1. Chapter 7 Statistical Quality Control

2. Quality Control Approaches l Statistical process control (SPC) Monitors the production process to prevent poor quality

3. Statistical Process Control l Take periodic samples from a process l Plot the sample points on a control chart l Determine if the process is within limits l Correct the process before defects occur

4. Types Of Data l Attribute data l Product characteristic evaluated with a discrete choice – Good/bad, yes/no l Variable data l Product characteristic that can be measured – Length, size, weight, height, time, velocity

5. SPC Applied To Services l Nature of defect is different in services l Service defect is a failure to meet customer requirements l Monitor times, customer satisfaction

6. Service Quality Examples l Hospitals timeliness, responsiveness, accuracy l Grocery Stores Check-out time, stocking, cleanliness l Airlines luggage handling, waiting times, courtesy l Fast food restaurants waiting times, food quality, cleanliness

7. Process Control Chart 12345678 9 10 Sample number Upper control limit Process average Lower control limit

8. Constructing a Control Chart l Decide what to measure or count l Collect the sample data l Plot the samples on a control chart l Calculate and plot the control limits on the control chart l Determine if the data is in-control l If non-random variation is present, discard the data (fix the problem) and recalculate the control limits

9. A Process Is In Control If l No sample points are outside control limits l Most points are near the process average l About an equal # points are above & below the centerline l Points appear randomly distributed

10. 99.74 % The Normal Distribution 95 %  = 0 1  22 33 -1-1 -2-2 -3  Area under the curve = 1.0

11. Control Charts and the Normal Distribution Mean UCL LCL + 3  - 3 

12. Types Of Data l Attribute data (p-charts, c-charts) Product characteristics evaluated with a discrete choice (Good/bad, yes/no, count) l Variable data (X-bar and R charts) Product characteristics that can be measured (Length, size, weight, height, time, velocity)

13. Control Charts For Attributes l p Charts Calculate percent defectives in a sample; an item is either good or bad l c Charts Count number of defects in an item

14. p - Charts Based on the binomial distribution p = number defective / sample size, n p = total no. of defectives total no. of sample observations UCL = p + 3 p(1-p)/n LCL = p - 3 p(1-p)/n

15. p-Chart Example The Western Jean Company produced denim jean. The company wants to establish a p-chart to monitor the production process and main high quality. Western beliefs that approximately 99.74 percent of the variability in the production process (corresponding to 3-sigma limits, or z = 3.00) is random and thus should be within control limits, whereas 0.26 percent of the process variability is not random and suggest that the process is out of control.

16. p-Chart Example The company has taken 20 sample (one per day for 20 days), each containing 100 pairs of jeans (n = 100), and inspected them for defects, the results of which are as follow.

17. Sample# DefectsSample# Defects 161112 20 10 341314 410148 56156 6416 7121712 8101814 981920 10 2018

18. p-Chart Calculations Proportion Sample Defect Defective 1 6.06 2 0.00 3 4.04... 20 18.18 200 = 0.10 = total defectives total sample observations 200 20 (100) p = 100 jeans in each sample LCL = p - 3 p(1-p) /n = 0.10 + 3 0.10 (1-0.10) /100 = 0.010 UCL = p + 3 p(1-p) /n = 0.10 + 3 0.10 (1-0.10) /100 = 0.190

19. Sample number

20. c - Charts l Count the number of defects in an item l Based on the Poisson distribution c = number of defects in an item c = total number of defects number of samples UCL = c + 3 c LCL = c - 3 c

21. c-Chart Example The Ritz Hotel has 240 rooms. The hotel’s housekeeping department is responsible for maintaining the quality of the room’s appearance and cleanliness. Each individual housekeeper is responsible for an area encompassing 20 rooms. Every room in use is thoroughly clean and its supplies, toiletries, and so on are restocked each day. Any defects that the housekeeping staff notice that are not part the normal housekeeping service are supposed to be reported hotel maintenance.

22. c-Chart Example Every room is briefly inspected each day by a housekeeping supervisor. However, hotel management also conducts inspection for quality- control purposes. The management inspector not only check for normal housekeeping defects like clean sheets, dust, room supplies, room literature, or towels, but also for defects like an inoperative or missing TV remote, poor TV picture quality or reception, defective lamps, a malfunctioning clock, tears or stains in bedcovers or curtain, or a malfunctioning curtain pull.

23. c-Chart Example An inspection sample include 12 rooms, i.e., one room selected at random from each of the twelve 20-room blocks served by a housekeeper. Following are the results from 15 inspection samples conducted at random during a 1-month period.

24. Sample# DefectsSample# Defects 1121112 28 10 3161314 4 17 51015 611 79 814 913 1015

25. c - Chart Calculations Count # of defects per roll in 15 rolls of denim fabric Sample Defects 1 12 2 8 3 16... 15 190 c = 190/15 = 12.67 UCL = c + 3 c = 12.67 + 3 12.67 = 23.35 LCL = c - 3 c = 12.67 - 3 12.67 = 1.99

26. Example c - Chart

27. Control Charts For Variables l Mean chart (X-Bar Chart) Measures central tendency of a sample l Range chart (R-Chart) Measures amount of dispersion in a sample l Each chart measures the process differently. Both the process average and process variability must be in control for the process to be in control.

28. Example: Control harts for Variable Data The Goliath Tool Company produces slip-ring bearings, which look like flat doughnut or washer, they fit around shafts or rods, such as drive shaft in machinery or motor. In the production process for a particular slip-ring bearing the employees has taken 10 samples (during a 10 day period) of 5 slip-ring bearing (i.e., n = 5). The individual observation from each sample are shown as followed:

29. Example: Control Charts for Variable Data Slip Ring Diameter (cm) Sample 12345 X R 15.025.014.944.994.96 4.98 0.08 25.015.035.074.954.96 5.00 0.12 34.995.004.934.924.99 4.97 0.08 45.034.915.014.984.89 4.96 0.14 54.954.925.035.055.01 4.99 0.13 64.975.065.064.965.03 5.01 0.10 75.055.015.104.964.99 5.02 0.14 85.095.105.004.995.08 5.05 0.11 95.145.104.995.085.09 5.08 0.15 105.014.985.085.074.99 5.03 0.10 50.09 1.15

30. Constructing an Range Chart UCL R = D 4 R = (2.11) (.115) = 0.24 LCL R = D 3 R = (0) (.115) = 0 where R =   R / k = 1.15 / 10 =.115 k = number of samples = 10 R = range = (largest - smallest)

31. Example R-Chart UCL R LCL

32. Constructing A Mean Chart UCL X = X + A 2 R = 5.01 + (0.58) (.115) = 5.08 LCL X = X - A 2 R = 5.01 - (0.58) (.115) = 4.94 where X = average of sample means =  X / n = 50.09 / 10 = 5.01 R = average range =   R / k = 1.15 / 10 =.115

33. Example X-bar Chart UCL X LCL

34. Variation l Common Causes Variation inherent in a process Can be eliminated only through improvements in the system l Special Causes Variation due to identifiable factors Can be modified through operator or management action

35. UCL LCL UCL Sample observations consistently below the center line Sample observations consistently above the center line Control Chart Patterns

36. LCL UCL Sample observations consistently increasing Sample observations consistently decreasing

37. Sample Size Determination l Attribute control charts 50 to 100 parts in a sample l Variable control charts 2 to 10 parts in a sample