1. McGraw-Hill/Irwin Copyright © 2008 by The McGraw-Hill Companies, Inc. All rights reserved. Quality Management CHAPTER 6

2. 6-2 Learning Objectives After completing this chapter you will: Understand the concept of Total Quality Management Know about how quality is measured and the different dimensions of quality Understand the Define, Measure, Analyze, Improve and Control (DMAIC) quality improvement process Know how to calculate the capability of a process Understand how processes are monitored with control charts Be familiar with acceptance sampling concepts

3. 6-3 Total Quality Management (TQM) Total quality management is defined as managing the entire organization so that it excels on all dimensions of products and services that are important to the customer

4. 6-4 Quality Specifications Design quality: Inherent value of the product in the marketplace Dimensions include: Performance Features Reliability/Durability Serviceability Aesthetics Perceived Quality. Conformance quality: Degree to which the product or service design specifications are met

5. 6-5 Costs of Quality External Failure Costs Appraisal Costs Prevention Costs Internal Failure Costs Costs of Quality

6. 6-6 ISO 9000 Series of standards agreed upon by the International Organization for Standardization (ISO) Adopted in 1987 More than 100 countries A prerequisite for global competition? ISO 9000 directs you to "document what you do and then do as you documented"

7. 6-7 Six Sigma Quality A philosophy and set of methods companies use to eliminate defects in their products and processes Seeks to reduce variation in the processes that lead to product defects The name, “six sigma” refers to the variation that exists within plus or minus three standard deviations of the process outputs

8. 6-8 Six Sigma Quality (Continued) Six Sigma allows managers to readily describe process performance using a common metric: Defects Per Million Opportunities (DPMO)

9. 6-9 Six Sigma Quality (Continued) Example of Defects Per Million Opportunities (DPMO) calculation. Suppose we observe 200 letters delivered incorrectly to the wrong addresses in a small city during a single day when a total of 200,000 letters were delivered. What is the DPMO in this situation? So, for every one million letters delivered this city’s postal managers can expect to have 1,000 letters incorrectly sent to the wrong address. Cost of Quality: What might that DPMO mean in terms of over-time employment to correct the errors?

10. 6-10 Six Sigma Quality: DMAIC Cycle Define, Measure, Analyze, Improve, and Control (DMAIC) Developed by General Electric as a means of focusing effort on quality using a methodological approach Overall focus of the methodology is to understand and achieve what the customer wants A 6-sigma program seeks to reduce the variation in the processes that lead to these defects DMAIC consists of five steps….

11. 6-11 Six Sigma Quality: DMAIC Cycle (Continued) 1. Define (D) 2. Measure (M) 3. Analyze (A) 4. Improve (I) 5. Control (C) Customers and their priorities Process and its performance Causes of defects Remove causes of defects Maintain quality

12. 6-12 Example to illustrate the process… We are the maker of this cereal. Consumer reports has just published an article that shows that we frequently have less than 15 ounces of cereal in a box. What should we do?

13. 6-13 Step 1 - Define What is the critical-to-quality characteristic? The CTQ (critical-to-quality) characteristic in this case is the weight of the cereal in the box.

14. 6-14 2 - Measure How would we measure to evaluate the extent of the problem? What are acceptable limits on this measure?

15. 6-15 2 – Measure (continued) Let’s assume that the government says that we must be within ± 5 percent of the weight advertised on the box. Upper Tolerance Limit = 16 +.05(16) = 16.8 ounces Lower Tolerance Limit = 16 –.05(16) = 15.2 ounces

16. 6-16 2. Measure (continued) We go out and buy 1,000 boxes of cereal and find that they weight an average of 15.875 ounces with a standard deviation of.529 ounces. What percentage of boxes are outside the tolerance limits?

17. 6-17 Upper Tolerance = 16.8 Lower Tolerance = 15.2 Process Mean = 15.875 Std. Dev. =.529 What percentage of boxes are defective (i.e. less than 15.2 oz)? Z = (x – Mean)/Std. Dev. = (15.2 – 15.875)/.529 = -1.276 NORMSDIST(Z) = NORMSDIST(-1.276) =.100978 Approximately, 10 percent of the boxes have less than 15.2 Ounces of cereal in them!

18. 6-18 Step 3 - Analyze - How can we improve the capability of our cereal box filling process? Decrease Variation Center Process Increase Specifications

19. 6-19 Step 4 – Improve – How good is good enough? Motorola’s “Six Sigma ” 6  minimum from process center to nearest spec

20. 6-20 Motorola’s “Six Sigma” Implies 2 ppB “bad” with no process shift With 1.5  shift in either direction from center (process will move), implies 3.4 ppm “bad”.

21. 6-21 Step 5 – Control Statistical Process Control (SPC) Use data from the actual process Estimate distributions Look at capability - is good quality possible Statistically monitor the process over time

22. 6-22 Analytical Tools for Six Sigma and Continuous Improvement: Flow Chart No, Continue… Material Received from Supplier Inspect Material for Defects Defects found? Return to Supplier for Credit Yes Can be used to find quality problems

23. 6-23 Analytical Tools for Six Sigma and Continuous Improvement: Run Chart Can be used to identify when equipment or processes are not behaving according to specifications 0.44 0.46 0.48 0.5 0.52 0.54 0.56 0.58 123456789101112 Time (Hours) Diameter

24. 6-24 Analytical Tools for Six Sigma and Continuous Improvement: Pareto Analysis Can be used to find when 80% of the problems may be attributed to 20% of the causes Can be used to find when 80% of the problems may be attributed to 20% of the causes Assy. Instruct. Frequency DesignPurch. Training 80%

25. 6-25 Analytical Tools for Six Sigma and Continuous Improvement: Checksheet Billing Errors Wrong Account Wrong Amount A/R Errors Wrong Account Wrong Amount Monday Can be used to keep track of defects or used to make sure people collect data in a correct manner

26. 6-26 Analytical Tools for Six Sigma and Continuous Improvement: Histogram Number of Lots Data Ranges Defects in lot 01234 Can be used to identify the frequency of quality defect occurrence and display quality performance

27. 6-27 Analytical Tools for Six Sigma and Continuous Improvement: Cause & Effect Diagram Effect ManMachine MaterialMethod Environment Possible causes: The results or effect Can be used to systematically track backwards to find a possible cause of a quality problem (or effect)

28. 6-28 Analytical Tools for Six Sigma and Continuous Improvement: Control Charts Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality 970 980 990 1000 1010 1020 0123456789101112131415 LCL UCL

29. 6-29 Other Six Sigma Tools Failure Mode and Effect Analysis (DMEA) is a structured approach to identify, estimate, prioritize, and evaluate risk of possible failures at each stage in the process Design of Experiments (DOE) a statistical test to determine cause-and-effect relationships between process variables and output

30. 6-30 Basic Forms of Variation Assignable variation is caused by factors that can be clearly identified and possibly managed Common variation is inherent in the production process Example: A poorly trained employee that creates variation in finished product output. Example: A molding process that always leaves “burrs” or flaws on a molded item.

31. 6-31 Statistical Quality Control The quantitative aspects of quality management Processes usually exhibit some variation in their output Some variation can be controlled and others are inherent in the process

32. 6-32 Taguchi’s View of Variation Incremental Cost of Variability High Zero Lower Spec Target Spec Upper Spec Traditional View Incremental Cost of Variability High Zero Lower Spec Target Spec Upper Spec Taguchi’s View Traditional view is that quality within the LS and US is good and that the cost of quality outside this range is constant, where Taguchi views costs as increasing as variability increases, so seek to achieve zero defects and that will truly minimize quality costs.

33. 6-33 Process Capability Process limits Specification limits How do the limits relate to one another?

34. 6-34 Process Capability Index, C pk Shifts in Process Mean Capability Index shows how well parts being produced fit into design limit specifications. As a production process produces items small shifts in equipment or systems can cause differences in production performance from differing samples.

35. 6-35 A simple ratio: Specification Width _________________________________________________________ Actual “Process Width” Generally, the bigger the better. Process Capability – A Standard Measure of How Good a Process Is.

36. 6-36 Process Capability This is a “one-sided” Capability Index Concentration on the side which is closest to the specification - closest to being “bad”

37. 6-37 The Cereal Box Example We are the maker of this cereal. Consumer reports has just published an article that shows that we frequently have less than 15 ounces of cereal in a box. Let’s assume that the government says that we must be within ± 5 percent of the weight advertised on the box. Upper Tolerance Limit = 16 +.05(16) = 16.8 ounces Lower Tolerance Limit = 16 –.05(16) = 15.2 ounces We go out and buy 1,000 boxes of cereal and find that they weight an average of 15.875 ounces with a standard deviation of.529 ounces.

38. 6-38 Cereal Box Process Capability Specification or Tolerance Limits Upper Spec = 16.8 oz Lower Spec = 15.2 oz Observed Weight Mean = 15.875 oz Std Dev =.529 oz

39. 6-39 What does a C pk of.4253 mean? An index that shows how well the units being produced fit within the specification limits. This is a process that will produce a relatively high number of defects. Many companies look for a C pk of 1.3 or better… 6-Sigma company wants 2.0!

40. 6-40 Types of Statistical Sampling Attribute (Go or no-go information) Defectives refers to the acceptability of product across a range of characteristics. Defects refers to the number of defects per unit which may be higher than the number of defectives. p-chart application Variable (Continuous) Usually measured by the mean and the standard deviation. X-bar and R chart applications

41. 6-41 Statistical Process Control (SPC) Charts UCL LCL Samples over time 1 2 3 4 5 6 UCL LCL Samples over time 1 2 3 4 5 6 UCL LCL Samples over time 1 2 3 4 5 6 Normal Behavior Possible problem, investigate

42. 6-42 Control Limits are based on the Normal Curve x 0123-3-2 z  Standard deviation units or “z” units.

43. 6-43 Control Limits We establish the Upper Control Limits (UCL) and the Lower Control Limits (LCL) with plus or minus 3 standard deviations from some x- bar or mean value. Based on this we can expect 99.7% of our sample observations to fall within these limits. x LCLUCL 99.7%

44. 6-44 Example of Constructing a p-Chart: Required Data Sample No. No. of Samples Number of defects found in each sample

45. 6-45 Statistical Process Control Formulas: Attribute Measurements (p-Chart) Given: Compute control limits:

46. 6-46 1. Calculate the sample proportions, p (these are what can be plotted on the p-chart) for each sample Example of Constructing a p-chart: Step 1

47. 6-47 2. Calculate the average of the sample proportions 3. Calculate the standard deviation of the sample proportion Example of Constructing a p-chart: Steps 2&3

48. 6-48 4. Calculate the control limits UCL = 0.0924 LCL = -0.0204 (or 0) UCL = 0.0924 LCL = -0.0204 (or 0) Example of Constructing a p-chart: Step 4

49. 6-49 Example of Constructing a p-Chart: Step 5 5. Plot the individual sample proportions, the average of the proportions, and the control limits 5. Plot the individual sample proportions, the average of the proportions, and the control limits UCL LCL

50. 6-50 Example of x-bar and R Charts: Required Data

51. 6-51 Example of x-bar and R charts: Step 1. Calculate sample means, sample ranges, mean of means, and mean of ranges.

52. 6-52 Example of x-bar and R charts: Step 2. Determine Control Limit Formulas and Necessary Tabled Values From Exhibit TN8.7

53. 6-53 Example of x-bar and R charts: Steps 3&4. Calculate x-bar Chart and Plot Values UCL LCL

54. 6-54 Example of x-bar and R charts: Steps 5&6. Calculate R-chart and Plot Values UCL LCL

55. 6-55 Basic Forms of Statistical Sampling for Quality Control Acceptance Sampling is sampling to accept or reject the immediate lot of product at hand Statistical Process Control is sampling to determine if the process is within acceptable limits

56. 6-56 Acceptance Sampling Purposes Determine quality level Ensure quality is within predetermined level Advantages Economy Less handling damage Fewer inspectors Upgrading of the inspection job Applicability to destructive testing Entire lot rejection (motivation for improvement)

57. 6-57 Acceptance Sampling (Continued ) Disadvantages Risks of accepting “bad” lots and rejecting “good” lots Added planning and documentation Sample provides less information than 100-percent inspection

58. 6-58 Acceptance Sampling: Single Sampling Plan A simple goal n c, Determine (1) how many units, n, to sample from a lot, and (2) the maximum number of defective items, c, that can be found in the sample before the lot is rejected

59. 6-59 Risk Acceptable Quality Level (AQL) Max. acceptable percentage of defectives defined by producer The  (Producer’s risk) The probability of rejecting a good lot Lot Tolerance Percent Defective (LTPD) Percentage of defectives that defines consumer’s rejection point The  (Consumer’s risk) The probability of accepting a bad lot

60. 6-60 Operating Characteristic Curve n = 99 c = 4 AQLLTPD 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 123456789101112 Percent defective Probability of acceptance  =.10 (consumer’s risk)  =.05 (producer’s risk) The OCC brings the concepts of producer’s risk, consumer’s risk, sample size, and maximum defects allowed together The shape or slope of the curve is dependent on a particular combination of the four parameters

61. 6-61 Example: Acceptance Sampling Problem Zypercom, a manufacturer of video interfaces, purchases printed wiring boards from an outside vender, Procard. Procard has set an acceptable quality level of 1% and accepts a 5% risk of rejecting lots at or below this level. Zypercom considers lots with 3% defectives to be unacceptable and will assume a 10% risk of accepting a defective lot. Develop a sampling plan for Zypercom and determine a rule to be followed by the receiving inspection personnel. Zypercom, a manufacturer of video interfaces, purchases printed wiring boards from an outside vender, Procard. Procard has set an acceptable quality level of 1% and accepts a 5% risk of rejecting lots at or below this level. Zypercom considers lots with 3% defectives to be unacceptable and will assume a 10% risk of accepting a defective lot. Develop a sampling plan for Zypercom and determine a rule to be followed by the receiving inspection personnel.

62. 6-62 Example: Step 1. What is given and what is not? In this problem, AQL is given to be 0.01 and LTDP is given to be 0.03. We are also given an alpha of 0.05 and a beta of 0.10. What you need to determine is your sampling plan is “c” and “n.”

63. 6-63 Example: Step 2. Determine “c” First divide LTPD by AQL. Then find the value for “c” by selecting the value in the TN7.10 “n(AQL)”column that is equal to or just greater than the ratio above. Exhibit TN 8.10 cLTPD/AQLn AQLcLTPD/AQLn AQL 044.8900.05253.5492.613 110.9460.35563.2063.286 26.5090.81872.9573.981 34.8901.36682.7684.695 44.0571.97092.6185.426 So, c = 6.

64. 6-64 Example: Step 3. Determine Sample Size c = 6, from Table n (AQL) = 3.286, from Table AQL =.01, given in problem c = 6, from Table n (AQL) = 3.286, from Table AQL =.01, given in problem Sampling Plan: Take a random sample of 329 units from a lot. Reject the lot if more than 6 units are defective. Sampling Plan: Take a random sample of 329 units from a lot. Reject the lot if more than 6 units are defective. Now given the information below, compute the sample size in units to generate your sampling plan n(AQL/AQL) = 3.286/.01 = 328.6, or 329 (always round up)

65. 6-65 End of Chapter 6