about a great management tool

1. What is Six Sigma?

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

5. DOE SPC Knowledge Management Benchmarking The Six Sigma Initiative integrates these efforts Improvement teams Problem Solving teams ISO 9000 Strategic planning and more

9. “ 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 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

12. 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

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

19. The Six Sigma metric

22. Scientific method (after Box)

24. Prioritise (D) Measure (M) Interpret (D/M/A) Problem (D/M/A) solve Improve (I) Hold gains (C) Alternative interpretation

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

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

27.        L S L U S L Statistical background Required Tolerance Target = 

28.             L S L U S L Statistical background Tolerance Target =  Six-Sigma

29.             L S L U S L p p m 1 3 5 0 p p m 1 3 5 0 Statistical background Tolerance Target = 

30.             L S L U S L p p m 0 . 0 0 1 p p m 1 3 5 0 p p m 1 3 5 0 p p m 0 . 0 0 1 Statistical background Tolerance Target = 

32. L S L 0 p p m p p m 3 . 4     U S L p p m 3 . 4 p p m 6 6 8 0 3        Statistical background Tolerance

33. Performance Standards 2 3 4 5 6 308537 66807 6210 233 3.4  PPM 69.1% 93.3% 99.38% 99.977% 99.9997% Yield Process performance Defects per million Long term yield Current standard World Class

34. Number of processes 3 σ 4 σ 5 σ 6 σ 1 10 100 500 1000 2000 2955 93.32 50.09 0.1 0 0 0 0 99.379 93.96 53.64 4.44 0.2 0 0 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 First Time Yield in multiple stage process Performance standards

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

36. Six Sigma and other Quality programmes

41. 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)

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

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

50. Key personnel in successful Six Sigma programmes

57. Six Sigma training package

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

60. Define Throughput time project 4 months (full time) Example of a Classic Training strategy Training (1 week) Work on project (3 weeks) Review Measure Analyze Improve Control

62. Draft training schedule

64. 5 weeks of training Measure Analyze Improve Control Define

75. Example - SPC (Statistical Process Control) - reduces variability and keeps the process stable Disturbed process Natural process Temporary upsets Natural boundary Natural boundary

78. Black Belt Training Application Review ISRU ISRU, Champion ISRU, Champion Project execution

80. The right support + The right projects + The right people + The right tools + The right plan = The right results

85. Key Quality Characteristics “CTQs” How will you measure them? How often? Who will measure? Is the outcome critical or important to results?

86. Outcome Examples Reduce defective parts per million Increased capacity or yield Improved quality Reduced re-work or scrap Faster throughput

87. Key Questions Is this a new product - process? Yes - then potential six-sigma Do you know how best to run a process? No - then potential six-sigma

88. Key Criteria Is the potential gain enough - e.g. - saving > $50,000 per annum? Can you do this within 3-4 months? Will results be usable? Is this the most important issue at the moment?

89. Why is ISRU an effective Six Sigma practitioner?

91. I NDUSTRIAL S TATISTICS R ESEARCH U NIT We are based in the School of Mechanical and Systems Engineering, University of Newcastle upon Tyne, England

92. Mission statement " To promote the effective and widespread use of statistical methods throughout European industry. "

102. Distance Learning Facility

105. Case study

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

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

111. Improvement opportunities USL USL

112. Diagnosis of problem

114. Control chart for moisture% Discovery of causes

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

119. A systematical experiment: Organized / discipline One factor at a time Other factors kept constant Procedure: X X X X O X X X X X 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

120. Design of Experiments (DoE) One factor (X) low high X 1 2 1 Two factors (X’ s ) low high high X 2 X 1 2 2 high Three factors (X’ s ) low high X 1 X 3 X 2 2 3

121. Advantages of multi-factor over one-factor

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

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

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

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

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

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

128. 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

133. ENBIS All joint authors - presenters - are members of: Pro-Enbis or 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