supplement-6-e28093-spc.ppt- SPC_ Operation Management
- 1. © 2008 Prentice Hall, Inc. S6 – 1 Operations Management Supplement 6 – Statistical Process Control PowerPoint presentation to accompany Heizer/Render Principles of Operations Management, 7e Operations Management, 9e
- 2. © 2008 Prentice Hall, Inc. S6 – 2 Outline Statistical Process Control (SPC) Control Charts for Variables The Central Limit Theorem Setting Mean Chart Limits (x-Charts) Setting Range Chart Limits (R-Charts) Using Mean and Range Charts Control Charts for Attributes Managerial Issues and Control Charts
- 3. © 2008 Prentice Hall, Inc. S6 – 3 Outline – Continued Process Capability Process Capability Ratio (Cp) Process Capability Index (Cpk ) Acceptance Sampling Operating Characteristic Curve Average Outgoing Quality
- 4. © 2008 Prentice Hall, Inc. S6 – 4 Learning Objectives When you complete this supplement you should be able to: 1. Explain the use of a control chart 2. Explain the role of the central limit theorem in SPC 3. Build x-charts and R-charts 4. List the five steps involved in building control charts
- 5. © 2008 Prentice Hall, Inc. S6 – 5 Learning Objectives When you complete this supplement you should be able to: 5. Build p-charts and c-charts 6. Explain process capability and compute Cp and Cpk 7. Explain acceptance sampling 8. Compute the AOQ
- 6. © 2008 Prentice Hall, Inc. S6 – 6 Variability is inherent in every process Natural or common causes Special or assignable causes Provides a statistical signal when assignable causes are present Detect and eliminate assignable causes of variation Statistical Process Control (SPC)
- 7. © 2008 Prentice Hall, Inc. S6 – 7 Natural Variations Also called common causes Affect virtually all production processes Expected amount of variation Output measures follow a probability distribution For any distribution there is a measure of central tendency and dispersion If the distribution of outputs falls within acceptable limits, the process is said to be “in control”
- 8. © 2008 Prentice Hall, Inc. S6 – 8 Assignable Variations Also called special causes of variation Generally this is some change in the process Variations that can be traced to a specific reason The objective is to discover when assignable causes are present Eliminate the bad causes Incorporate the good causes
- 9. © 2008 Prentice Hall, Inc. S6 – 9 Samples To measure the process, we take samples and analyze the sample statistics following these steps (a) Samples of the product, say five boxes of cereal taken off the filling machine line, vary from each other in weight Frequency Weight # # # # # # # # # # # # # # # # # # # # # # # # # # Each of these represents one sample of five boxes of cereal Figure S6.1
- 10. © 2008 Prentice Hall, Inc. S6 – 10 Samples To measure the process, we take samples and analyze the sample statistics following these steps (b) After enough samples are taken from a stable process, they form a pattern called a distribution The solid line represents the distribution Frequency Weight Figure S6.1
- 11. © 2008 Prentice Hall, Inc. S6 – 11 Samples To measure the process, we take samples and analyze the sample statistics following these steps (c) There are many types of distributions, including the normal (bell-shaped) distribution, but distributions do differ in terms of central tendency (mean), standard deviation or variance, and shape Weight Central tendency Weight Variation Weight Shape Frequency Figure S6.1
- 12. © 2008 Prentice Hall, Inc. S6 – 12 Samples To measure the process, we take samples and analyze the sample statistics following these steps (d) If only natural causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable Weight Frequency Prediction Figure S6.1
- 13. © 2008 Prentice Hall, Inc. S6 – 13 Samples To measure the process, we take samples and analyze the sample statistics following these steps (e) If assignable causes are present, the process output is not stable over time and is not predicable Weight Frequency Prediction ? ? ? ? ? ? ? ? ? ? ?? ? ? ? ? ? ?? Figure S6.1
- 14. © 2008 Prentice Hall, Inc. S6 – 14 Control Charts Constructed from historical data, the purpose of control charts is to help distinguish between natural variations and variations due to assignable causes
- 15. © 2008 Prentice Hall, Inc. S6 – 15 Process Control Figure S6.2 Frequency (weight, length, speed, etc.) Size Lower control limit Upper control limit (a) In statistical control and capable of producing within control limits (b) In statistical control but not capable of producing within control limits (c) Out of control
- 16. © 2008 Prentice Hall, Inc. S6 – 16 Types of Data Characteristics that can take any real value May be in whole or in fractional numbers Continuous random variables Variables Attributes Defect-related characteristics Classify products as either good or bad or count defects Categorical or discrete random variables
- 17. © 2008 Prentice Hall, Inc. S6 – 17 Central Limit Theorem Regardless of the distribution of the population, the distribution of sample means drawn from the population will tend to follow a normal curve 1. The mean of the sampling distribution (x) will be the same as the population mean m x = m s n sx = 2. The standard deviation of the sampling distribution (sx) will equal the population standard deviation (s) divided by the square root of the sample size, n
- 18. © 2008 Prentice Hall, Inc. S6 – 18 Population and Sampling Distributions Three population distributions Beta Normal Uniform Distribution of sample means Standard deviation of the sample means = sx = s n Mean of sample means = x | | | | | | | -3sx -2sx -1sx x +1sx +2sx +3sx 99.73% of all x fall within ± 3sx 95.45% fall within ± 2sx Figure S6.3
- 19. © 2008 Prentice Hall, Inc. S6 – 19 Sampling Distribution x = m (mean) Sampling distribution of means Process distribution of means Figure S6.4
- 20. © 2008 Prentice Hall, Inc. S6 – 20 Control Charts for Variables For variables that have continuous dimensions Weight, speed, length, strength, etc. x-charts are to control the central tendency of the process R-charts are to control the dispersion of the process These two charts must be used together
- 21. © 2008 Prentice Hall, Inc. S6 – 21 Setting Chart Limits For x-Charts when we know s Upper control limit (UCL) = x + zsx Lower control limit (LCL) = x - zsx where x = mean of the sample means or a target value set for the process z = number of normal standard deviations sx = standard deviation of the sample means = s/ n s = population standard deviation n = sample size
- 22. © 2008 Prentice Hall, Inc. S6 – 22 Setting Control Limits Hour 1 Sample Weight of Number Oat Flakes 1 17 2 13 3 16 4 18 5 17 6 16 7 15 8 17 9 16 Mean 16.1 s = 1 Hour Mean Hour Mean 1 16.1 7 15.2 2 16.8 8 16.4 3 15.5 9 16.3 4 16.5 10 14.8 5 16.5 11 14.2 6 16.4 12 17.3 n = 9 LCLx = x - zsx = 16 - 3(1/3) = 15 ozs For 99.73% control limits, z = 3 UCLx = x + zsx = 16 + 3(1/3) = 17 ozs
- 23. © 2008 Prentice Hall, Inc. S6 – 23 17 = UCL 15 = LCL 16 = Mean Setting Control Limits Control Chart for sample of 9 boxes Sample number | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 Variation due to assignable causes Variation due to assignable causes Variation due to natural causes Out of control Out of control
- 24. © 2008 Prentice Hall, Inc. S6 – 24 Setting Chart Limits For x-Charts when we don’t know s Lower control limit (LCL) = x - A2R Upper control limit (UCL) = x + A2R where R = average range of the samples A2 = control chart factor found in Table S6.1 x = mean of the sample means
- 25. © 2008 Prentice Hall, Inc. S6 – 25 Control Chart Factors Table S6.1 Sample Size Mean Factor Upper Range Lower Range n A2 D4 D3 2 1.880 3.268 0 3 1.023 2.574 0 4 .729 2.282 0 5 .577 2.115 0 6 .483 2.004 0 7 .419 1.924 0.076 8 .373 1.864 0.136 9 .337 1.816 0.184 10 .308 1.777 0.223 12 .266 1.716 0.284
- 26. © 2008 Prentice Hall, Inc. S6 – 26 Setting Control Limits Process average x = 12 ounces Average range R = .25 Sample size n = 5
- 27. © 2008 Prentice Hall, Inc. S6 – 27 Setting Control Limits UCLx = x + A2R = 12 + (.577)(.25) = 12 + .144 = 12.144 ounces Process average x = 12 ounces Average range R = .25 Sample size n = 5 From Table S6.1
- 28. © 2008 Prentice Hall, Inc. S6 – 28 Setting Control Limits UCLx = x + A2R = 12 + (.577)(.25) = 12 + .144 = 12.144 ounces LCLx = x - A2R = 12 - .144 = 11.857 ounces Process average x = 12 ounces Average range R = .25 Sample size n = 5 UCL = 12.144 Mean = 12 LCL = 11.857
- 29. © 2008 Prentice Hall, Inc. S6 – 29 R – Chart Type of variables control chart Shows sample ranges over time Difference between smallest and largest values in sample Monitors process variability Independent from process mean
- 30. © 2008 Prentice Hall, Inc. S6 – 30 Setting Chart Limits For R-Charts Lower control limit (LCLR) = D3R Upper control limit (UCLR) = D4R where R = average range of the samples D3 and D4 = control chart factors from Table S6.1
- 31. © 2008 Prentice Hall, Inc. S6 – 31 Setting Control Limits UCLR = D4R = (2.115)(5.3) = 11.2 pounds LCLR = D3R = (0)(5.3) = 0 pounds Average range R = 5.3 pounds Sample size n = 5 From Table S6.1 D4 = 2.115, D3 = 0 UCL = 11.2 Mean = 5.3 LCL = 0
- 32. © 2008 Prentice Hall, Inc. S6 – 32 Mean and Range Charts (a) These sampling distributions result in the charts below (Sampling mean is shifting upward but range is consistent) R-chart (R-chart does not detect change in mean) UCL LCL Figure S6.5 x-chart (x-chart detects shift in central tendency) UCL LCL
- 33. © 2008 Prentice Hall, Inc. S6 – 33 Mean and Range Charts R-chart (R-chart detects increase in dispersion) UCL LCL Figure S6.5 (b) These sampling distributions result in the charts below (Sampling mean is constant but dispersion is increasing) x-chart (x-chart does not detect the increase in dispersion) UCL LCL
- 34. © 2008 Prentice Hall, Inc. S6 – 34 Steps In Creating Control Charts 1. Take samples from the population and compute the appropriate sample statistic 2. Use the sample statistic to calculate control limits and draw the control chart 3. Plot sample results on the control chart and determine the state of the process (in or out of control) 4. Investigate possible assignable causes and take any indicated actions 5. Continue sampling from the process and reset the control limits when necessary
- 35. © 2008 Prentice Hall, Inc. S6 – 35 Manual and Automated Control Charts
- 36. © 2008 Prentice Hall, Inc. S6 – 36 Control Charts for Attributes For variables that are categorical Good/bad, yes/no, acceptable/unacceptable Measurement is typically counting defectives Charts may measure Percent defective (p-chart) Number of defects (c-chart)
- 37. © 2008 Prentice Hall, Inc. S6 – 37 Control Limits for p-Charts Population will be a binomial distribution, but applying the Central Limit Theorem allows us to assume a normal distribution for the sample statistics UCLp = p + zsp ^ LCLp = p - zsp ^ where p = mean fraction defective in the sample z = number of standard deviations sp = standard deviation of the sampling distribution n = sample size ^ p(1 - p) n sp = ^
- 38. © 2008 Prentice Hall, Inc. S6 – 38 p-Chart for Data Entry Sample Number Fraction Sample Number Fraction Number of Errors Defective Number of Errors Defective 1 6 .06 11 6 .06 2 5 .05 12 1 .01 3 0 .00 13 8 .08 4 1 .01 14 7 .07 5 4 .04 15 5 .05 6 2 .02 16 4 .04 7 5 .05 17 11 .11 8 3 .03 18 3 .03 9 3 .03 19 0 .00 10 2 .02 20 4 .04 Total = 80 (.04)(1 - .04) 100 sp = = .02 ^ p = = .04 80 (100)(20)
- 39. © 2008 Prentice Hall, Inc. S6 – 39 .11 – .10 – .09 – .08 – .07 – .06 – .05 – .04 – .03 – .02 – .01 – .00 – Sample number Fraction defective | | | | | | | | | | 2 4 6 8 10 12 14 16 18 20 p-Chart for Data Entry UCLp = p + zsp = .04 + 3(.02) = .10 ^ LCLp = p - zsp = .04 - 3(.02) = 0 ^ UCLp = 0.10 LCLp = 0.00 p = 0.04
- 40. © 2008 Prentice Hall, Inc. S6 – 40 .11 – .10 – .09 – .08 – .07 – .06 – .05 – .04 – .03 – .02 – .01 – .00 – Sample number Fraction defective | | | | | | | | | | 2 4 6 8 10 12 14 16 18 20 UCLp = p + zsp = .04 + 3(.02) = .10 ^ LCLp = p - zsp = .04 - 3(.02) = 0 ^ UCLp = 0.10 LCLp = 0.00 p = 0.04 p-Chart for Data Entry Possible assignable causes present
- 41. © 2008 Prentice Hall, Inc. S6 – 41 Control Limits for c-Charts Population will be a Poisson distribution, but applying the Central Limit Theorem allows us to assume a normal distribution for the sample statistics where c = mean number defective in the sample UCLc = c + 3 c LCLc = c - 3 c
- 42. © 2008 Prentice Hall, Inc. S6 – 42 c-Chart for Cab Company c = 54 complaints/9 days = 6 complaints/day | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 Day Number defective 14 – 12 – 10 – 8 – 6 – 4 – 2 – 0 – UCLc = c + 3 c = 6 + 3 6 = 13.35 LCLc = c - 3 c = 6 - 3 6 = 0 UCLc = 13.35 LCLc = 0 c = 6
- 43. © 2008 Prentice Hall, Inc. S6 – 43 Managerial Issues and Control Charts Select points in the processes that need SPC Determine the appropriate charting technique Set clear policies and procedures Three major management decisions:
- 44. © 2008 Prentice Hall, Inc. S6 – 44 Which Control Chart to Use Using an x-chart and R-chart: Observations are variables Collect 20 - 25 samples of n = 4, or n = 5, or more, each from a stable process and compute the mean for the x-chart and range for the R-chart Track samples of n observations each Variables Data
- 45. © 2008 Prentice Hall, Inc. S6 – 45 Which Control Chart to Use Using the p-chart: Observations are attributes that can be categorized in two states We deal with fraction, proportion, or percent defectives Have several samples, each with many observations Attribute Data
- 46. © 2008 Prentice Hall, Inc. S6 – 46 Which Control Chart to Use Using a c-Chart: Observations are attributes whose defects per unit of output can be counted The number counted is a small part of the possible occurrences Defects such as number of blemishes on a desk, number of typos in a page of text, flaws in a bolt of cloth Attribute Data
- 47. © 2008 Prentice Hall, Inc. S6 – 47 Patterns in Control Charts Normal behavior. Process is “in control.” Upper control limit Target Lower control limit Figure S6.7
- 48. © 2008 Prentice Hall, Inc. S6 – 48 Upper control limit Target Lower control limit Patterns in Control Charts One plot out above (or below). Investigate for cause. Process is “out of control.” Figure S6.7
- 49. © 2008 Prentice Hall, Inc. S6 – 49 Upper control limit Target Lower control limit Patterns in Control Charts Trends in either direction, 5 plots. Investigate for cause of progressive change. Figure S6.7
- 50. © 2008 Prentice Hall, Inc. S6 – 50 Upper control limit Target Lower control limit Patterns in Control Charts Two plots very near lower (or upper) control. Investigate for cause. Figure S6.7
- 51. © 2008 Prentice Hall, Inc. S6 – 51 Upper control limit Target Lower control limit Patterns in Control Charts Run of 5 above (or below) central line. Investigate for cause. Figure S6.7
- 52. © 2008 Prentice Hall, Inc. S6 – 52 Upper control limit Target Lower control limit Patterns in Control Charts Erratic behavior. Investigate. Figure S6.7
- 53. © 2008 Prentice Hall, Inc. S6 – 53 Process Capability The natural variation of a process should be small enough to produce products that meet the standards required A process in statistical control does not necessarily meet the design specifications Process capability is a measure of the relationship between the natural variation of the process and the design specifications
- 54. © 2008 Prentice Hall, Inc. S6 – 54 Process Capability Ratio Cp = Upper Specification - Lower Specification 6s A capable process must have a Cp of at least 1.0 Does not look at how well the process is centered in the specification range Often a target value of Cp = 1.33 is used to allow for off-center processes Six Sigma quality requires a Cp = 2.0
- 55. © 2008 Prentice Hall, Inc. S6 – 55 Process Capability Ratio Cp = Upper Specification - Lower Specification 6s Insurance claims process Process mean x = 210.0 minutes Process standard deviation s = .516 minutes Design specification = 210 ± 3 minutes
- 56. © 2008 Prentice Hall, Inc. S6 – 56 Process Capability Ratio Cp = Upper Specification - Lower Specification 6s Insurance claims process Process mean x = 210.0 minutes Process standard deviation s = .516 minutes Design specification = 210 ± 3 minutes = = 1.938 213 - 207 6(.516)
- 57. © 2008 Prentice Hall, Inc. S6 – 57 Process Capability Ratio Cp = Upper Specification - Lower Specification 6s Insurance claims process Process mean x = 210.0 minutes Process standard deviation s = .516 minutes Design specification = 210 ± 3 minutes = = 1.938 213 - 207 6(.516) Process is capable
- 58. © 2008 Prentice Hall, Inc. S6 – 58 Process Capability Index A capable process must have a Cpk of at least 1.0 A capable process is not necessarily in the center of the specification, but it falls within the specification limit at both extremes Cpk = minimum of , Upper Specification - x Limit 3s Lower x - Specification Limit 3s
- 59. © 2008 Prentice Hall, Inc. S6 – 59 Process Capability Index New Cutting Machine New process mean x = .250 inches Process standard deviation s = .0005 inches Upper Specification Limit = .251 inches Lower Specification Limit = .249 inches
- 60. © 2008 Prentice Hall, Inc. S6 – 60 Process Capability Index New Cutting Machine New process mean x = .250 inches Process standard deviation s = .0005 inches Upper Specification Limit = .251 inches Lower Specification Limit = .249 inches Cpk = minimum of , (.251) - .250 (3).0005
- 61. © 2008 Prentice Hall, Inc. S6 – 61 Process Capability Index New Cutting Machine New process mean x = .250 inches Process standard deviation s = .0005 inches Upper Specification Limit = .251 inches Lower Specification Limit = .249 inches Cpk = = 0.67 .001 .0015 New machine is NOT capable Cpk = minimum of , (.251) - .250 (3).0005 .250 - (.249) (3).0005 Both calculations result in
- 62. © 2008 Prentice Hall, Inc. S6 – 62 Interpreting Cpk Cpk = negative number Cpk = zero Cpk = between 0 and 1 Cpk = 1 Cpk > 1 Figure S6.8
- 63. © 2008 Prentice Hall, Inc. S6 – 63 Acceptance Sampling Form of quality testing used for incoming materials or finished goods Take samples at random from a lot (shipment) of items Inspect each of the items in the sample Decide whether to reject the whole lot based on the inspection results Only screens lots; does not drive quality improvement efforts
- 64. © 2008 Prentice Hall, Inc. S6 – 64 Acceptance Sampling Form of quality testing used for incoming materials or finished goods Take samples at random from a lot (shipment) of items Inspect each of the items in the sample Decide whether to reject the whole lot based on the inspection results Only screens lots; does not drive quality improvement efforts Rejected lots can be: Returned to the supplier Culled for defectives (100% inspection)
- 65. © 2008 Prentice Hall, Inc. S6 – 65 Operating Characteristic Curve Shows how well a sampling plan discriminates between good and bad lots (shipments) Shows the relationship between the probability of accepting a lot and its quality level
- 66. © 2008 Prentice Hall, Inc. S6 – 66 Return whole shipment The “Perfect” OC Curve % Defective in Lot P(Accept Whole Shipment) 100 – 75 – 50 – 25 – 0 – | | | | | | | | | | | 0 10 20 30 40 50 60 70 80 90 100 Cut-Off Keep whole shipment
- 67. © 2008 Prentice Hall, Inc. S6 – 67 An OC Curve Probability of Acceptance Percent defective | | | | | | | | | 0 1 2 3 4 5 6 7 8 100 – 95 – 75 – 50 – 25 – 10 – 0 – = 0.05 producer’s risk for AQL = 0.10 Consumer’s risk for LTPD LTPD AQL Bad lots Indifference zone Good lots Figure S6.9
- 68. © 2008 Prentice Hall, Inc. S6 – 68 AQL and LTPD Acceptable Quality Level (AQL) Poorest level of quality we are willing to accept Lot Tolerance Percent Defective (LTPD) Quality level we consider bad Consumer (buyer) does not want to accept lots with more defects than LTPD
- 69. © 2008 Prentice Hall, Inc. S6 – 69 Producer’s and Consumer’s Risks Producer's risk () Probability of rejecting a good lot Probability of rejecting a lot when the fraction defective is at or above the AQL Consumer's risk () Probability of accepting a bad lot Probability of accepting a lot when fraction defective is below the LTPD
- 70. © 2008 Prentice Hall, Inc. S6 – 70 OC Curves for Different Sampling Plans n = 50, c = 1 n = 100, c = 2
- 71. © 2008 Prentice Hall, Inc. S6 – 71 Average Outgoing Quality where Pd = true percent defective of the lot Pa = probability of accepting the lot N = number of items in the lot n = number of items in the sample AOQ = (Pd)(Pa)(N - n) N
- 72. © 2008 Prentice Hall, Inc. S6 – 72 Average Outgoing Quality 1. If a sampling plan replaces all defectives 2. If we know the incoming percent defective for the lot We can compute the average outgoing quality (AOQ) in percent defective The maximum AOQ is the highest percent defective or the lowest average quality and is called the average outgoing quality level (AOQL)
- 73. © 2008 Prentice Hall, Inc. S6 – 73 Automated Inspection Modern technologies allow virtually 100% inspection at minimal costs Not suitable for all situations
- 74. © 2008 Prentice Hall, Inc. S6 – 74 SPC and Process Variability (a) Acceptance sampling (Some bad units accepted) (b) Statistical process control (Keep the process in control) (c) Cpk >1 (Design a process that is in control) Lower specification limit Upper specification limit Process mean, m Figure S6.10