Quality Control PDF PowerPoint Presentation

Because learning changes everything. ® Chapter 10 Quality Control © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.  What is Quality Control? LO 10.1...

Because learning changes everything. ® Chapter 10 Quality Control © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.  What is Quality Control? LO 10.1 Quality control: A process that evaluates output relative to a standard and takes corrective action when output doesn’t meet standards. If results are acceptable no further action is required. Unacceptable results call for correction action. Inspection alone is not sufficient to achieve a reasonable level of quality. Most organizations rely upon some inspection and a great deal of process control to achieve an acceptable level of quality. © McGraw Hill 3  Approaches to Quality LO 10.1 Assurance Access the text alternative for slide images. © McGraw Hill 4  Acceptance Sampling and LO 10.2 Process Control Inspection before and after production often involves acceptance sampling procedures. Monitoring during production process called process control. © McGraw Hill 5  Inspection LO 10.2 Inspection: An appraisal activity that compares goods or services to a standard. Inspection issues: How much to inspect and how often. At what points in the process inspection should occur. Whether to inspect in a centralized or on-site location. Whether data results from inspecting attributes or variables. © McGraw Hill 6  How Much to Inspect LO 10.2 Access the text alternative for slide images. © McGraw Hill 7  How Often to Inspect LO 10.2 Frequency of inspection depends on the rate at which: A process may go out of control. On number of lots being inspected. A stable process will require only infrequent checks. Many small lots will require more samples than a few large lots. © McGraw Hill 8  Where to Inspect in the Process LO 10.2 Typical inspection points: Raw materials and purchased parts. Finished products. Before a costly operation. Before an irreversible process. Before a covering process. © McGraw Hill 9  Example of Inspection at a LO 10.2 Hotel/Motel Inspection Point Characteristics Accounting/billing Accuracy, timeliness Building and grounds Appearance and safety Main desk Appearance, waiting times, accuracy of bills Maid service Completeness, productivity Personnel Appearance, manners, productivity Reservations/occupancy Over/underbooking, percent occupancy Restaurants Kitchen, menus, meals, bills Room service Waiting time, quality of food Supplies Ordering, receiving, inventories © McGraw Hill 10  Off-Site v s On-Site Inspection ersu LO 10.2 Effects on cost and level of disruption are a major issue in selecting centralized versus on-site inspection. Off-Site: Specialized tests that may best be completed in a lab. More specialized testing equipment. More favorable testing environment. On-Site: Quicker decisions are rendered. Avoidance of introduction of extraneous factors. Quality at the source. © McGraw Hill 11  Statistical Process Control (SPC) LO 10.2 Quality control seeks quality of conformance of a process. Does the output of a process conform to the intent of design? SPC is used to evaluate process output. Statistical evaluation of the output of a process. Helps us to decide if a process is “in control” or if corrective action is needed. © McGraw Hill 12  Process Variability LO 10.2 All processes generate output that exhibits some degree of variability. Two basic questions concerning variability: 1. Issue of process control. Are the variations random? If nonrandom variation is present, the process is considered to be unstable. 2. Issue of process capability. Given a stable process, is the inherent variability of the process within a range that conforms to performance criteria? © McGraw Hill 13  Variation LO 10.2 Two kinds of variability: Chance or random variations (common cause): Natural variations in the output of a process, created by countless minor factors. Assignable (special cause) variation: A variation whose cause can be identified. A nonrandom variation. © McGraw Hill 14  Sampling and Sampling LO 10.2 Distribution 1 Statistical process control involves periodically taking samples of process output and computing sample statistics: Sample means. The number of occurrences of certain outcome. Sample statistics exhibit variation. Sample statistics are used to judge the randomness of process variation. The variability of sample statistics described by its sampling distribution. © McGraw Hill 15  Sampling and Sampling LO 10.2 Distribution 2 The sampling distribution of means is normal, and it has less variability than the process distribution, which might not be normal. © McGraw Hill 16  Sampling and Sampling LO 10.2 Distribution 3 Percentage of values within given ranges in a normal distribution. © McGraw Hill 17  Control Process LO 10.3 Sampling and corrective action are only a part of the control process. Steps required for effective control: Define: What is to be controlled? Measure: How will measurement be accomplished? Compare: There must be a standard of comparison. Evaluate: Establish a definition of out of control. Correct: Uncover the cause of nonrandom variability and fix it. Monitor results: Verify that the problem has been eliminated. © McGraw Hill 18  Control Charts: The Voice of the LO 10.4 Process Control chart: A time-ordered plot of sample statistics obtained from an ongoing process (for example, sample means), used to determine if the variability exhibited reflects random variation. Control limits: The dividing lines between random and nonrandom deviations from the mean of the distribution. Upper and lower control limits define the range of acceptable variation. © McGraw Hill 19  Control Chart and Control Limits LO 10.4 Each point on the control chart represents a sample of n observations. Access the text alternative for slide images. © McGraw Hill 20  Control Limits LO 10.4 Control limits are based on the sampling distribution. © McGraw Hill 21  Type one and Type two Errors 1 LO 10.4 Type I error. Concluding a process is not in control when it actually is. The probability of rejecting the null hypothesis when the null hypothesis is true. Manufacturer’s risk. Type II error. Concluding a process is in control when it is not. The probability of failing to reject the null hypothesis when the null hypothesis is false. Consumer’s risk. © McGraw Hill 22  Type one and Type two Errors 2 LO 10.4 And the conclusion is that it is: In Control Out of Control If a process is actually: In Control No error Type I error (producer's risk ) Out of Control Type II error (consumer's risk ) No error © McGraw Hill 23  Type one Error LO 10.4 © McGraw Hill 24  Observations from Sample LO 10.4 Distribution Each observation is compared to the selected limits of the sampling distribution. © McGraw Hill 25  Control Charts for Variables LO 10.5 Variables generate data that are measured. Mean control charts. Used to monitor the central tendency of a process. x " x­bar" charts. Range control charts. Used to monitor the process dispersion. R-charts. © McGraw Hill 26  Establishing Control Limits LO 10.5 k k x i R i x  i 1 R  i 1 k k where where x Average of sample means R Average of sample ranges xi Mean of sample i Ri Range of sample i k Number of samples © McGraw Hill 27  x-Bar Charts: Control Limits LO 10.5 Used to monitor the central tendency of a process. Upper control limit UCLx  x  z x Lower control limit LCLx  x  z x where  x  n  x = Standard deviation of distribution of sample means  = Estimate of the process standard deviation n = Sample size z = The number of standard deviation that control limits are based x = Average of sample means © McGraw Hill 28  Sample Range: Control Limits LO 10.5 If the standard deviation of the process is unknown use sample range as a measure of process variability. UCLx  x  A2 R LCLx x  A2 R where A2 = A factor from Table 10.3 R = Average of sample ranges x = Average of sample means © McGraw Hill 29  Range Charts: Control Limits LO 10.5 Used to monitor process dispersion. UCLR D4 R LCLR D3 R where D3 = a control chart factor based on sample size, n D4 = a control chart factor based on sample size, n D3 and D4 are obtained from Table 10.3 © McGraw Hill 30  Factors for Three Sigma Control LO 10.5 Charts 1 Table 10.3: Factors for three-sigma control limits for x and R charts. Number of Factor Factors For R Charts Factors For R Charts observations for Chart , Lower Control Limit , Upper Control Limit , inSample, A2 D3 D4 n 2 1.88 0 3.27 3 1.02 0 2.57 4 0.73 0 2.28 5 0.58 0 2.11 6 0.48 0 2.00 7 0.42 0.08 1.92 8 0.37 0.14 1.86 9 0.34 0.18 1.82 10 0.31 0.22 1.78 © McGraw Hill 31  Factors for Three Sigma Control LO 10.5 Charts 2 Number of Factor Factors For R Charts Factors For R Charts observations for Chart , Lower Control Limit , Upper Control Limit , inSample , A2 D3 D4 n 11 0.29 0.26 1.74 12 0.27 0.28 1.72 13 0.25 0.31 1.69 14 0.24 0.33 1.67 15 0.22 0.35 1.65 16 0.21 0.36 1.64 17 0.20 0.38 1.62 18 0.19 0.39 1.61 19 0.19 0.40 1.60 20 0.18 0.41 1.59 Source: Adapted from Eugene Grant and Richard Leavenworth, Statistical Quality Control, 5th ed. 19 80. McGraw-Hill Education. © McGraw Hill 32  Using Mean and Range Charts LO 10.5 To determine initial control limits: 1. Obtain 20 to 25 samples. Compute appropriate sample statistic(s) for each sample. 2. Establish preliminary control limits. 3. Determine if any points fall outside of the control limits. 4. Plot the data on the control chart and check for patterns. 5. If no out-of-control signs are found, assume that the process is in control. If any out-of-control signals are found, investigate and correct causes of variations. Then resume the process. © McGraw Hill 33  Mean and Range Charts - LO 10.5 Example Access the text alternative for slide images. © McGraw Hill 34  Control Charts for Attributes LO 10.5 Are used when the process characteristic is counted rather than measured. p-chart: Used to monitor the proportion of defective items generated by a process. c-chart: Used to monitor the number of defects per unit. © McGraw Hill 35  Use of a p-Chart LO 10.5 When observations can be placed into one of two categories. a. Good or bad. b. Pass or fail. c. Operate or don’t operate. When the data consist of multiple samples of n observations each. © McGraw Hill 36  Managerial Considerations LO 10.5 Managers must make a number of important decisions: At what points in the process to use control charts. What size samples to take. What type of control chart to use. Variables. Attributes. How often samples should be taken. © McGraw Hill 39  Run Tests LO 10.6 Even if all points are within the control limits, the data may still not reflect a random process. Analysts often supplement control charts with a run test. Run test: Checks for patterns in a sequence of observations. Run is defined as: Sequence of observations with a certain characteristic, followed by one or more observations with a different characteristic. © McGraw Hill 40  Nonrandom Patterns LO 10.6 Access the text alternative for slide images. © McGraw Hill 41  Process Capability 1 LO 10.7 Once the stability of a process has been established, it is necessary to determine if the process is capable of producing output that is within an acceptable range: Specifications or tolerances. Range of acceptable values established by engineering design or customer requirements. Control limits. Process variability. Natural or inherent variability in a process. Process capability: The inherent variability of process output (process width) relative to the variation allowed by the design specification (specification width). © McGraw Hill 42  Process Capability 2 LO 10.7 Access the text alternative for slide images. © McGraw Hill 43  Process Capability Improvement LO 10.7 Requires reducing the process variability that is inherent in a process. Method Examples Simplify Eliminate steps, reduce the number of parts, use modular design. Standardize Use standard parts, standard procedures. Make mistake- Design parts that can only be assembled the correct proof way; have simple checks to verify a procedure has been performed correctly. Upgrade Replace worn-out equipment; take advantage of equipment technological improvements. Automate Substitute automated processing for manual processing. © McGraw Hill 46  Limitations of Capability Indexes LO 10.7 There are several risks of using capability index: The process may not be stable, capability index is meaningless. The process output may not be normally distributed, inferences about the fraction of output that isn’t acceptable will be incorrect. The process is not centered but C p is used, result is misleading. © McGraw Hill 48  Operations Strategy LO 10.7 Quality is a major consideration for virtually all customers: Achieving and maintaining quality standards is of strategic importance to all business organizations. Product and service design. Increase capability in order to move from extensive use of control charts and inspection to achieve desired quality outcomes. © McGraw Hill 49  End of Main Content Because learning changes everything. ® www.mheducation.com © McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.