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  • 1. SIX SIGMA QUALITY TECHNIQUES... WHERE YOU NEED TO BE TO COMPETE IN THE NEW MILLENNIUM Michael W. Piczak Dipl.T., B.Comm., MBA
  • 2.  
  • 3. THE MAIN ELEMENTS
  • 4. DE FACTO, 6 SIGMA IS:
    • The search for and control of X’ s
  • 5. GOALS OF 6 SIGMA
    • Defect reduction
    • Yield improvement
    • Improved customer satisfaction
    • Higher net income
  • 6. WHERE TO FOCUS?
    • For each product or process critical to quality (CTQ):
    • Measure
    • Analyze
    • Improve
    • Control
  • 7. PRIMARY SOURCES OF VARIATION
    • Inadequate design margin
    • Unstable parts and material
    • Insufficient process capability
  • 8. WHO IS THE ENEMY?
    • VARIATION
  • 9. SELECTION OF RESPONSE VARIABLE (Y) CHOICE OF FACTORS (X i ’s), LEVELS, RANGES RECOGNITION OF & STATEMENT OF PROBLEM CHOICE OF EXPERIMENTAL DESIGN PERFORMING EXPERIMENT STATISTICAL ANALYSIS OF DATA CONCLUSIONS, RECOMMENDATIONS, NEXT STEPS
  • 10.  
  • 11. OUR BASIC RESEARCH PARADIGM
    • Enter data and editing same
    • Verify data integrity via Counts/Describe
    • Run Descriptives
    • Generate graphs & charts of data
    • Analyze ANOVAs
    • Run regressions, DOEs, GR&Rs
  • 12. PEDAGOGICAL APPROACH
    • Lecture
    • Discussion, debate and argument
    • Videos
    • Hands-on exercises using general and company specific examples
  • 13. TERMINAL PERFORMANCE OBJECTIVES
    • As a result of taking this program, the participant will be able to:
    • Appreciate the scope of 6 Sigma practices in context of other company initiatives
    • Apply a variety of tools to solve problems
  • 14. T.P.O.s CONTINUED...
    • Participate as a contributing member of a continuous improvement or problem solving team
    • Use Minitab as a data analysis tool
  • 15. GENESIS OF 6 SIGMA
  • 16. WHAT ARE WE REACHING FOR?
  • 17. ELEMENT 1
  • 18. PORTER’S 5 FORCES MODEL
  • 19. PEST MODEL
  • 20. ‘BONUS’ MODEL A key element
  • 21. VOICE OF THE CUSTOMER
    • 2 Brands of customers
        • internal
        • external
  • 22. ALL ON THE SAME PAGE Voice of the customer
  • 23. DESCRIBE THE PROCESS
  • 24. IMPROVING THE PROCESS
    • Elimination
    • Simplification
    • Combination
    • Reuse
    • Parallel processing
    • Subcontracting
  • 25. CRITICAL EXAMINATION
  • 26. NO NEW PROBLEMS PLEASE
    • Poka Yoke techniques
        • guide pins
        • templates
        • limit switches
        • limited computer screen fields
        • checklists
        • interconnects
  • 27. GETTING BETTER?
    • The need to measure in quantitative terms important
    • QS9000 demands it in terms of quality and effectiveness
        • customer satisfaction
        • quality levels (# non-conformances, dpu, dpmo)
        • cycle times
        • die change times
  • 28. ELEMENT 2: MEASUREMENT
  • 29. OLD METRICS
    • Measures of central tendency or typicality (mean, median, mode)
    • Measures of dispersion (range, variance, standard deviation)
  • 30. THE NORMAL DISTRIBUTION
  • 31. NORMAL CURVE CHARACTERISTICS
    • Continuous
    • Symmetrical
    • Tails asymptotic to zero
    • Bell shaped
    • Mean = median = mode
    • Total area under curve = 1
  • 32. A KEY FORMULA
  • 33. VARIATION IN PERSPECTIVE
    • ± 1 Sigma
    • ± 2 Sigma
    • ± 3 Sigma
    • ± 4 Sigma
    • ± 5 Sigma
    • ± 6 Sigma
    • ± ? Sigma
  • 34. VISUALIZING VARIATION
  • 35. THE HUNT FOR X
  • 36. FIXING BELIEF
    • Method of tenacity
    • Method of authority
    • Method of reasoning
    • Method of science
  • 37. THE SCIENTIFIC METHOD
  • 38. VISUALIZING VARIATION
  • 39.  
  • 40. PROCESS CAPABILITY
  • 41. PROCESS CAPABILITY II
  • 42. THE JOURNEY
    • Most companies presently at 3-4 sigma
    • The move is toward 6 sigma (Cp = 2)
    • Literature has references to 12 sigma (Cp = ?)
  • 43. Cpk
  • 44. HYDRAULIC LIFT COMPANY
    • See case on Page 37
  • 45. CAPABILITY ST & LT
  • 46. Cp LONG TERM (LT)
  • 47. ST to LT
  • 48. NEW METRICS
    • dpu
    • dmpo
    • THE CAVEAT
  • 49. Dpmo, Cp and Sigma
    • using page 608 Lindsay and Evans, derive figures shown
    • using page 48 Piczak, derive figures shown
  • 50. 2 ROADS TO PROFITABILITY
  • 51. COSTS OF QUALITY
  • 52. ELEMENT 3: QUALITY INITIATIVES
  • 53. SDWT’s
    • See Appendix G
  • 54. LITERATURE IDENTIFIED BENEFITS
      • Productivity  15% -250%
      • All employees can perform all tasks
      • Costs  30%
      • Cycle time  50%-90%
      • Inventory  66%
      • Rework due to engineering flaws  50%
  • 55. BENEFITS CONT’D
      • Late jobs  1000%
      • Quality 
      • Recurring defective product problems  10%
      • Return on investment/sales 
  • 56. BENEFITS CONT’D
      • Sales  830%
      • Operating statistics improved by 25-40%
      • Accounts receivable  from 66 days to 51 days
      • Corporate overhead  from $100M to $24M
      • Accidents  72%
  • 57. SHORT CYCLE MFG.
    • SMED
    • automated & computerized inspection
    • X and moving range control charts
    • automated systems (MAPs/CAD/CAM/flexible mfg., etc.)
    • flexible, self directed work force
  • 58. DFM
    • Group technology
    • accessibility of different parts & areas
    • ease of workpiece handling
    • ergonomic principles
    • safety requirements
    • appearance
    • QFD
  • 59. BENCHMARKING
    • more than just organized tourism
    • more than just a nice walk over at a friend’s plant
    • not industrial espionage
    • not a one way channel of communication
  • 60. THE ALCOA SEQUENCE
  • 61. SPC
    • using numbers to describe absence or presence of a phenomenon
    • systematic gathering of data
    • using a collection of analytics that promote common understanding
    • emphasis is on measurement
  • 62. STATISTICS
    • Collecting
    • Organizing
    • Summarizing
    • Analyzing
    • Presenting
  • 63. THE ANALYST’S DUTY
    • Start with a regularity, uniformity or curiosity
    • identify all previously significant predictors of phemon in question
    • theorize as to why independent variables (X’s) should be predictive of dependent variables (Y)
  • 64.
    • construct conceptual model of hypothesized relationships
    • set out research question(s) clearly
    • gather data
    • organize same into spread/worksheet
    • run full model followed by reduced form
    • draw conclusions/rec’s and share same
  • 65.  
  • 66. 3 KINDS OF STATISTICS
    • Descriptive (p. 71)
    • Inferential
    • Predictive
  • 67. NASA DATA & REGRESSION LINE
  • 68.  
  • 69. DATA TYPES
    • Discrete
    • Continuous
  • 70. CHART TYPES
  • 71. CHART TYPES
        • X Bar and R charts
        • X and Moving Range charts
        • p charts
        • c charts and
        • u charts
  • 72. CONTROL LIMITS FOR X BAR & R CHARTS
      • Upper control limit (UCL  )= x double bar + Z  
      • Lower control limit (LCL  ) = x double bar - Z  
  • 73. OR
  • 74. FOR R
  • 75. X & MOVING RANGE CHARTS
  • 76. PLOTTING R
  • 77. PLOTTING X
  • 78. P CHARTS
  • 79. AN EXAMPLE P. 102
  • 80. A SUMMARY TABLE OF FORMULAS
  • 81. INTERPRETING CHARTS
    • Examining patterns to make rational decisions
    • Using patterns puts the odds of making a good decision on your side
    • Can make two good decisions and two bad decisions
  • 82. U CAN BE RIGHT, U CAN BE WRONG
  • 83. PATTERN ANALYSIS FIG. 41
  • 84. CHANGE OR JUMP IN LEVEL
  • 85. RECURRING CYCLES F. 43
  • 86. TREND OR STEADY CHANGE IN LEVEL
  • 87. NO BRAINERS
  • 88. 50% ABOVE/BELOW MEAN
  • 89. 6 POINT RUN
  • 90. CYCLICAL PATTERN
  • 91. CYCLICAL PATTERN
  • 92. SHORT TERM TREND WITH ADJUSTMENT
  • 93. 68% WITHIN 1 SIGMA
  • 94. SYSTEMATIC CAUSES OF VARIATION
    • Lack of preventative maintenance
    • Worn tools
    • Operator performance
    • Differentials
    • Environmental changes
    • Sorting practices
  • 95.