1. Quality Control and Management

2. Quality and Cost of quality “Quality is the totality of features and characteristics of a product or service that bears on its ability to satisfy stated or implied needs” American Society for Quality “It costs a lot to produce a bad product.” Norman Augustine 1.Prevention costs 2.Appraisal costs 3.Internal failure costs 4.External failure costs 5.Opportunity costs

3. External Failure Internal Failure Prevention Costs of Quality Appraisal Total Cost Quality Improvement Total Cost

4. What is quality management all about? Try to manage all aspects of the organization in order to excel in all dimensions that are important to “customers” Two aspects of quality: features: more features that meet customer needs = higher quality freedom from trouble: fewer defects = higher quality

5. The Quality Gurus – Edward Deming 1900-1993 1986  Quality is “uniformity and dependability”  Focus on SPC and statistical tools  “14 Points” for management  PDCA method

6. Deming’s Fourteen Points TABLE 6.2Deming’s 14 Points for Implementing Quality Improvement 1.Create consistency of purpose 2.Lead to promote change 3.Build quality into the product; stop depending on inspections to catch problems 4.Build long-term relationships based on performance instead of awarding business on price 5.Continuously improve product, quality, and service 6.Start training 7.Emphasize leadership

7. Deming’s Fourteen Points Deming’s 14 Points for Implementing Quality Improvement 8.Drive out fear 9.Break down barriers between departments 10.Stop haranguing workers 11.Support, help, and improve 12.Remove barriers to pride in work 13.Institute a vigorous program of education and self-improvement 14.Put everyone in the company to work on the transformation

8. The Quality Gurus – Joseph Juran 1904 - 2008 1951  Quality is “fitness for use”  Pareto Principle  Cost of Quality  General management approach as well as statistics

9. History: how did we get here… Deming and Juran outlined the principles of Quality Management. Tai-ichi Ohno applies them in Toyota Motors Corp. Japan has its National Quality Award (1951). U.S. and European firms begin to implement Quality Management programs (1980’s). U.S. establishes the Malcolm Baldridge National Quality Award (1987). Today, quality is an imperative for any business.

10. What does Total Quality Management encompass? TQM is a management philosophy: continuous improvement leadership development partnership development Cultural Alignment Technical Tools (Process Analysis, SPC, QFD) Customer

11. Developing quality specifications InputProcessOutput Design Design quality Dimensions of quality Conformance quality

12. 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 six standard deviations of the process outputs

13. Six Sigma Quality

14. Quality Improvement Traditional Continuous Improvement Time Quality

15. Continuous improvement philosophy 1.Kaizen: Japanese term for continuous improvement. A step-by-step improvement of business processes. 2.PDCA: Plan-do-check-act as defined by Deming. PlanDo ActCheck 3.Benchmarking : what do top performers do?

16. Tools used for continuous improvement 1. Process flowchart

17. Tools used for continuous improvement 2. Run Chart PerformanceTime

18. Tools used for continuous improvement 3. Control Charts Performance Metric Time

19. Tools used for continuous improvement 4. Cause and effect diagram (fishbone) Environment Machine Man MethodMaterial

20. Tools used for continuous improvement 5. Check sheet ItemABCDEFG ------- √ √ √ √ √ √ √ √√√√√√ √

21. Tools used for continuous improvement 6. Histogram Frequency

22. Tools used for continuous improvement 7. Pareto Analysis ABCDEF Frequency Percentage 50% 100% 0% 75% 25% 10 20 30 40 50 60

23. Summary of Tools 1.Process flow chart 2.Run diagram 3.Control charts 4.Fishbone 5.Check sheet 6.Histogram 7.Pareto analysis

24. Case: shortening telephone waiting time… A bank is employing a call answering service The main goal in terms of quality is “zero waiting time” - customers get a bad impression - company vision to be friendly and easy access The question is how to analyze the situation and improve quality

25. The current process Customer B Operator Customer A Receiving Party How can we reduce waiting time?

26. Makes custome r wait Absent receiving party Working system of operators CustomerOperator Fishbone diagram analysis Absent Out of office Not at desk Lunchtime Too many phone calls Absent Not giving receiving party’s coordinates Complaining Leaving a message Lengthy talk Does not know organization well Takes too much time to explain Does not understand customer

27. Daily average Total number AOne operator (partner out of office)14.3172 BReceiving party not present6.173 CNo one present in the section receiving call5.161 DSection and name of the party not given1.619 EInquiry about branch office locations1.316 FOther reasons0.810 29.2351 Reasons why customers have to wait (12-day analysis with check sheet)

28. Pareto Analysis: reasons why customers have to wait ABCDEF FrequencyPercentage 0% 49% 71.2% 100 200 300 87.1% 150 250

29. Ideas for improvement 1.Taking lunches on three different shifts 2.Ask all employees to leave messages when leaving desks 3.Compiling a directory where next to personnel’s name appears her/his title

30. Results of implementing the recommendations ABCDEF Frequency Percentage 100% 0% 49% 71.2% 100 200 300 87.1% 100% BCADEF Frequency Percentage 0% 100 200 300 Before… …After Improvement

31. In general, how can we monitor quality…? 1.Assignable variation: we can assess the cause 2.Common variation: variation that may not be possible to correct (random variation, random noise) By observing variation in output measures!

32. Statistical Process Control (SPC) Every output measure has a target value and a level of “acceptable” variation (upper and lower tolerance limits) SPC uses samples from output measures to estimate the mean and the variation (standard deviation) Example We want beer bottles to be filled with 12 FL OZ ± 0.05 FL OZ Question: How do we define the output measures?

33. In order to measure variation we need… The average (mean) of the observations: The standard deviation of the observations:

34. Average & Variation example Number of pepperoni’s per pizza: 25, 25, 26, 25, 23, 24, 25, 27 Average: 25 Standard Deviation: 1.1 Number of pepperoni’s per pizza: 25, 22, 28, 30, 27, 20, 25, 23 Average: 25 Standard Deviation: 3.17 Which pizza would you rather have?

35. Accuracy and Consistency We say that a process is accurate if its mean is close to the target T. We say that a process is consistent if its standard deviation is low.

36. Control Charts Control charts tell you when a process measure is exhibiting abnormal behavior. Upper Control Limit Central Line Lower Control Limit

37. Two Types of Control Charts X/R Chart This is a plot of averages and ranges over time (used for performance measures that are variables) p Chart This is a plot of proportions over time (used for performance measures that are yes/no attributes)

38. When should we use p charts? 1.When decisions are simple “yes” or “no” by inspection 2.When the sample sizes are large enough (>50) Sample (day)ItemsDefectivePercentage 1200100.050 220080.040 320090.045 4200130.065 5200150.075 6200250.125 7200160.080 Statistical Process Control with p Charts

39. Let’s assume that we take t samples of size n …

40. Statistical Process Control with p Charts

41. LCL = 0.015 UCL = 0.117 p = 0.066 Statistical Process Control with p Charts

42. When should we use X/R charts? 1.It is not possible to label “good” or “bad” 2.If we have relatively smaller sample sizes (<20) Statistical Process Control with X/R Charts

43. Take t samples of size n (sample size should be 5 or more) R is the range between the highest and the lowest for each sample Statistical Process Control with X/R Charts X is the mean for each sample

44. Statistical Process Control with X/R Charts X is the average of the averages. R is the average of the ranges

45. define the upper and lower control limits… Statistical Process Control with X/R Charts Read A 2, D 3, D 4 from the Table according to the sample size. In the next example n=5 and A 2 =0.58, D 3 =0 and D 4 =2.11. If sample size n=10, then A 2 =0.31, D 3 =0.223 and D 4 =1.78

46. Example: SPC for bottle filling… SampleObservation (x i )AverageRange (R) 111.9011.9212.0911.9112.01 212.03 11.9211.9712.07 311.9212.0211.9312.0112.07 411.9612.0612.0011.9111.98 511.9512.1012.0312.0712.00 611.9911.9811.9412.06 712.0012.0411.9212.0012.07 812.0212.0611.9412.0712.00 912.0112.0611.9411.9111.94 1011.9212.0511.9212.0912.07

47. Example: SPC for bottle filling… SampleObservation (x i )AverageRange (R) 111.9011.9212.0911.9112.0111.970.19 212.03 11.9211.9712.0712.000.15 311.9212.0211.9312.0112.0711.990.15 411.9612.0612.0011.9111.98 0.15 511.9512.1012.0312.0712.0012.030.15 611.9911.9811.9412.06 12.010.12 712.0012.0411.9212.0012.0712.010.15 812.0212.0611.9412.0712.0012.020.13 912.0112.0611.9411.9111.9411.970.15 1011.9212.0511.9212.0912.0712.010.17 Calculate the average and the range for each sample…

48. Then… is the average of the averages is the average of the ranges

49. Finally… Calculate the upper and lower control limits

50. LCL = 11.90 UCL = 12.10 The X Chart X = 12.00

51. The R Chart LCL = 0.00 R = 0.15 UCL = 0.32

52. The X/R Chart LCL UCL X LCL R UCL What can you conclude?