2. INDUSTRIAL STATISTICS RESEARCH UNIT We are based in the School of Mechanical and Systems Engineering, University of Newcastle upon Tyne, England

3. Our work we do can be broken down into 3 main categories: Consultancy Training Major Research Projects All with the common goal of promoting quality improvement by implementing statistical techniques

4. European Research Projects The Unit has provided the statistical input into many major European projects - Examples include - Assessing steel rail reliability Testing fire-fighter’s boots for safety Calibsensory - Effect on food of the taints and odours in packaging materials Pro-Enbis - Network of Six-Sigma and other statistical practitioners around Europe Kensys - Kansei Engineering

5. Six-Sigma Basics

6. Basics Effective application of statistical tools within a structured methodology Repeated application of Continuous Improvement strategy to individual projects Projects deliberately selected to have a substantial impact on the ‘bottom line’

7. 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” Emperical Approach

8. Statistical Methods Production Design Service Purchase HRM Administration Quality Depart. Management M & S IT Where can Statistical techniques be applied?

9. KnowledgeManagement Using statistics can integrate all of these issues

10. Technical Skills Soft Skills Statisticians Advanced course delegates Course delegates Quality Improvement Facilitators CD ACD

11. Focus Accelerating fast breakthrough performance Significant financial results in 4-8 months Results first, then change!

12. 11 Improvement cycle PDCA cycle Plan Do Check Act

13. Prioritise (Define) Measure (M) Interpret (Analyse) Problem solve (Analyse - Improve) Improve (I) Hold gains (Control) Alternative interpretation (Six Sigma structure)

14. Scientific method (after Box)

15. 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)

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

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

18. Result Oriented Programs Project based Experimental Guided by empirical evidence Measurable results Easier to assess cause and effect Cascading strategy

19. ISRU Training Open and in-House courses Six-Sigma TPM Distance Learning Essentially a further learning resource for statistical tools and methodology

20. Six-Sigma by Day Release The Plan

21. Plan

29. Plan 2

36. Different Belts Yellow Belts - 5 days –days - 1 - 3 - 8 - 12 - 17 Green Belts - 10 days –days - 1 - 2 - 3 - 4 - 8 - 9 - 12 - 13 - 17 - 18 Black belts - 20 days

37. Additional Project Support Project support - on-going Yellow Belts - 1 day Green Belts - 2 days Black-Belts - 4 days

38. Project Funding SMEs - Training free! Just pay £150 per delegate per support day - green belts = £300 for complete course. Non-SMEs - half usual cost –e.g. green belt is £1,200 per man - by day release in this way. End

39. Six-Sigma Case study

40. 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

41. 1.Select the Critical to Quality (CTQ) characteristic 2.Define performance standards 3.Validate measurement system Case study: Measure

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

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

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

45. USL Improvement opportunities

46. Diagnosis of problem

47. -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

48. Control chart for moisture% Discovery of causes

49. -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

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

51. -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

52. 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

53. 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

54. 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)

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

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

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

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

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

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

61. 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

62. -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

63. -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. Approach to this project

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