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Statistical Quality Control in Services: Monitoring, Evaluation, Improvement

Learn about Statistical Process Control, types of data, constructing control charts, Normal Distribution, using control charts for attributes and variables, and examples in service quality management.

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Statistical Quality Control in Services: Monitoring, Evaluation, Improvement

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  1. Chapter 7 Statistical Quality Control

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

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

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

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

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

  7. Upper control limit Process average Lower control limit 10 9 1 2 3 4 5 6 7 8 Process Control Chart Sample number

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

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

  10. -1s -3s -2s m = 0 1s 2s 3s The Normal Distribution 95 % 99.74 % Area under the curve = 1.0

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

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

  13. Control Charts For Attributes • p Charts • Calculate percent defectives in a sample; • an item is either good or bad • 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. UCL = p + 3 p(1-p) /n = 0.10 + 3 0.10 (1-0.10) /100 = 0.190 p-Chart Calculations Proportion SampleDefect Defective 1 6 .06 2 0 .00 3 4 .04 . . . . . . 20 18 .18 200 • LCL= p - 3 p(1-p) /n • = 0.10 + 3 0.10 (1-0.10) /100 • = 0.010 100 jeans in each sample total defectives total sample observations 200 20 (100) p = = = 0.10

  18. Sample number

  19. c - Charts • Count the number of defects in an item • 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

  20. 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.

  21. 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.

  22. 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.

  23. c - Chart Calculations Count # of defects per roll in 15 rolls of denim fabric SampleDefects 1 12 2 8 3 16 . . . . 15 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

  24. Example c - Chart

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

  26. 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:

  27. Example: Control Charts for Variable Data Slip Ring Diameter (cm) Sample 1 2 3 4 5 X R 1 5.02 5.01 4.94 4.99 4.96 4.98 0.08 2 5.01 5.03 5.07 4.95 4.96 5.00 0.12 3 4.99 5.00 4.93 4.92 4.99 4.97 0.08 4 5.03 4.91 5.01 4.98 4.89 4.96 0.14 5 4.95 4.92 5.03 5.05 5.01 4.99 0.13 6 4.97 5.06 5.06 4.96 5.03 5.01 0.10 7 5.05 5.01 5.10 4.96 4.99 5.02 0.14 8 5.09 5.10 5.00 4.99 5.08 5.05 0.11 9 5.14 5.10 4.99 5.08 5.09 5.08 0.15 10 5.01 4.98 5.08 5.07 4.99 5.03 0.10 50.09 1.15

  28. Constructing an Range Chart UCLR = D4 R = (2.11) (.115) = 0.24 LCLR = D3 R = (0) (.115) = 0 where R = SR / k = 1.15 / 10 = .115 k = number of samples = 10 R = range = (largest - smallest)

  29. Example R-Chart UCL R LCL

  30. Constructing A Mean Chart UCLX = X + A2 R = 5.01 + (0.58) (.115) = 5.08 LCLX = X - A2 R = 5.01 - (0.58) (.115) = 4.94 where X = average of sample means = S X / n = 50.09 / 10 = 5.01 R = average range = SR / k = 1.15 / 10 = .115

  31. Example X-bar Chart UCL X LCL

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

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

  34. Control Chart Patterns UCL UCL LCL LCL Sample observations consistently increasing Sample observations consistently decreasing

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

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