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Heizer supp 06
This document outlines the key concepts and steps for statistical process control using control charts. It discusses control charts for variables, which use x-charts to monitor central tendency and R-charts to monitor dispersion. The document explains how to set control limits for these charts using factors from tables, and the importance of using both charts together. It also briefly introduces control charts for attributes and concepts like process capability. The overall goal is to distinguish natural from assignable causes of variation.
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Heizer supp 06
- 1. Operations Management Supplement 6 – Statistical Process Control PowerPoint presentation to accompany Heizer/Render Principles of Operations Management, 7e Operations Management, 9e © 2008 Prentice Hall, Inc. S6 – 1
- 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 © 2008 Prentice Hall, Inc. S6 – 2
- 3. Outline – Continued Process Capability Process Capability Ratio (Cp) Process Capability Index (Cpk ) Acceptance Sampling Operating Characteristic Curve Average Outgoing Quality © 2008 Prentice Hall, Inc. S6 – 3
- 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 © 2008 Prentice Hall, Inc. S6 – 4
- 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 © 2008 Prentice Hall, Inc. S6 – 5
- 6. Statistical Process Control (SPC) 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 © 2008 Prentice Hall, Inc. S6 – 6
- 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” © 2008 Prentice Hall, Inc. S6 – 7
- 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 © 2008 Prentice Hall, Inc. S6 – 8
- 9. Samples To measure the process, we take samples and analyze the sample statistics following these steps Each of these represents one (a) Samples of the sample of five product, say five boxes of cereal boxes of cereal taken off the filling Frequency # # machine line, vary # # # from each other in # # # # weight # # # # # # # # # # # # # # # # # Figure S6.1 Weight © 2008 Prentice Hall, Inc. S6 – 9
- 10. Samples To measure the process, we take samples and analyze the sample statistics following these steps The solid line represents the (b) After enough distribution samples are taken from a stable process, they form a Frequency pattern called a distribution Figure S6.1 Weight © 2008 Prentice Hall, Inc. S6 – 10
- 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 Figure S6.1 Frequency Central tendency Variation Shape Weight Weight Weight © 2008 Prentice Hall, Inc. S6 – 11
- 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 Frequency present, the Prediction output of a process forms a distribution that Tim e is stable over Weight time and is Figure S6.1 predictable © 2008 Prentice Hall, Inc. S6 – 12
- 13. Samples To measure the process, we take samples and analyze the sample statistics following these steps ? ? ?? ? (e) If assignable ? ? ? ? causes are ? ? ? ? ?? ? ?? present, the Frequency ? process output is Prediction not stable over time and is not predicable Tim e Weight Figure S6.1 © 2008 Prentice Hall, Inc. S6 – 13
- 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 © 2008 Prentice Hall, Inc. S6 – 14
- 15. Process Control (a) In statistical control and capable of producing within Frequency control limits Lower control limit Upper control limit (b) In statistical control but not capable of producing within control limits (c) Out of control Size (weight, length, speed, etc.) Figure S6.2 © 2008 Prentice Hall, Inc. S6 – 15
- 16. Types of Data Variables Attributes Characteristics that Defect-related can take any real characteristics value Classify products May be in whole or as either good or in fractional bad or count numbers defects Continuous random Categorical or variables discrete random variables © 2008 Prentice Hall, Inc. S6 – 16
- 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 x=µ as the population mean µ 2. The standard deviation of the sampling distribution (σ x) will σ equal the population standard σx = n deviation (σ ) divided by the square root of the sample size, n © 2008 Prentice Hall, Inc. S6 – 17
- 18. Population and Sampling Distributions Three population Distribution of distributions sample means Mean of sample means = x Beta Standard deviation of σ the sample = σx = Normal n means Uniform | | | | | | | -3σ x -2σ x -1σ x x +1σ x +2σ x +3σ x 95.45% fall within ± 2σ x 99.73% of all x fall within ± 3σ x Figure S6.3 © 2008 Prentice Hall, Inc. S6 – 18
- 19. Sampling Distribution Sampling distribution of means Process distribution of means x=µ (mean) Figure S6.4 © 2008 Prentice Hall, Inc. S6 – 19
- 20. Control Charts forVariables 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 © 2008 Prentice Hall, Inc. S6 – 20
- 21. Setting Chart Limits For x-Charts when we know σ Upper control limit (UCL) = x + zσ x Lower control limit (LCL) = x - zσ x where x = mean of the sample means or a target value set for the process z = number of normal standard deviations σx = standard deviation of the sample means = σ/ n © 2008 Prentice Hall, Inc. σ = population standard S6 – 21
- 22. Setting Control Limits Hour 1 Hour Mean Hour Mean Sample Weight of 1 16.1 7 15.2 Number Oat Flakes 2 16.8 8 16.4 1 17 3 15.5 9 16.3 2 13 4 16.5 10 14.8 3 16 5 16.5 11 14.2 4 18 6 16.4 12 17.3 n=9 5 17 6 16 For 99.73% control limits, z = 3 7 15 8 17 UCLx = x + zσ x = 16 + 3(1/3) = 17 ozs 9 16 Mean 16.1 LCLx = x - zσ x = 16 - 3(1/3) = 15 ozs σ= 1 © 2008 Prentice Hall, Inc. S6 – 22
- 23. Setting Control Limits Control Chart for sample of Variation due Out of to assignable 9 boxes control causes 17 = UCL Variation due to 16 = Mean natural causes 15 = LCL Variation due | | | | | | | | | | | | to assignable 1 2 3 4 5 6 7 8 9 10 11 12 Out of causes Sample number control © 2008 Prentice Hall, Inc. S6 – 23
- 24. Setting Chart Limits For x-Charts when we don’t know σ Upper control limit (UCL) = x + A2R Lower control limit (LCL) = x - A2R where R = average range of the samples A2 = control chart factor found in Table S6.1 x = mean of the sample means © 2008 Prentice Hall, Inc. S6 – 24
- 25. Control Chart Factors 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 Table S6.1 © 2008 Prentice Hall, Inc. S6 – 25
- 26. Setting Control Limits Process average x = 12 ounces Average range R = .25 Sample size n = 5 © 2008 Prentice Hall, Inc. S6 – 26
- 27. Setting Control Limits Process average x = 12 ounces Average range R = .25 Sample size n = 5 UCLx = x + A2R = 12 + (.577)(.25) = 12 + .144 = 12.144 ounces From Table S6.1 © 2008 Prentice Hall, Inc. S6 – 27
- 28. Setting Control Limits Process average x = 12 ounces Average range R = .25 Sample size n = 5 UCLx = x + A2R UCL = 12.144 = 12 + (.577)(.25) = 12 + .144 Mean = 12 = 12.144 ounces LCLx = x - A2R LCL = 11.857 = 12 - .144 = 11.857 ounces © 2008 Prentice Hall, Inc. S6 – 28
- 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 © 2008 Prentice Hall, Inc. S6 – 29
- 30. Setting Chart Limits For R-Charts Upper control limit (UCLR) = D4R Lower control limit (LCLR) = D3R where R = average range of the samples D3 and D4 = control chart factors from Table S6.1 © 2008 Prentice Hall, Inc. S6 – 30
- 31. Setting Control Limits Average range R = 5.3 pounds Sample size n = 5 From Table S6.1 D4 = 2.115, D3 = 0 UCLR = D4R UCL = 11.2 = (2.115)(5.3) = 11.2 pounds Mean = 5.3 LCLR = D3 R LCL = 0 = (0)(5.3) = 0 pounds © 2008 Prentice Hall, Inc. S6 – 31
- 32. Mean and RangeCharts (a) These (Sampling mean is sampling shifting upward but distributions range is consistent) result in the charts below UCL (x-chart detects x-chart shift in central tendency) LCL UCL (R-chart does not R-chart detect change in mean) LCL Figure S6.5 © 2008 Prentice Hall, Inc. S6 – 32
- 33. Mean and RangeCharts (b) These (Sampling mean sampling is constant but distributions dispersion is result in the increasing) charts below UCL (x-chart does not x-chart detect the increase in dispersion) LCL UCL (R-chart detects R-chart increase in dispersion) LCL Figure S6.5 © 2008 Prentice Hall, Inc. S6 – 33
- 34. Steps In CreatingControl 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 © 2008 Prentice Hall, Inc. S6 – 34
- 35. Manual and Automated Control Charts © 2008 Prentice Hall, Inc. S6 – 35
- 36. Control Charts forAttributes 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) © 2008 Prentice Hall, Inc. S6 – 36
- 37. Control Limits forp-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 + zσ p ^ p(1 - p) σp = ^ n LCLp = p - zσ p ^ p = mean fraction defective in the sample z = number of standard deviations σp = standard deviation of the sampling distribution ^ n = sample size © 2008 Prentice Hall, Inc. S6 – 37
- 38. p-Chart for DataEntry 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 80 (.04)(1 - .04) p= = .04 σp = = .02 (100)(20) ^ 100 © 2008 Prentice Hall, Inc. S6 – 38
- 39. p-Chart for DataEntry UCLp = p + zσ p = .04 + 3(.02) = .10 ^ LCLp = p - zσ p = .04 - 3(.02) = 0 ^ .11 – .10 – UCLp = 0.10 .09 – Fraction defective .08 – .07 – .06 – .05 – .04 – p = 0.04 .03 – .02 – .01 – LCLp = 0.00 .00 – | | | | | | | | | | 2 4 6 8 10 12 14 16 18 20 Sample number © 2008 Prentice Hall, Inc. S6 – 39
- 40. p-Chart for DataEntry UCLp = p + zσ p = .04 + 3(.02) = .10 ^ Possible LCLp = p - zσ p = .04 - 3(.02) = 0 ^ assignable causes present .11 – .10 – UCLp = 0.10 .09 – Fraction defective .08 – .07 – .06 – .05 – .04 – p = 0.04 .03 – .02 – .01 – LCLp = 0.00 .00 – | | | | | | | | | | 2 4 6 8 10 12 14 16 18 20 Sample number © 2008 Prentice Hall, Inc. S6 – 40
- 41. Control Limits forc-Charts Population will be a Poisson distribution, but applying the Central Limit Theorem allows us to assume a normal distribution for the sample statistics UCLc = c + 3 c LCLc = c - 3 c c = mean number defective in the sample © 2008 Prentice Hall, Inc. S6 – 41
- 42. c-Chart for CabCompany c = 54 complaints/9 days = 6 complaints/day UCLc = c + 3 c 14 – UCLc = 13.35 14 Number defective =6+3 6 12 – = 13.35 10 – 8 – 6 – c= 6 LCLc = c - 3 c 4 – =6-3 6 2 – LCLc = 0 0 – | | | | | | | | | =0 1 2 3 4 5 6 7 8 9 Day © 2008 Prentice Hall, Inc. S6 – 42
- 43. Managerial Issues and Control Charts Three major management decisions: Select points in the processes that need SPC Determine the appropriate charting technique Set clear policies and procedures © 2008 Prentice Hall, Inc. S6 – 43
- 44. Which Control Chartto Use Variables Data 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 © 2008 Prentice Hall, Inc. S6 – 44
- 45. Which Control Chartto Use Attribute Data 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 © 2008 Prentice Hall, Inc. S6 – 45
- 46. Which Control Chartto Use Attribute Data 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 © 2008 Prentice Hall, Inc. S6 – 46
- 47. Patterns in ControlCharts Upper control limit Target Lower control limit Normal behavior. Process is “in control.” Figure S6.7 © 2008 Prentice Hall, Inc. S6 – 47
- 48. Patterns in ControlCharts Upper control limit Target Lower control limit One plot out above (or below). Investigate for Figure S6.7 cause. Process is “out of control.” © 2008 Prentice Hall, Inc. S6 – 48
- 49. Patterns in ControlCharts Upper control limit Target Lower control limit Trends in either direction, 5 plots. Figure S6.7 Investigate for cause of progressive change. © 2008 Prentice Hall, Inc. S6 – 49
- 50. Patterns in ControlCharts Upper control limit Target Lower control limit Two plots very near lower (or upper) Figure S6.7 control. Investigate for cause. © 2008 Prentice Hall, Inc. S6 – 50
- 51. Patterns in ControlCharts Upper control limit Target Lower control limit Run of 5 above (or below) central line. Figure S6.7 Investigate for cause. © 2008 Prentice Hall, Inc. S6 – 51
- 52. Patterns in ControlCharts Upper control limit Target Lower control limit Erratic behavior. Investigate. Figure S6.7 © 2008 Prentice Hall, Inc. S6 – 52
- 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 © 2008 Prentice Hall, Inc. S6 – 53
- 54. Process Capability Ratio Upper Specification - Lower Specification Cp = 6σ 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 © 2008 Prentice Hall, Inc. S6 – 54
- 55. Process Capability Ratio Insurance claims process Process mean x = 210.0 minutes Process standard deviation σ = .516 minutes Design specification = 210 ± 3 minutes Upper Specification - Lower Specification Cp = 6σ © 2008 Prentice Hall, Inc. S6 – 55
- 56. Process Capability Ratio Insurance claims process Process mean x = 210.0 minutes Process standard deviation σ = .516 minutes Design specification = 210 ± 3 minutes Upper Specification - Lower Specification Cp = 6σ 213 - 207 = = 1.938 6(.516) © 2008 Prentice Hall, Inc. S6 – 56
- 57. Process Capability Ratio Insurance claims process Process mean x = 210.0 minutes Process standard deviation σ = .516 minutes Design specification = 210 ± 3 minutes Upper Specification - Lower Specification Cp = 6σ 213 - 207 = = 1.938 Process is 6(.516) capable © 2008 Prentice Hall, Inc. S6 – 57
- 58. Process Capability Index Upper Lower Cpk = minimum of Specification - x , x - Specification Limit Limit 3σ 3σ 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 © 2008 Prentice Hall, Inc. S6 – 58
- 59. Process Capability Index New Cutting Machine New process mean x = .250 inches Process standard deviation σ = .0005 inches Upper Specification Limit = .251 inches Lower Specification Limit = .249 inches © 2008 Prentice Hall, Inc. S6 – 59
- 60. Process Capability Index New Cutting Machine New process mean x = .250 inches Process standard deviation σ = .0005 inches Upper Specification Limit = .251 inches Lower Specification Limit = .249 inches (.251) - .250 Cpk = minimum of , (3).0005 © 2008 Prentice Hall, Inc. S6 – 60
- 61. Process Capability Index New Cutting Machine New process mean x = .250 inches Process standard deviation σ = .0005 inches Upper Specification Limit = .251 inches Lower Specification Limit = .249 inches (.251) - .250 .250 - (.249) Cpk = minimum of , (3).0005 (3).0005 Both calculations result in New machine is .001 Cpk = = 0.67 NOT capable .0015 © 2008 Prentice Hall, Inc. S6 – 61
- 62. Interpreting Cpk Cpk = negative number Cpk = zero Cpk = between 0 and 1 Cpk = 1 Cpk > 1 Figure S6.8 © 2008 Prentice Hall, Inc. S6 – 62
- 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 © 2008 Prentice Hall, Inc. S6 – 63
- 64. Acceptance Sampling Form of quality testing used for incoming materials or finished goods Take samples at random from a lot Rejected lots can be: (shipment) of items Inspect each of theReturnedthethe items in to sample supplier Decide whether to reject the whole lot Culled for based on the inspection results defectives Only screens lots; does not drive (100% inspection) quality improvement efforts © 2008 Prentice Hall, Inc. S6 – 64
- 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 © 2008 Prentice Hall, Inc. S6 – 65
- 66. The “Perfect” OCCurve Keep whole shipment P(Accept Whole Shipment) 100 – 75 – Return whole 50 – shipment 25 – Cut-Off 0 – | | | | | | | | | | | 0 10 20 30 40 50 60 70 80 90 100 % Defective in Lot © 2008 Prentice Hall, Inc. S6 – 66
- 67. An OC Curve Figure S6.9 100 – 95 – α = 0.05 producer’s risk for AQL 75 – Probability of 50 – Acceptance 25 – 10 – β = 0.10 0 |– | | | | | | | | Percent 0 1 2 3 4 5 6 7 8 defective AQL LTPD Consumer’s risk for LTPD Good Indifference Bad lots lots zone © 2008 Prentice Hall, Inc. S6 – 67
- 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 © 2008 Prentice Hall, Inc. S6 – 68
- 69. Producer’s and Consumer’s Risks Producer's risk (α ) Probability of rejecting a good lot Probability of rejecting a lot when the fraction defective is at or above the AQL Consumer's risk (β ) Probability of accepting a bad lot Probability of accepting a lot when fraction defective is below the LTPD © 2008 Prentice Hall, Inc. S6 – 69
- 70. OC Curves forDifferent Sampling Plans n = 50, c = 1 n = 100, c = 2 © 2008 Prentice Hall, Inc. S6 – 70
- 71. Average Outgoing Quality (Pd)(Pa)(N - n) AOQ = N 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 © 2008 Prentice Hall, Inc. S6 – 71
- 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) © 2008 Prentice Hall, Inc. S6 – 72
- 73. Automated Inspection Modern technologies allow virtually 100% inspection at minimal costs Not suitable for all situations © 2008 Prentice Hall, Inc. S6 – 73
- 74. SPC and ProcessVariability Lower Upper specification specification limit limit (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) Process mean, µ Figure S6.10 © 2008 Prentice Hall, Inc. S6 – 74
Editor's Notes
- #7 Points which might be emphasized include: - Statistical process control measures the performance of a process, it does not help to identify a particular specimen produced as being “good” or “bad,” in or out of tolerance. - Statistical process control requires the collection and analysis of data - therefore it is not helpful when total production consists of a small number of units - While statistical process control can not help identify a “good” or “bad” unit, it can enable one to decide whether or not to accept an entire production lot. If a sample of a production lot contains more than a specified number of defective items, statistical process control can give us a basis for rejecting the entire lot. The issue of rejecting a lot which was actually good can be raised here, but is probably better left to later.
- #15 Students should understand both the concepts of natural and assignable variation, and the nature of the efforts required to deal with them.
- #16 This slide helps introduce different process outputs. It can also be used to illustrate natural and assignable variation.
- #17 Once the categories are outlined, students may be asked to provide examples of items for which variable or attribute inspection might be appropriate. They might also be asked to provide examples of products for which both characteristics might be important at different stages of the production process.
- #18 This slide introduces the difference between “natural” and “assignable” causes. The next several slides expand the discussion and introduce some of the statistical issues.
- #20 It may be useful to spend some time explicitly discussing the difference between the sampling distribution of the means and the mean of the process population.
- #38 Instructors may wish to point out the calculation of the standard deviation reflects the binomial distribution of the population
- #41 There is always a focus on finding and eliminating problems. But control charts find any process changed, good or bad. The clever company will be looking at Operator 3 and 19 as they reported no errors during this period. The company should find out why (find the assignable cause) and see if there are skills or processes that can be applied to the other operators.
- #42 Instructors may wish to point out the calculation of the standard deviation reflects the Poisson distribution of the population where the standard deviation equals the square root of the mean
- #48 Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
- #49 Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
- #50 Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
- #51 Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
- #52 Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
- #53 Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
- #64 Here again it is useful to stress that acceptance sampling relates to the aggregate, not the individual unit. You might also discuss the decision as to whether one should take only a single sample, or whether multiple samples are required.
- #65 Here again it is useful to stress that acceptance sampling relates to the aggregate, not the individual unit. You might also discuss the decision as to whether one should take only a single sample, or whether multiple samples are required.
- #66 You can use this and the next several slides to begin a discussion of the “quality” of the acceptance sampling plans. You will find additional slides on “consumer’s” and “producer’s” risk to pursue the issue in a more formal manner in subsequent slides.
- #69 Once the students understand the definition of these terms, have them consider how one would go about choosing values for AQL and LTPD.
- #70 This slide introduces the concept of “producer’s” risk and “consumer’s” risk. The following slide explores these concepts graphically.
- #71 This slide presents the OC curve for two possible acceptance sampling plans.
- #72 It is probably important to stress that AOQ is the average percent defective , not the average percent acceptable.
- #73 It is probably important to stress that AOQ is the average percent defective , not the average percent acceptable.
- #75 This may be a good time to stress that an overall goal of statistical process control is to “do it better,” i.e., improve over time.