1. OMSAN LOJİSTİK

2. Top Management Program in Logistics & Supply Chain Management (TMPLSM) Production and Operations Management 7: Improving and Monitoring the Process

3. 3 Statistical Quality Control Methods Our Focus

4. 4 A few tools for Assessing process capability Monitoring process performance Improving process performance

5. 5 Small Large Easy / Direct Different processes need different approaches Difficult / Indirect New Products Volume Measurement Law Suits New Business Dev’t. Volume Production (Coca Cola) Advertisements Let’s focus first on these types of processes

6. 6 Process Capability (C p ) “Six Sigma companies aim for C p = 2” What does that mean?

7. 7 Separate Product from Process Product Process First fundamental question: Is the process capable to produce the product?

8. 8 Let’s look at a simple example We produce beer in “12 Oz” bottles Anything between 11.4 oz and 12.6 oz is acceptable Design Specs 11.4 oz12.6 oz12.0 Oz LSLUSL Is our filling process capable to meet these specs?

9. 9 Which line is capable to meet these design specs? Assume we have three filling lines 11.4 oz12.6 oz12.0 Oz Line 1 Line 2 Line 3

10. 10 We must first answer another question: How many “out of spec” bottles are acceptable? 5%? 0.3% (or 3 ppt)? 3.4 ppm? [Or how many “sigmas?”] 11.4 oz12.6 oz12.0 Oz Line 1 Line 2 Line 3

11. 11 A Quick Refresher! Sigma -Measure of Variability σ 1σ 2.5 % ±2 σ: 95 % 3σ 0.15 % ± 3σ: 99.7 %

12. 12 A “3 σ” Process Produces Only 0.3% Out-of-Spec 3σ 0.15 % 11.4 OZ12.O OZ12.6 OZ XX 99.7 %

13. 13 If we accept a “3 σ policy”, then lines 2 and 3 seem to be good. 11.4 oz12.6 oz12.0 Oz Line 1 Line 2 Line 3 XX Variability (or Sigma) of Line 1 is too big Line 2 seems just right for getting 99.7 % good bottles Line 3 seems capable of doing better than 99.7 % good bottles Variability (or Sigma) of Line 1 is too big Line 2 seems just right for getting 99.7 % good bottles Line 3 seems capable of doing better than 99.7 % good bottles

14. 14 “Cp” A measure for Process Capability Cp = Acceptable range of design specs / 2 * 3 σ Assume σ for Line 1 =.3 oz Line 2 = 0.2 oz Line 3 = 0.1 oz Cp of Line 1 = (12.6-11.4) / 2*3*0.3 = 0.67 (“2 σ capability”) Cp of Line 2 = (12.6-11.4) / 2*3*0.2 = 1 (“3 σ capability” ) Cp of Line 3 = (12.6-11.4) / 2*3*0.1 = 2 (“6 σ capability”)

15. 15 A “6 σ process” has a Cp=2 It is capable of producing less than 3.4 ppm defects 6σ 1.7 ppm 11.4 OZ12.O OZ12.6 OZ XX “3 σ Process” Almost 1000 times better than “3 σ” !

16. 16 So The journey towards improving process capability (and eventually Six Sigma) starts with reduction of process variability

17. 17 We must also watch for the drift of the process mean But achieving the desired Cp is necessary but not sufficient 11.4 oz12.6 oz12.0 Oz

18. 18 12.0 Oz XX LSL USL C pk A better measure of process capability

19. 19 12.3 Oz XX LSL=11.4 oz USL=12.6 oz An Example Assume the mean in line 3 (s=0.1) has shifted to 12.3 oz. What is its C pk ?

20. 20

21. 21 A Quick refresher on Control Charts

22. 22 X-Bar Chart An Example Take a sample of 4 bottles every hour For example, at 9 a.m.: 12.5 oz 12.3 oz 11.9 oz 11.7 oz Average 12.1 oz 12.0 oz 12.5 oz 11.5 oz 9 a.m.10 a.m.

23. 23 12.0 oz 12.5 oz 11.5 oz 9 a.m.1011Noon1234 p.m. Did the process mean drifted too much today? Remember: These are averages of 4 bottles

24. 24 9 a.m.1011Noon1234 p.m. X= 12.0 oz = To make it clearer, let’s set upper and lower control limits Lower Control Limit Upper Control Limit ? ? But Where?

25. 25 The answer depends on how often you want to raise a flag 9 a.m.1011Noon1234 p.m. X= 12.0 oz = Lower Control Limit Upper Control Limit ? ? Should you have raised a flag at 1 p.m? 2 p.m? 3 p.m? Were they “abnormal”? ? ?

26. 26 You decide what should be regarded as “normal” and “abnormal” 9 a.m.1011Noon1234 p.m. X= 12.0 oz = UCL LCL “Abnormal”: Do Something! Statistical tools can help you set UCLs and LCLs but they don’t make the managerial decision for you

27. 27 Control Chart Evidence for Investigation Source: Bertrand L. Hansen, Quality Control: Theory and Applications, © 1963, p. 65. Reprinted by permission of Prentice Hall, Inc., Englewood Cliffs, NJ.

28. 28 Control Chart Evidence for Investigation (cont’d) Source: Bertrand L. Hansen, Quality Control: Theory and Applications, © 1963, p. 65. Reprinted by permission of Prentice Hall, Inc., Englewood Cliffs, NJ.

29. 29 X-Bar chart tracks the process mean How do we track process variability?

30. 30 Construct a Range (R) Chart Go back to the sample of 4 bottles every hour 12.5 oz 12.3 oz 11.9 oz 11.7 oz Range: 12.5-11.7= 0.8 oz 0.6 oz 1.0 oz 0.2 oz 9 a.m.10 a.m.

31. 31 0.6 oz 1.0 oz 0.2 oz 9 a.m.1011Noon1234 p.m. Did the variability of the process change too much today? Range Sample of 4

32. 32 UCL LCL 9 a.m.1011Noon1234 p.m. Again, Let’s Set Control Limits Range Sample of 4 R - What went right?!

33. 33 UCL LCL 9 a.m.1011Noon1234 p.m. Range Sample of 4 R - 9 a.m.1011Noon1234 p.m. X= 12.0 oz = UCL LCL Now read the X-bar and R charts together

34. 34 3 LCL UCL _ R 9102111124 p.m. LCL UCL = X 9102111124 p.m. 3 When was the best performance? When was the worst performance? Any hypotheses for what happened during this shift? And the next day...

35. 35

36. 36 Basic recipe for continuous improvement of processes: Narrow the range Then adjust the mean

37. 37 Taguchi: “Beat the design specs!” Design Specs LSLUSLMean Costs High Low Because the real cost function is not like this

38. 38 It is like this: Design Specs LSLUSLMean Costs High Low It pays to reduce the variability -- even if you are within design specs

39. 39 Cause-and-Effect Diagram for Customer Complaints in a Restaurant

40. 40 Pareto Chart of Factors in an Emergency Room

41. 41 Remember SPC offers simple and valuable tools for tracking critical variables in repetitive manufacturing or service delivery processes Small Large Easy / DirectDifficult / Indirect New Products Volume Measurement Law Suits New Business Dev’t. Volume Production (Coca Cola) Advertisements Don’t push them too hard in other situations!