I NDUSTRIAL S TATISTICS R ESEARCH U NIT

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

2. 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

3. 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

4. Six-Sigma Basics

5. 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’

6. Emperical Approach 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”

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

8. Using statistics can integrate all of these issues SPC Improvement teams Problem Solving teams Strategic planning Knowledge Management ISO 9000 DOE Benchmarking and more

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

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

11. Improvement cycle • PDCA cycle Plan Act Do Check

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

13. Scientific method (after Box)

14. 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)

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

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

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

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

19. Six-Sigma by Day Release The Plan

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35. 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

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

37. 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

38. Six-Sigma Case study

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

40. Case study: Measure • Select the Critical to Quality (CTQ) characteristic • Define performance standards • Validate measurement system

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

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

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

44. USL USL Improvement opportunities

45. Diagnosis of problem

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

47. Discovery of causes Control chart for moisture%

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

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

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