2. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 1 Chapter 7 Six Sigma Quality and Statistical Process Control

3. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. Definition: Total Quality Management Total Quality Management (TQ, QM or TQM) and Six Sigma (6  ) are sweeping “culture change” efforts to position a company for greater customer satisfaction, profitability and competitiveness. TQ may be defined as managing the entire organization so that it excels on all dimensions of products and services that are important to the customer.

4. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 3 Meeting Our Customer’s Requirements Doing Things Right the First Time; Freedom from Failure (Defects) Consistency (Reduction in Variation) Continuous Improvement Quality in Everything We Do Total Quality Is…

5. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 4 Measurement Empowerment/ Shared Leadership Process Improvement/ Problem Solving Team Management Customer Satisfaction Business Results The Continuous Improvement Process...

6. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 5 Is 99% Quality Good Enough? 22,000 checks will be deducted from the wrong bank accounts in the next 60 minutes. 20,000 incorrect drug prescriptions will be written in the next 12 months. 12 babies will be given to the wrong parents each day.

7. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 6 But is Six Sigma Realistic? · 10 1 100 1K 10K 100K 765432 (66810 ppm) · IRS – Tax Advice (phone-in) Best in Class (3.4 ppm) Domestic Airline Flight Fatality Rate (0.43 ppm) · (233 ppm) Average Company Purchased Material Lot Reject Rate Air Line Baggage Handling Wire Transfers Journal Vouchers Order Write-up Payroll Processing Doctor Prescription Writing Restaurant Bills · · · · · · · Defects Per Million Opportunities (DPMO) SIGMA

8. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 7 Six Sigma Quality The objective of Six Sigma quality is 3.4 defects per million opportunities!

9. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 8 Statistical Process Control Take periodic samples from processTake periodic samples from process Plot sample points on control chartPlot sample points on control chart Determine if process is within limitsDetermine if process is within limits Prevent quality problemsPrevent quality problems UCL LCL

10. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 9 Variation Common Causes Common Causes Variation inherent in a process Variation inherent in a process Can be eliminated only through improvements in the system Can be eliminated only through improvements in the system Special Causes Special Causes Variation due to identifiable factors Variation due to identifiable factors Can be modified through operator or management action Can be modified through operator or management action

11. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 10 Types of Data Attribute data Attribute data Product characteristic evaluated with a discrete choice Product characteristic evaluated with a discrete choice Good/bad, yes/no Good/bad, yes/no Variable data Variable data Product characteristic that can be measured Product characteristic that can be measured Length, size, weight, height, time, velocity Length, size, weight, height, time, velocity

12. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 11 SPC Applied to Services Nature of defect is different in services Nature of defect is different in services Service defect is a failure to meet customer requirements Service defect is a failure to meet customer requirements Monitor times, customer satisfaction Monitor times, customer satisfaction

13. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 12 Service Quality Examples Hospitals Hospitals Timeliness, responsiveness, accuracy of lab tests Timeliness, responsiveness, accuracy of lab tests Grocery Stores Grocery Stores Check-out time, stocking, cleanliness Check-out time, stocking, cleanliness Airlines Airlines Luggage handling, waiting times, courtesy Luggage handling, waiting times, courtesy Fast food restaurants Fast food restaurants Waiting times, food quality, cleanliness, employee courtesy Waiting times, food quality, cleanliness, employee courtesy

14. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 13 Service Quality Examples Catalog-order companies Catalog-order companies Order accuracy, operator knowledge and courtesy, packaging, delivery time, phone order waiting time Order accuracy, operator knowledge and courtesy, packaging, delivery time, phone order waiting time Insurance companies Insurance companies Billing accuracy, timeliness of claims processing, agent availability and response time Billing accuracy, timeliness of claims processing, agent availability and response time

15. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 14 Control Charts Graph establishing process control limits Graph establishing process control limits Charts for variables Charts for variables Mean (x-bar), Range (R) Mean (x-bar), Range (R) Chart for attributes Chart for attributes P Chart P Chart C Chart C Chart

16. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 15 Process Control Chart 12345678910 Sample number Uppercontrollimit Processaverage Lowercontrollimit Out of control Figure 15.1

17. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 16 A Process is In Control if 1.No sample points outside limits 2.Most points near process average 3.About equal number of points above & below centerline 4.Points appear randomly distributed

18. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 17 Development of Control Chart Based on in-control data Based on in-control data If non-random causes present, find the special cause and discard data If non-random causes present, find the special cause and discard data Correct control chart limits Correct control chart limits

19. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 18 Control Chart for Attributes p Charts p Charts Calculate percent defectives in sample Calculate percent defectives in sample

20. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 19 p-Chart UCL = p + z  p LCL = p - z  p where z=the number of standard deviations from the process average p=the sample proportion defective; an estimate of the process average  p =the standard deviation of the sample proportion p =p =p =p = p(1 - p) n

21. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 20 The Normal Distribution  =0 1111 2222 3333 -1  -2  -3  95% 99.74%

22. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 21 Control Chart Z Values Smaller Z values make more sensitive charts Smaller Z values make more sensitive charts Z = 3.00 is standard Z = 3.00 is standard Compromise between sensitivity and errors Compromise between sensitivity and errors

23. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 22 p-Chart Example 20 samples of 100 pairs of jeans NUMBER OFPROPORTION SAMPLEDEFECTIVESDEFECTIVE 16.06 20.00 34.04 ::: 2018.18 200 Example 15.1

24. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 23 p-Chart Example 20 samples of 100 pairs of jeans NUMBER OFPROPORTION SAMPLEDEFECTIVESDEFECTIVE 16.06 20.00 34.04 ::: 2018.18 200 Example 15.1 p= = 200 / 20(100) = 0.10 total defectives total sample observations

25. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 24 p-Chart Example 20 samples of 100 pairs of jeans NUMBER OFPROPORTION SAMPLEDEFECTIVESDEFECTIVE 16.06 20.00 34.04 ::: 2018.18 200 Example 15.1 p = 0.10 UCL = p + z = 0.10 + 3 p(1 - p) n 0.10(1 - 0.10) 100 UCL = 0.190 LCL = 0.010 LCL = p - z = 0.10 - 3 p(1 - p) n 0.10(1 - 0.10) 100

26. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 25 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.180.20 Proportion defective Sample number 2468101214161820 UCL = 0.190 LCL = 0.010 p = 0.10 p-Chart

27. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 26 C Chart Used when you can’t calculate a proportion defective and an actual count is used. Key –the number of defects is assumed to come from a large population Ex. Defects in the paint job of a car

28. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 27 C Chart con’t The mean is the average counted number of defects per item (total divided number of samples The sample standard deviation is √c bar (square root of the mean of C)

29. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 28 Control Charts for Variables Mean chart ( x -Chart ) Mean chart ( x -Chart ) Uses average of a sample Uses average of a sample Range chart ( R-Chart ) Range chart ( R-Chart ) Uses amount of dispersion in a sample Uses amount of dispersion in a sample

30. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 29 Range ( R- ) Chart UCL = D 4 RLCL = D 3 R R =R =R =R = RRkkRRkkk where R= range of each sample k= number of samples

31. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 30 Range ( R- ) Chart nA2D3D4nA2D3D4 SAMPLE SIZEFACTOR FOR x-CHARTFACTORS FOR R-CHART 21.880.003.27 31.020.002.57 40.730.002.28 50.580.002.11 60.480.002.00 70.420.081.92 80.370.141.86 90.440.181.82 100.110.221.78 110.990.261.74 120.770.281.72 130.550.311.69 140.440.331.67 150.220.351.65 160.110.361.64 170.000.381.62 180.990.391.61 190.990.401.61 200.880.411.59 Table 15.1

32. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 31 R-Chart Example OBSERVATIONS (SLIP-RING DIAMETER, CM) SAMPLE k 12345xR 15.025.014.944.994.964.980.08 25.015.035.074.954.965.000.12 34.995.004.934.924.994.970.08 45.034.915.014.984.894.960.14 54.954.925.035.055.014.990.13 64.975.065.064.965.035.010.10 75.055.015.104.964.995.020.14 85.095.105.004.995.085.050.11 95.145.104.995.085.095.080.15 105.014.985.085.074.995.030.10 50.091.15 Example 15.3

33. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 32 R-Chart Example OBSERVATIONS (SLIP-RING DIAMETER, CM) SAMPLE k 12345xR 15.025.014.944.994.964.980.08 25.015.035.074.954.965.000.12 34.995.004.934.924.994.970.08 45.034.915.014.984.894.960.14 54.954.925.035.055.014.990.13 64.975.065.064.965.035.010.10 75.055.015.104.964.995.020.14 85.095.105.004.995.085.050.11 95.145.104.995.085.095.080.15 105.014.985.085.074.995.030.10 50.091.15 Example 15.3 RkRk R = = = 0.115 1.15 10 UCL = D 4 R = 2.11(0.115) = 0.243 LCL = D 3 R = 0(0.115) = 0 UCL = 0.243 LCL = 0 Range Sample number R = 0.115 |1|1 |2|2 |3|3 |4|4 |5|5 |6|6 |7|7 |8|8 |9|9 | 10 0.28 – 0.24 – 0.20 – 0.16 – 0.12 – 0.08 – 0.04 – 0 –

34. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 33 x-Chart Calculations x =x =x =x = x 1 + x 2 +... x k k= UCL = x + A 2 RLCL = x - A 2 R == where x= the average of the sample means =

35. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 34 x-Chart Example Example 15.4 OBSERVATIONS (SLIP-RING DIAMETER, CM) SAMPLE k 12345xR 15.025.014.944.994.964.980.08 25.015.035.074.954.965.000.12 34.995.004.934.924.994.970.08 45.034.915.014.984.894.960.14 54.954.925.035.055.014.990.13 64.975.065.064.965.035.010.10 75.055.015.104.964.995.020.14 85.095.105.004.995.085.050.11 95.145.104.995.085.095.080.15 105.014.985.085.074.995.030.10 50.091.15 UCL = x + A 2 R = 5.01 + (0.58)(0.115) = 5.08 LCL = x - A 2 R = 5.01 - (0.58)(0.115) = 4.94 = = x = = = 5.01 cm = xkxk 50.09 10

36. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 35 x-Chart Example Example 15.4 OBSERVATIONS (SLIP-RING DIAMETER, CM) SAMPLE k 12345xR 15.025.014.944.994.964.980.08 25.015.035.074.954.965.000.12 34.995.004.934.924.994.970.08 45.034.915.014.984.894.960.14 54.954.925.035.055.014.990.13 64.975.065.064.965.035.010.10 75.055.015.104.964.995.020.14 85.095.105.004.995.085.050.11 95.145.104.995.085.095.080.15 105.014.985.085.074.995.030.10 50.091.15 UCL = x + A 2 R = 5.01 + (0.58)(0.115) = 5.08 LCL = x - A 2 R = 5.01 - (0.58)(0.115) = 4.94 = = x = = = 5.01 cm = xkxk 50.09 10 UCL = 5.08 LCL = 4.94 Mean Sample number |1|1 |2|2 |3|3 |4|4 |5|5 |6|6 |7|7 |8|8 |9|9 | 10 5.10 – 5.08 – 5.06 – 5.04 – 5.02 – 5.00 – 4.98 – 4.96 – 4.94 – 4.92 – x = 5.01 =

37. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 36 Using x- and R-Charts Together Each measures the process differently Each measures the process differently Both process average and variability must be in control Both process average and variability must be in control

38. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 37 Sample Size Determination Attribute control charts Attribute control charts 50 to 100 parts in a sample 50 to 100 parts in a sample Variable control charts Variable control charts 2 to 10 parts in a sample 2 to 10 parts in a sample

39. Process Capability Process limits (The “Voice of the Process” or The “Voice of the Data”) - based on natural (common cause) variation Tolerance limits (The “Voice of the Customer”) – customer requirements Process Capability – A measure of how “capable” the process is to meet customer requirements; compares process limits to tolerance limits

40. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 39 Process Capability Range of natural variability in process Range of natural variability in process Measured with control charts. Measured with control charts. Process cannot meet specifications if natural variability exceeds tolerances Process cannot meet specifications if natural variability exceeds tolerances 3-sigma quality 3-sigma quality Specifications equal the process control limits. Specifications equal the process control limits. 6-sigma quality 6-sigma quality Specifications twice as large as control limits Specifications twice as large as control limits

41. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 40 Process Capability (b) Design specifications and natural variation the same; process is capable of meeting specifications most the time. Design Specifications Process (a) Natural variation exceeds design specifications; process is not capable of meeting specifications all the time. Design Specifications Process Figure 15.5

42. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 41 Process Capability Figure 15.5 (c) Design specifications greater than natural variation; process is capable of always conforming to specifications. Design Specifications Process (d) Specifications greater than natural variation, but process off center; capable but some output will not meet upper specification. Design Specifications Process

43. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 42 Process Capability Measures Process Capability Index C pk = minimum x - lower specification limit 3  = upper specification limit - x 3  =,

44. To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. 43 Computing C pk Net weight specification = 9.0 oz  0.5 oz Process mean = 8.80 oz Process standard deviation = 0.12 oz C pk = minimum = minimum, = 0.83 x - lower specification limit 3  = upper specification limit - x 3  =, 8.80 - 8.50 3(0.12) 9.50 - 8.80 3(0.12) Example 15.7

45. Interpreting the Process Capability Index C pk < 1Not Capable C pk > 1Capable at 3  C pk > 1.33Capable at 4  C pk > 1.67Capable at 5  C pk > 2Capable at 6 