education Provider Registered Education Providers (REPs) are organizations approved by SCS to offer project management training for Professional Development Units (PDU). The Kerzner Approach® to Best Practices (APMC™) Completion of this 64-hour advanced live eLearning curriculum Certificate of Course Completion extends beyond what is needed to complete individual IIL is an authorized CEU sponsor projects on time and within budget. It focuses on providing member of the International you with advanced project management knowledge and Association for Continuing integrating project management process improvement into an Education and Training. organization at every level--from individual projects up through enterprise-wide portfolio management. ACE College Credit Recommendations The American Council on Education (ACE) College Credit Recommendation Service (CREDIT) has recommended Letter Grades and Transcripts numerous IIL courses for IIL has established cooperative agreements with undergraduate and graduate ACE universities, such as The University of Chicago. credits. SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0

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