Six Sigma: Point and Counterpoint

1. Six Sigma: Point and Counterpoint By Hans J. Bajaria Multiface, Inc. 6721 Merriman Road Garden City, Michigan 48135 1956 USA Phone: 734-421-6330 Fax: 734-421-1142 email: hbajaria@aol.com Web site: www.multiface.com Presented at ADCATS Conference BYU Provo, Utah June 15, 2000

2. 1 Interpretation from learning viewpoint Much talked about side of the coin Other side of the coin Interpretation from cultural viewpoint For Against

3. 2 Point Short-term PPM = 0.001 Long-term PPM = 3.4 (with 1.5 sigma shift) Counterpoint There is nothing outside 3 sigma.

4. 2 There is nothing outside ±3s. Reality Approximation -¥ -6s -5s -4s -3s -2s -1s 0 1s 2s 3s 4s 5s 6s ¥

5. 2 10 Deutsche Mark bill is a form of evidence that there is nothing outside of ±3s. ZEHN DEUTSCHE MARK f(x) 0.4 0.3 0.2 0.1 0 m-s m m+s

6. 2 Model PPM Reality PPM s value No. 1 +1 or -1 158,500 158,500 2 +2 or -2 23,000 23,000 3 +3 or -3 1,350 0 4 +4 or -4 31.5 0 5 +4.5 or -4.5 3.4 0

7. 2 • Supporting evidence: • Deming • Shewhart • Neave • Gauss • Wheeler

8. 2 What is the big deal? You cannot reflect achievement in terms of sigma level. Better option: Draw before and after picture.

9. 3 Point 1.5 sigma shift is assumed to compute 3.4 PPM of a six sigma process. Counterpoint Amount of shift and type of shift are a matter of discovery and not a matter of assumption. It is important to focus on target problem as well as variation problem.

10. 3 • Supporting evidence: • Target of a stamping process jumps around when a coil is changed. This jump is directly affects downstream quality. Partially used coils are returned to the supplier.

11. 3 Off-target: With every change in coil output target changes. Coil 1 Coil 2 Stamping Press Coil 3

12. 3 What is the big deal? The problem-solving process is desensitized if we allow for target shifts. Better option: Label target shift as a problem and solve it.

13. 4 Point Six sigma performance is said to be achieved when process variation is half that of specification range (Cp = 2) and target shift is 1.5 sigma. Counterpoint Taguchi: Performance is measured by uniformity around target with no reference to specification.

14. 4 • Supporting evidence: • Loss = K[s2 +(average - m)2]

15. 4 1.5s shift Loss = 3.25K -6 -3 3 6 Loss = 3.25K -6 -3 3 6 Specification range

16. 4 What is the big deal? Operational excellence efforts should be measured independent of specification range. Better option: Measure target performance first, variation performance then.

17. 5 Point PPM calculations associated with six sigma apply to attribute data as well as variable data. Counterpoint PPM calculations do not apply to attribute data.

18. Variable data 5 Strategy 2 Strategy 1 Move target Reduce variation Before After

19. 5 Attribute data Strategy 2 Strategy 1 Move target Reduce variation Deal with existing system Deal with a new system

20. 5 • Supporting evidence: • For variable data, target shift and variation are independent. Therefore, it is easier to choose a problem-solving tactic. • For attribute data, target shift and variation are dependent. Therefore, it is difficult to choose a problem-solving tactic.

21. 5 Example of how people play games with attribute data

22. 5 Electronic Board Manufacturer Process 90% board Good 10% board Bad 100,000 ppm

23. 5

24. 5 Electronic Board Manufacturer Process 2,000,000 joints Good 400 joints Bad 200 ppm

25. 5 What is the big deal? A strategic error could be made if distinction between variable data and attribute data is ignored. Better option Choose between improving existing system and developing a new system.

26. 6 Point Main emphasis is on reducing variation. Counterpoint Main emphasis should be on reducing variability, not on reducing variation. Variability has three components: instability, variation, and off-target. Once we surpass the 3 sigma quality, instability and off-target problem conditions become major contributors to variability.

27. 6 Problem condition: Instability (Operational disturbance) Current performance +20 -20 -8 +8 Six sigma quality range Specification range

28. 6 Problem condition: Excessive Variation (Lack of complete understanding) Current performance -20 +20 -15 +15 Six sigma quality range Specification range

29. 6 Problem condition: Off-target ( inability to perform task even at system’s best) Current performance -20 0 +20 -18 -2 Six sigma quality range Specification range

30. 6 Top paper Instability: Glue build up Bottom paper • Variation: • Temperature • Speed • Glue viscosity • Paper Moisture • Pressure GLUE BUILDUP ON THE ROLL

31. 6 • Supporting evidence: • Shewhart: It is almost impossible to reduce variation in the presence of instability.

32. 6 Reality Primary problem Model Primary problem s value No. Variation 1 +1 or -1 Variation Variation 2 +2 or -2 Variation V, I, O 3 +3 or -3 Variation V, I, O 4 +4 or -4 Variation I, O 5 +4.5 or -4.5 Variation I = Instability O = Off-target V = Variation

33. 6 What is the big deal? If variation is the only assumed component of variability, we could be actually working on the wrong problem condition. Better option Decompose variability in three parts. Choose the part to be resolved based on quality principles.

34. 7 Point More attention on reducing variation. Less attention on developing robustness. Counterpoint Robustness can altogether eliminate the need for reducing variation.

35. 7 We need engineering foundation to develop robustness. Part A Finding solution to this problem without buying new machines would mean “tolerance robust” design. Total Gap Part B Processes that make PART A and PART B are incapable of controlling TOTAL GAP within specified limits.

36. 7 Tolerance Robust Part A spring Total Gap Part B Use of spring as a forgiving mechanism made assembly insensitive to PART A and PART B tolerances.

37. 7 What is the big deal? Variation reduction is emphasized at the cost of other options. Better option Consider robustness as the first option. Consider variation reduction as a second option.

38. 8 Point Six sigma is a broader collection of methods. Counterpoint Reliability tools, multivariate tools, and observational studies receive lighter discussions.

39. 8 • Supporting evidence: • Reliability engineering • Supplier of clutch systems have a problem with leaky cylinders in the field. • The cylinders meet all manufacturing tests and all design tests before shipment. • The solution to this problem will require reliability engineering expertise.

40. 8 Supporting evidence: Multivariate tools Characteristic A of a part is good. Characteristic B of a part is also good. But the part itself is nonfunctional. The solution to this problem will require expertise in multivariate analysis.

41. 8 Supporting evidence: Observational studies Part A is good, Part B is good, but Parts A and B cannot be assembled. This problem can be investigated with observational studies.

42. 8 What is the big deal? Six sigma collection of methods is only a subset. Therefore, it can only address subset of problems. Better option Collection of methods must include a larger class to address problems from a bigger set.

43. 9 Point Six sigma can tackle transactional problems. Counterpoint How do you perform DOE to solve transactional problem? We deal with possible actions in transactional problems. We deal with investigative variables in design or manufacturing problems.

44. 9 Supporting evidence: WARD’s Auto World reports…… “One of the Big 3 is taking up to 120 days, and in some cases even longer, to pay for work delivered.” What statistical methods will you use to find the root cause?

45. 9 What is the big deal? Transactional Problems are solved by statistical thinking not by statistical methods. Better option Large number of situations require that we counter the problem rather than determine the root-cause.Methods must be inclusive of statistical thinking to deal with possible actions.

46. 10 Point Six sigma is a breakthrough strategy to solve a broader class of problems. Counterpoint Industry evils which have been gobbling lots of money are not even touched.

47. 10 • Supporting evidence: • Key characteristics: • Industry spends millions to control key characteristics. • Some key characteristics are at six sigma level, and yet performance is at less than three sigma level.

48. 10 • Supporting evidence: • Reliability: • Industry spends millions on testing. • Industry does long cycle durability tests. • What is most needed are short period reliability tests.

49. 10 • Supporting evidence: • SPC: • Industry continues to spend money using SPC for monitoring. • Diagnostic SPC is where money can be better spent.

50. 10 • Supporting evidence: • Process capability: • Industry sinks lots of money in absurdity. • Cpk ³ 1.33 is an absurd rule. • Cp -Cpk £ 0.33 and Cp ³ 1.33 is a better set of rules.