Statistical process control ppt @ bec doms
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Statistical process control ppt @ bec doms
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Statistical process control ppt @ bec doms
- 8. Types of Statistical Quality Control Statistical Quality Control Process Control Acceptance Sampling Variables Charts Attributes Charts
- 9. Natural and Assignable Variation
- 12. Process Control: Three Types of Process Outputs Frequency Lower control limit Size (Weight, length, speed, etc. ) Upper control limit ( b) In statistical control , but not capable of producing within control limits. A process in control (only natural causes of variation are present) but not capable of producing within the specified control limits; and (c) Out of control. A process out of control having assignable causes of variation. (a) In statistical control and capable of producing within control limits. A process with only natural causes of variation and capable of producing within the specified control limits .
- 13. The Relationship Between Population and Sampling Distributions Uniform Normal Beta Distribution of sample means Standard deviation of the sample means (mean) Three population distributions
- 14. Sampling Distribution of Means, and Process Distribution Sampling distribution of the means Process distribution of the sample
- 17. Theoretical Basis of Control Charts As sample size gets large enough, sampling distribution becomes almost normal regardless of population distribution. Central Limit Theorem
- 18. Theoretical Basis of Control Charts Mean Central Limit Theorem Standard deviation
- 19. Theoretical Basis of Control Charts Properties of normal distribution
- 20. Control Chart Types Control Charts R Chart Variables Charts Attributes Charts X Chart P Chart C Chart Continuous Numerical Data Categorical or Discrete Numerical Data
- 21. Statistical Process Control Steps Produce Good Provide Service Stop Process Yes No Take Sample Inspect Sample Find Out Why Create Control Chart Start Can we assign causes?
- 23. Control Chart for Samples of 9 Boxes Variation due to natural causes 17=UCL 16=Mean 15=LCL Variation due to assignable causes Variation due to assignable causes Out of control 1 2 3 4 5 6 7 8 9 10 11 12 Sample Number
- 24. X Chart Control Limits Range for sample i # Samples Mean for sample i From Table S6.1
- 25. Factors for Computing Control Chart Limits
- 27. R Chart Control Limits Range for Sample i # Samples From Table S6.1
- 30. Mean and Range Charts Complement Each Other
- 32. p Chart Control Limits # Defective Items in Sample i Size of sample i z = 2 for 95.5% limits; z = 3 for 99.7% limits
- 33. P-Chart for Data Entry Example UCL p =0.10 LCL p =0.00
- 35. c Chart Control Limits # Defects in Unit i # Units Sampled Use 3 for 99.7% limits
- 36. Patterns to Look for in Control Charts
- 39. Process Capability Ratio, C p
- 41. Meanings of C pk Measures C pk = negative number C pk = zero C pk = between 0 and 1 C pk = 1 C pk > 1
- 45. OC Curve 100% Inspection % Defective in Lot P(Accept Whole Shipment) 100% 0% Cut-Off Return whole shipment Keep whole shipment 1 2 3 4 5 6 7 8 9 10 0
- 46. OC Curve with Less than 100% Sampling P(Accept Whole Shipment) 100% 0% % Defective in Lot Cut-Off 1 2 3 4 5 6 7 8 9 10 0 Return whole shipment Keep whole shipment Probability is not 100%: Risk of keeping bad shipment or returning good one.
- 49. An Operating Characteristic (OC) Curve Showing Risks = 0.05 producer’s risk for AQL = 0.10 Consumer’s risk for LTPD Probability of Acceptance Percent Defective Bad lots Indifference zone Good lots LTPD AQL 0 1 2 3 4 5 6 7 8 100 95 75 50 25 10 0
- 50. OC Curves for Different Sampling Plans 1 2 3 4 5 6 7 8 9 10 0 % Defective in Lot P(Accept Whole Shipment) 100% 0% LTPD AQL n = 50, c = 1 n = 100, c = 2
- 51. Average Outgoing Quality Where: P d = true percent defective of the lot P a = probability of accepting the lot N = number of items in the lot n = number of items in the sample
- 53. Statistical Process Control - Identify and Reduce Process Variability Lower specification limit Upper specification limit (a) Acceptance sampling –[ Some bad units accepted; the “lot” is good or bad] (b) Statistical process control – [Keep the process in “control”] (c) c pk >1 – [Design a process that is in control]
Notas do Editor
- 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.
- This slide provides a framework for differentiating between “Process Control” and “Acceptance Sampling,” and “Variables” and “Attributes.” One might also raise the distinction between producer (process control) and customer (acceptance sampling). The next several slides deal with these distinctions.
- Students should understand both the concepts of natural and assignable variation, and the nature of the efforts required to deal with them.
- 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.
- This slide introduces the difference between “natural” and “assignable” causes. The next several slides expand the discussion and introduce some of the statistical issues.
- This slide helps introduce different process outputs. It can also be used to illustrate natural and assignable variation.
- 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.
- An example of a control chart. .
- The next three slides can be used in a discussion of the theoretical basis for statistical process control.
- This slide simply introduces the various types of control charts.
- This slide introduces the statistical control process. It may be helpful here to walk students through an example or two of the process. The first walk through should probably be for a manufacturing process. The next several slides present information about the various types of process control slides:
- The following slide provides much of the data from Table S6.1.
- Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated.
- 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.
- 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.
- Once the students understand the definition of these terms, have them consider how one would go about choosing values for AQL and LTPD.
- This slide introduces the concept of “producer’s” risk and “consumer’s” risk. The following slide explores these concepts graphically.
- This slide presents the OC curve for two possible acceptance sampling plans.
- It is probably important to stress that AOQ is the average percent defective , not the average percent acceptable.
- 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.