2. Everything you wanted to know about Six-Sigma but were afraid to ask! Dave Stewardson - ISRU Ronald Does – The Netherlands Soren Bisgaard - USA Bo Bergman – Sweden Ron Kennet – Israel Oystein Evandt – Norway Xavier Tort-Martorell - Spain

3. Pro-Enbis All joint authors - presenters- are members of: Pro-Enbis and ENBIS. This presentation is supported by Pro-Enbis a Thematic Network funded under the ‘Growth’ programme of the European Commission’s 5th Framework research programme - contract number G6RT-CT-2001-05059

4. ENBIS European Network for Business and Industrial Statistics www.enbis.org

5. Overview u Brief resume of Six Sigma - Key concepts - Training - Execution u The Scientific Method u Project selection u “Quotes” u Barriers – Overcoming these u Critique of ISO 9000 u Change programs u Real reasons why six-sigma works u Simple case study

6.  Hoovering 30 m 2 on 6  -level means only 1 cm 2 missed. u 1/3.4 million part of the day equals 0.29 second u 1/3.4 million part of the equator of the earth equals about 140 meter.  is the symbol for the standard deviation. “6  ” is equivalent with 3.4 defects per million opportunities. 6  : new world A new way of doing business?

7.   Statistical background Target =  Some Key measure

8.   Statistical background Target =  ‘Control’ limits

9.   LSL USL Statistical background Required Tolerance Target = 

10.   LSL USL Statistical background Tolerance Target =  Six-Sigma

11.   LSL USL ppm 1350 ppm 1350 Statistical background Tolerance Target = 

12.   LSL USL ppm 0.001 ppm 1350 ppm 1350 ppm 0.001 Statistical background Tolerance Target = 

13. Statistical background But Six-Sigma allows for un-forseen ‘problems’ and longer term issues when calculating failure error or re-work rates Assumes a process ‘shift’

14. LSL 0 ppm ppm 3.4  USL ppm 3.4 ppm 66803   Statistical background Tolerance

15. Performance Standards 2345623456 308537 66807 6210 233 3.4  PPM 69.1% 93.3% 99.38% 99.977% 99.9997% Yield Process performance Process performance Defects per million Defects per million Long term yield Long term yield Current standard World Class

16. Number of processes 3σ3σ 3σ3σ 4σ4σ 4σ4σ 5σ5σ 5σ5σ 6σ6σ 6σ6σ 1 10 100 500 1000 2000 2955 1 10 100 500 1000 2000 2955 93.32 50.09 0.1 0 93.32 50.09 0.1 0 99.379 93.96 53.64 4.44 0.2 0 99.379 93.96 53.64 4.44 0.2 0 99.9767 99.77 97.70 89.02 79.24 62.75 50.27 99.9767 99.77 97.70 89.02 79.24 62.75 50.27 99.99966 99.9966 99.966 99.83 99.66 99.32 99.0 99.99966 99.9966 99.966 99.83 99.66 99.32 99.0 First Time Yield in multiple stage process Performance standards

17. Benefits of 6  approach w.r.t. financials Financial Aspects

18. u Simple –Eliminate defects –Eliminate the opportunity to have defects u Complex –Vision –Metric (Standard measuring method) –Benchmark –Philosophy –Method –Tool for:  Customer satisfaction  ‘Breakthrough’ improvements  Continuous improvement  Employee involvement –Agressive goals What is Six Sigma as a Concept?

19. A scientific and practical method to achieve improvements in a company Scientific: Structured approach. Assuming quantitative data. Practical: Emphasis on financial result. Start with the voice of the customer. “Show me the data” ”Show me the money” Six Sigma

20. Six Sigma Methods Production Design Service Purchase HRM Administration Quality Depart. Management M & S IT Where can Six Sigma be applied?

21. GE “Service company”Examples u Approving a credit card application u Installing a turbine u Lending money u Servicing an aircraft engine u Answering a service call for an appliance u Underwriting an insurance policy u Developing software for a new CAT product u Overhauling a locomotive

22. DMAIC Define Select a project Measure Prepare for assimilating information Analyze Characterise the current situation Improve Optimise the process Control Assure the improvements Six-Sigma - A “Roadmap” for improvement

23. Belongs to the middle management Is well-educated Project is related to his daily activities May prioritise his work Well motivated Willing to change Has good social skills Black Belt Improvement potential: € 50 000 Execution

24. Training (1 week) Work on project (3 weeks) Work on project (3 weeks) Review Define MeasureAnalyzeImproveControl Throughput time project 4 months (full time) Classic Training strategy

25. In Spain 5 x three day sessions Includes weekend More ‘Homework’ Heaver individual support Fewer advanced methods It is their view that some training is not assimilated by delegates and that some items do not fit the need of some delegates Training

26. In Poland 5 x 5 day sessions But with 5 weeks between training sessions not 3 weeks Extra Support via on-line materials Individual support stepped up Training

27. In Sweden 4 x 5 day day sessions 3 week gaps as in America Less emphasis on top-down Perceived to be more need for buy-in by staff than in America Training

28. Black Belt Training Application Review MBB MBB, Champion MBB, Champion MBB, Champion MBB, Champion Project execution

29. TraditionalSix Sigma -Project leader is obliged to make an effort. -Set of tools. -Focus on technical knowledge. -Project leader is left to his own devices. -Results are fuzzy. -Safe targets. -Projects conducted “on the side”. -Black Belt is obliged to achieve financial results. -Well-structured method. -Focus on experimentation. -Black Belt is coached by champion. -Results are quantified. -Stretched targets. -Projects are top priority. Conducting projects

30. Black Belt is given the required resources -Training in statistical methods. -Time to conduct his project! -Software to facilitate data analysis. -Permissions to make required changes!! -Coaching by a champion – or external support. Project support

31. In other words the Black Belt is -Empowered. -In the sense that it was always meant! -As the theroists have been saying for years! Project support

32. 7. Screen potential causes. 8. Discover variable relationships. 9. Establish operating tolerances. 10. Validate measurement system. 11. Determine process capability. 12. Implement process controls. DMAIC procedure 4. Establish product capability. 5. Define performance objectives. 6. Identify variation sources. 1. Select CTQ characteristic. 2. Define performance standards. 3. Validate measurement system. Measure Analyze Improve Control Define Roadmap to improvement

33. Statistics Methods for the collection, presentation and analysis of data. Based on mathematics and mathematical modelling. Major role is played by uncertainty / variation. Statistical approach to quality improvement: 1. Explain  predict  control. 2. All ideas are empirically tested before they are accepted. 1. Y = f(X 1, X 2, …, X n ). 2. “Show me the data”. 1. Y = f(X 1, X 2, …, X n ). 2. “Show me the data”. Basic approach

34. Data, measurements, observations Hypotheses (potential leverage variables) Creative thinking Critical thinking Testing Exploratory study Learning by scientific method: Scientific method

35. Scientific method (after Box)

36. Plan Do Check/Study Act Deming Cycle

37. The Scientific Process u Key elements: –Formulation of the problem –Collection of data –Experimentation –Generation of ideas from patterns in data– hypothesis generation –Making predictions from hypothesis –Comparing predictions with real data –Making inferences from the data

38. Exploratory study: At first we search -- like a detective -- in the data for traces of potential leverage variables. We must not be critical. It is more important to find all leverage variables. Testing: Then we determine -- like a judge -- which of the potential leverage variables are indeed important. We do this by conducting an experiment. How to discover potential leverage variables: Exploit available knowledge: FMEA Cause and effect diagram Technical literature Collection and analysis of data: Control chart Boxplot Scatter diagram The search for root causes

39. Practical solutionStatistical solution Statistical problemPractical problem Y = f(X 1, X 2, …, X n ) Approach to improve Problem fixing vs. explanation

40. Define Select: - the project -the process -the Black Belt -the potential savings -time schedule -team Project selection Is management’s responsibility.

41. Projects may be selected according to: 1.A complete list of requirements of customers. 2.A complete list of costs of poor quality. 3.A complete list of existing problems or targets. Project selection

42. 1. Requirements, 2. Costs, 3. Problems. 1.Collect data 2.Arrange the information 3.Give priority -Financial benefits -Expected throughput time of the project -Severity of the problem 321 Project prioritization

43. Before a simple case study a few quotes - some important issues - then some why’s ?

44. “the most important initiative GE has ever undertaken”. Jack Welch Chief Executive Officer General Electric In 1995 mandated each GE employee to work towards achieving 6 sigma The average process at GE was 3 sigma in 1995 In 1997 the average reached 3.5 sigma GE’s goal is to reach 6 sigma by 2001 Investments in 6 sigma training and projects reached 45MUS$ in 1998, profits increased by 1.2BUS$ General Electric

45. “At Motorola we use statistical methods daily throughout all of our disciplines to synthesize an abundance of data to derive concrete actions…. How has the use of statistical methods within Motorola Six Sigma initiative, across disciplines, contributed to our growth? Over the past decade we have reduced in-process defects by over 300 fold, which has resulted in a cumulative manufacturing cost savings of over 11 billion dollars”*. Robert W. Galvin Chairman of the Executive Committee Motorola, Inc. MOTOROLA *From the forward to MODERN INDUSTRIAL STATISTICS by Kenett and Zacks, Duxbury, 1998

46. “Six Sigma is making war on defects” Bill Smith, Motorola “If an employee is not enthusiastic about Six Sigma, GE is simply not the right company for that person” Jack Welch, General Electric “If all we have is spirit, we will lose to the US” President Idei, Sony Some more Quotes

47. Even more Quotes “Six-Sigma is remarkable – it has made managers start to adopt those simple and efficient methods that they have all needed desperately ever since they were developed back in the 1920s” Translated from Oystein Evandt (Norway) “Six-sigma’s focus on the bottom line provides the missing ingredient in Deming’s philosophy”

48. KnowledgeManagement The Six Sigma Initiative integrates these efforts

49. Black Belt training programs may include 6 sigma principles Quality Improvement Quality by Design Quality Control Teamwork Effective presentations QFD/VOC Statistical thinking Process mapping Barriers to breakthroughs JMP, MINITAB….. Gage R&R SPC SPC Strategy Risk Management FMEA Statistical Inference Design Of Experiments DOE Strategy Bootstrapping Robust Designs System Thinking

50. Barrier #1: Engineers and managers are not interested in mathematical statistics Barrier #2: Statisticians have problems communicating with managers and engineers Barrier #3: Non-statisticians experience “statistical anxiety” which has to be minimized before learning can take place Barrier # 4: Statistical methods need to be matched to management style and organizational culture Barriers to implementation

51. Technical Skills Soft Skills Statisticians Master Black Belts Black Belts Quality Improvement Facilitators BB MBB

52. Leadership Group Processes, internal and external customers Team 1Team 2 Team 3 BB BB BB MBB The 6 Sigma Project Structure KPA ISRU IBIS ENBIS CAMT

53. Comparing three recent developments in “Quality Management ” u ISO 9000 (-2000) u EFQM Model u Quality Improvement and Six Sigma Programs

54. ISO 9000 u Proponents claim that ISO 9000 is a general system for Quality Management u The de facto applications seem to be –an excessive emphasis on Quality Assurance, and –standardization of already existing systems with little attention to Quality Improvement u It would have been better if improvement efforts had preceded standardization

55. Critique of ISO 9000 u Bureaucratic, large scale u Focus on satisfying auditors, not customers u Certification is the goal; the job is done when certified u Little emphasis on improvement u The return on investment is not transparent u Main driver is: –We need ISO 9000 to become a certified supplier, –Not “we need to be the best and most cost effective supplier to win our customer’s business” u Corrupting influence on the quality profession

56. EFQM Model u A tool for assessment: Can measure where we are and how well we are doing u Assessment is a small piece of the bigger scheme of Quality Management: –Planning –Control –Improvement u EFQM provides a tool for assessment, but no tools, training, concepts and managerial approaches for improvement and planning

57. The “Success” of Change Programs? “Performance improvement efforts … have as much impact on operational and financial results as a ceremonial rain dance has on the weather” Schaffer and Thomson, Harvard Business Review (1992)

58. Change Management: Two Alternative Approaches Activity Based Programs Result Oriented Programs Change Management Reference: Schaffer and Thomson, HBR, Jan-Feb. 1992

59. Activity Centered Programs u Activity Centered Programs: The pursuit of activities that sound good, but contribute little to the bottom line u Assumption: If we carry out enough of the “right” activities, performance improvements will follow –This many people have been trained –This many companies have been certified u Bias Towards Orthodoxy: Weak or no empirical evidence to assess the relationship between efforts and results

60. No Checking with Empirical Evidence, No Learning Process ISO 9000 Data Hypothesis Deduction Induction

61. An Alternative: Result-Driven Improvement Programs u Result-Driven Programs: Focus on achieving specific, measurable, operational improvements within a few months u Examples of specific measurable goals: –Increase yield –Reduce delivery time –Increase inventory turns –Improved customer satisfaction –Reduce product development time

62. Result Oriented Programs: uProject based uExperimental uGuided by empirical evidence uMeasurable results uEasier to assess cause and effect uCascading strategy

63. Why Transformation Efforts Fail! u John Kotter, Professor, Harvard Business School u Leading scholar on Change Management u Lists 8 common errors in managing change, two of which are: 1.Not establishing a sense of urgency 2.Not systematically planning for and creating short term wins

64. Six Sigma Demystified* Six Sigma is TQM in disguise, but this time the focus is: –Alignment of customers, strategy, process and people –Significant measurable business results –Large scale deployment of advanced quality and statistical tools –Data based, quantitative *Adapted from Zinkgraf (1999), Sigma Breakthrough Technologies Inc., Austin, TX.

65. Keys to Success * u Set clear expectations for results u Measure the progress (metrics) u Manage for results *Adapted from Zinkgraf (1999), Sigma Breakthrough Technologies Inc., Austin, TX.

66. Six Sigma u The precise definition of Six Sigma is not important; the content of the program is u A disciplined quantitative approach for improvement of defined metrics u Can be applied to all business processes, manufacturing, finance and services

67. Focus of Six Sigma* u Accelerating fast breakthrough performance u Significant financial results in 4-8 months u Ensuring Six Sigma is an extension of the Corporate culture, not the program of the month u Results first, then culture change! * Adapted from Zinkgraf (1999), Sigma Breakthrough Technologies Inc., Austin, TX.

68. Six Sigma: Reasons for Success u The Success at Motorola, GE and AlliedSignal has been attributed to: –Strong leadership (Jack Welch, Larry Bossidy and Bob Galvin personally involved) –Initial focus on operations –Aggressive project selection (potential savings in cost of poor quality > $50,000/year) –Training the right people

69. The right way! u Plan for “quick wins” –Find good initial projects - fast wins u Establish resource structure –Make sure you know where it is u Publicise success –Often and continually - blow that trumpet u Embed the skills –Everyone owns successes

70. Roast Cool Grind Pack Coffee beans Sealed coffee Moisture content Savings: -Savings on rework and scrap -Water costs less than coffee Potential savings: 500 000 Euros Case study: project selection

71. Measure 1.Select the CTQ characteristic 2.Define performance standards 3.Validate measurement system Case study: Measure

72. Measure Moisture contents of roasted coffee 1. CTQ - Unit: one batch - Defect: Moisture% > 12.6% 2. Standards Case study: Measure

73. Gauge R&R study 3. Measurement reliability Measurement system too unreliable! Case study: Measure So fix it!!

74. Analyze 4. Establish product capability 5. Define performance objectives 6. Identify influence factors Case study: Analyze

75. USL Improvement opportunities

76. Diagnosis of problem

77. -Brainstorming -Exploratory data analysis 6. Identify factors MaterialMachineMan Method Measure- ment Mother Nature Amount of added water Roasting machines Batch size Reliability of Quadra Beam Weather conditions Moisture% Discovery of causes

78. Control chart for moisture% Discovery of causes

79. -Roasting machines (Nuisance variable) -Weather conditions (Nuisance variable) -Stagnations in the transport system (Disturbance) -Batch size (Nuisance variable) -Amount of added water (Control variable) Potential influence factors A case study

80. Improve 7. Screen potential causes 8. Discover variable relationships 9. Establish operating tolerances Case study: Improve

81. -Relation between humidity and moisture% not established -Effect of stagnations confirmed -Machine differences confirmed 7. Screen potential causes Design of Experiments (DoE) 8. Discover variable relationships Case study: Improve

82. Experiments are run based on:Intuition Knowledge Experience Power Emotions Possible settings for X 1 Possible settings for X 2 X: Settings with which an experiment is run. X X X X X X X Actually: we’re just trying unsystematical no design/plan How do we often conduct experiments? Experimentation

83. A systematical experiment: Organized / discipline One factor at a time Other factors kept constant Procedure: XXXXOXXXXX X: First vary X 1 ; X 2 is kept constant O: Optimal value for X 1. X: Vary X 2 ; X 1 is kept constant. : Optimal value (???) X X X X X X X Possible settings for X 1 Possible settings for X 2 Experimentation

84. One factor (X) low high X1X1 2 1 Two factors (X’s) low high X2X2 X1X1 2 2 Three factors (X’s) lowhigh X1X1 X3X3 X2X2 2 3 Design of Experiments (DoE)

85. Experiment: Y: moisture% X 1 : Water (liters) X 2 : Batch size (kg) A case study: Experiment

86. Feedback adjustments for influence of weather conditions A case study 9. Establish operating tolerances

87. A case study: feedback adjustments Moisture% without adjustments

88. A case study: feedback adjustments Moisture% with adjustments

89. Control 10. Validate measurement system (X’s) 11. Determine process capability 12. Implement process controls Case study: Control

90.  long-term < 0.280 Objective  long-term = 0.532 Before  long-term < 0.100 Result Results

91. Benefits of this project  long-term < 0.100 P pk = 1.5 This enables us to increase the mean to 12.1% Per 0.1% coffee: 100 000 Euros saving Benefits of this project: 1 100 000 Euros per year Benefits Approved by controller

92. -SPC control loop -Mistake proofing -Control plan -Audit schedule 12. Implement process controls Case study: control -Documentation of the results and data. -Results are reported to involved persons. -The follow-up is determined Project closure

93. -Step-by-step approach. -Constant testing and double checking. -No problem fixing, but: explanation  control. -Interaction of technical knowledge and experimentation methodology. -Good research enables intelligent decision making. -Knowing the financial impact made it easy to find priority for this project. Six Sigma approach to this project

94. Re-cap I! u Structured approach – roadmap u Systematic project-based improvement u Plan for “quick wins” –Find good initial projects - fast wins u Publicise success –Often and continually - blow that trumpet u Use modern tools and methods u Empirical evidence based improvement

95. Re-cap II! u DMAIC is a basic ‘training’ structure u Establish your resource structure –Make sure you know where external help is u Key ingredient is the support for projects - It’s the project that ‘wins’ not the training itself u Fit the training programme around the company needs – not the company around the training u Embed the skills –Everyone owns the successes