LECTURE 5-1

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LECTURE 5-1

  1. 1. PRINCIPLES OF SIX SIGMA ISE 412/512 LECTURE 5-1 MEASURE (DMAIC) THOMAS E. SCOTT, PhD
  2. 2. FUNDAMENTALS OF 6 σ SIPOC DMAIC Voice of the Customer Voice of the Process Cost of Quality COPQ DPMO Gage R&R DOE Show Me The Money $500K and 6 Months BELTS: YELLOW, GREEN, BLACK, MBB, CHAMPION, CEO USL LSL -6 σ 6 σ 20mm 22.5 25mm 27.5 30mm USL LSL VoP SIX SIGMA METHODOLOGY
  3. 3. 6 σ Problem Solving Methodology Existing Business Processes Supplier Inputs Processes Outputs Customer Quality Productivity Cost Profitability DMAIC <ul><li>Six Sigma Methodology </li></ul><ul><li>Define </li></ul><ul><li>Measure </li></ul><ul><li>Analyze </li></ul><ul><li>Improve </li></ul><ul><li>Control </li></ul>Improved Business Performance
  4. 4. Six Sigma Premise <ul><li>The variation of the process </li></ul><ul><li>(Voice of the Process) </li></ul><ul><li>can use up no more than </li></ul><ul><li>HALF </li></ul><ul><li>of the specification limit </li></ul><ul><li>(Voice of the Customer) </li></ul>
  5. 5. DEFINE ANALYZE CONTROL Six Sigma Problem Solving Methodology Existing Business Processes IMPROVED BUSINESS PERFORMANCE IMPROVE MEASURE What is important? How are we doing? What is wrong? What needs to be done? How do we guarantee performance?
  6. 6. DMAIC: Define <ul><li>All about the OUTPUT </li></ul><ul><ul><li>What is important to the customer? </li></ul></ul><ul><li>DEFINE </li></ul><ul><li>Scope and boundary </li></ul><ul><li>Specify Measurable Defects </li></ul><ul><li>Estimate the $ impact </li></ul>
  7. 7. DMAIC: Define <ul><li>Business case with a project objective </li></ul><ul><li>SIPOC analysis </li></ul><ul><li>Voice of Customer analysis </li></ul>
  8. 8. DMAIC: Measure <ul><li>All about the INPUT </li></ul><ul><ul><li>What things can we control? </li></ul></ul><ul><li>MEASURE </li></ul><ul><li>Map process </li></ul><ul><li>Identify inputs and outputs </li></ul><ul><li>Cause and Effects Matrix </li></ul><ul><li>Preliminary FMEA </li></ul><ul><li>Establish Measurement System Capability </li></ul><ul><li>Establish Process Capability Baseline </li></ul>
  9. 9. DMAIC: Measure <ul><li>Operationally define each CTQ characteristic </li></ul><ul><li>Understand the measurement system for each CTQ characteristic </li></ul><ul><li>Understand the current capability of each CTQ characteristic </li></ul>
  10. 10. GAGE R&R <ul><li>ESTIMATE THE PORTION OF TOTAL VARIATION RELATED TO </li></ul><ul><ul><li>UNIT-TO-UNIT VARIATION </li></ul></ul><ul><ul><li>R & R </li></ul></ul><ul><ul><ul><li>REPEATABILITY </li></ul></ul></ul><ul><ul><ul><li>REPRODUCIBILITY </li></ul></ul></ul><ul><ul><ul><li>OPERATOR-PART INTERACTION </li></ul></ul></ul><ul><ul><ul><ul><li>DIFFERENT PEOPLE MEASURE DIFFERENT UNITS DIFFERENT WAYS </li></ul></ul></ul></ul>
  11. 11. GAGE R&R – DATA COLLECTION <ul><li>COLLECT THE DATA USING THE FULL RANGE OF CONDITIONS EXPERIENCED BY THE MEASUREMENT SYSTEM </li></ul><ul><li>IN THE MEASURE PHASE, WE EVALUATE THE MEASUREMENT SYSTEM CAPABILITY SO WE CAN BE CERTAIN OF THE QUALITY OF THE DATA BEING OBTAINED </li></ul><ul><li>ANSWERS THE QUESTION “CAN THE DATA BE BELIEVED?” </li></ul>
  12. 12. GAGE R&R <ul><li>FOR EXAMPLE </li></ul><ul><ul><li>HAVE TWO INSPECTORS MEASURE FIVE ITEMS, FOUR DIFFERENT TIMES </li></ul></ul><ul><ul><li>2 x 5 x 4 = 40 MEASUREMENTS </li></ul></ul>
  13. 13. GAGE RUN STUDY <ul><li>GOOD REPEATABILITY – LOW VARIATION OF SQUARES </li></ul><ul><li>GOOD WITHIN GROUP REPEATABILITY </li></ul><ul><li>GOOD REPRODUCIBILITY </li></ul><ul><li>GOOD BETWEEN GROUP REPRODUCIBILITY </li></ul><ul><li>CONCLUSION: MOST OF THE VARIATION IS DUE TO UNIT DIFFERENCES </li></ul>
  14. 14. Gage R&R Study - ANOVA Method Two-Way ANOVA Table With Interaction Source DF SS MS F P Unit 4 17.6209 4.40522 2005.79 0.000 Inspector 1 0.1061 0.10609 48.31 0.002 Unit * Inspector 4 0.0088 0.00220 1.23 0.319 Repeatability 30 0.0536 0.00179 Total 39 17.7894 <ul><li>UNIT TO UNIT VARIATION MOST OF THE VARIATION (SIGNIFICANT) P = 0.000 </li></ul><ul><li>(NOTE: P IS THE OBSERVED LEVEL OF SIGNIFICANCE) </li></ul><ul><li>INSPECTOR TO INSPECTOR VARIATION SMALL BUT SIGNIFICANT P = 0.002 </li></ul><ul><li>INSPECTOR TO UNIT VARIATION SMALL AND NOT SIGNIFICANT P = 0.319 </li></ul><ul><li>BECAUSE OF THIS, NO NEED TO CONSIDER INTERACTION </li></ul>
  15. 15. UNIT TO UNIT VARIATION INSP TO INSP VARIATION INSP CROSS UNIT VARIATION
  16. 16. Two-Way ANOVA Table Without Interaction (WITHOUT INSPECTOR CROSS UNIT VARIATION) Source DF SS MS F P Unit 4 17.6209 4.40522 2400.86 0.000 Inspector 1 0.1061 0.10609 57.82 0.000 Repeatability 34 0.0624 0.00183 Total 39 17.7894 PART TO PART AND UNIT VARIATIONS ARE SIGNIFICANT
  17. 17. GAGE R&R ASSESSMENT <ul><li>Gage R&R (THE STUDY OF VARIATION) </li></ul><ul><li>%Contribution </li></ul><ul><li>Source VarComp (of VarComp) </li></ul><ul><li>Total Gage R&R 0.007048 1.26 </li></ul><ul><li>Repeatability 0.001835 0.33 </li></ul><ul><li>Reproducibility 0.005213 0.94 </li></ul><ul><li>Inspector 0.005213 0.94 </li></ul><ul><li>Part-To-Part 0.550423 98.74 </li></ul><ul><li>Total Variation 0.557471 100.00 </li></ul><ul><li>Study Var %Study Var </li></ul><ul><li>Source StdDev (SD) (6 * SD) (%SV) </li></ul><ul><li>Total Gage R&R 0.083950 0.50370 11.24 </li></ul><ul><li>Repeatability 0.042835 0.25701 5.74 </li></ul><ul><li>Reproducibility 0.072199 0.43320 9.67 </li></ul><ul><li>Inspector 0.072199 0.43320 9.67 </li></ul><ul><li>Part-To-Part 0.741905 4.45143 99.37 </li></ul><ul><li>Total Variation 0.746640 4.47984 100.00 </li></ul><ul><li>Number of Distinct Categories = 12 </li></ul><ul><li>A GOOD MEASUREMENT SYSTEM HAS A MAXIMUM GAGE R&R OF 10% </li></ul><ul><li>GAGE R&R OF 10% TO 30% ARE MARGINAL SYSTEMS </li></ul><ul><li>THEREFORE, THIS MEASUREMENT SYSTEM IS MARGINAL </li></ul>TOTAL EFFECT = 11.24%
  18. 18. GAGE R&R CONCLUSIONS <ul><li>UNIT TO UNIT VARIATION IS A LARGE PORTION OF TOTAL VARIATION </li></ul><ul><ul><li>THIS IS GOOD </li></ul></ul>
  19. 19. UNIT TO UNIT VARIATION IS A LARGE PORTION OF TOTAL VARIATION INSPECTORS ASSIGN THE SAME MEASURE TO THE SAME UNIT (REPRODUCIBILITY) SMALL RANGE IN REPEATED MEASUREMENT MEANS NARROW CONTROL LIMITS (A GOOD THING) SMALL VARIATION IN UNIT MEASUREMENT (A GOOD THING) EASY TO SEE THAT MOST VARIATION IS IN MEASUREMENT (A GOOD THING)
  20. 20. PAPER ORGANIZERS INTERNATIONAL <ul><li>OPERATIONAL DEFINITION OF BENDS </li></ul><ul><li>BEND THE METALLIC SECURING DEVICES (MSDs) </li></ul><ul><li>COLLECT DATA FOR </li></ul><ul><ul><li>WHERE THE MSD WAS OBTAINED </li></ul></ul><ul><ul><li>WHO THE INSPECTOR WAS </li></ul></ul><ul><ul><li>HOW MANY BENDS TO FAILURE </li></ul></ul>
  21. 21. GAGE R&R RUN CHART
  22. 22. YIELD <ul><li>BASED ON BEND TESTS (TABLE 16.18) </li></ul><ul><ul><li>DURABILITY </li></ul></ul><ul><ul><ul><li>6 GOOD OUT OF 16 TRYS (YIELD = .375) </li></ul></ul></ul><ul><ul><ul><li>CRITERIA WAS AT LEAST 4 BENDS </li></ul></ul></ul><ul><ul><li>FUNCTIONALITY </li></ul></ul><ul><ul><ul><li>6 BOXES OUT OF 16 ATTEMPTS (YIELD = .375) </li></ul></ul></ul><ul><ul><ul><li>CRITERIA WAS NO MORE THAN FIVE FAILURES PER BOX </li></ul></ul></ul>
  23. 23. INDIVIDUAL AND MOVING RANGE CHART WHOA! WHAT’S THIS?
  24. 24. DOT PLOT <ul><li>NOTICE THAT THE DATA IS NOT NORMALLY DISTRIBUTED! YIPES!! THEREFORE: </li></ul><ul><li>SHOULD NOT USE I & MR CHART AT THIS TIME </li></ul><ul><li>LOOKS LIKE POISSON, SO USE C-CHART </li></ul>
  25. 25. C-CHART (POISSON) YIPES! WHAT’S THIS? LOOKS LIKE MAYBE THE FIRST HOUR THERE WAS A PROBLEM. PERHAPS SLOWER BENDING.
  26. 26. C-CHART LOOKS GOOD!
  27. 27. POISSON DISTRIBUTED
  28. 28. PROCESS PERFORMANCE FOR CTQs <ul><li>CTQs </li></ul><ul><ul><li>DURABILITY </li></ul></ul><ul><ul><li>FUNCTIONALITY </li></ul></ul>100 FOLD IMPROVEMENT CONSISTENT WITH GOALS STATED IN THE DEFINE PHASE

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