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MIM 558 Comparative Operations Management

MIM 558 Comparative Operations Management. Dr. Alan Raedels, C.P.M. Performance Features Reliability Durability Conformance. Serviceability Aesthetics Perceived quality Procureability Responsiveness. Dimensions of Quality. Costs of Conformance Prevention Appraisal.

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MIM 558 Comparative Operations Management

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  1. MIM 558Comparative Operations Management Dr. Alan Raedels, C.P.M.

  2. Performance Features Reliability Durability Conformance Serviceability Aesthetics Perceived quality Procureability Responsiveness Dimensions of Quality

  3. Costs of Conformance Prevention Appraisal Costs of Nonconformance Internal failure External failure Cost of Quality

  4. 1. Plan improvement a. Measure and track quality status b. Set targets for improvement 2. Systematically search for problems Are we working on the right problem? Location, time, operation 3. Systematically search for causes Ask why 5 times Cause & effect diagrams Pareto diagrams Experimentation SQC 4. Propose corrective action 5. Verify that corrective action is effective 6. Standardize into permanent practice Six Steps to Improving Quality

  5. Wasteful Sampling and inspection add cost and decrease value Inaccurate Even 100% inspection is only 80% effective because of the possibility of human errors Impractical Inspection may involve destructive testing Wrong message Inspection communicates to people and suppliers that bad parts will still be tolerated. High risks In sampling and inspection there is a risk of accepting bad lots and rejecting good lots No continuous improvement Sampling is still inspection, not prevention, so that quality cannot be continuously improved. Disadvantages of Inspection

  6. Types of Statistical Quality Control Statistical Quality Control Process Acceptance Control Sampling Variables Attributes Variables Attributes Charts Charts

  7. Quality Characteristics • Characteristics for which you focus on defects • Classify products as either ‘good’ or ‘bad’, or count # defects • e.g., radio works or not • Categorical or discrete random variables Variables Attributes • Characteristics that you measure, e.g., weight, length • May be in whole or in fractional numbers • Continuous random variables

  8. Process Control Charts

  9. Control Chart Purposes • Show changes in data pattern • e.g., trends • Make correctionsbeforeprocess is out of control • Show causes of changes in data • Assignable causes • Data outside control limits or trend in data • Natural causes • Random variations around average

  10. Control Chart Types Continuous Numerical Data Categorical or Discrete Numerical Data Control Charts Variables Attributes Charts Charts R P C X Chart Chart Chart Chart

  11. Developing a Control Chart 1. Take 20-30 random samples of size n where n depends on type of control chart. X-bar, n=3-10; p, n=30-100; c, n=1 2. For each sample calculate sample statistic such as X-bar, R or p. 3. Plot the sample statistics sequentially 4. Calculate grand means and control limits 5. Evaluate results and recalculate control limits if necessary.

  12. X Chart • Type of variables control chart • Interval or ratio scaled numerical data • Shows sample means over time • Monitors process average • Example: Weigh samples of coffee & compute means of samples; Plot

  13. X Chart Control Limits From page 91, Tague Sample Range at Time i Sample Mean at Time i # Samples

  14. Factors for Computing Control Chart Limits

  15. R Chart • Type of variables control chart • Interval or ratio scaled numerical data • Shows sample ranges over time • Difference between smallest & largest values in inspection sample • Monitors variability in process • Example: Weigh samples of coffee & compute ranges of samples; Plot

  16. R Chart Control Limits From page 91, Tague Sample Range at Time i # Samples

  17. p Chart • Type of attributes control chart • Nominally scaled categorical data • e.g., good-bad • Shows % of nonconforming items • Example: Count # defective chairs & divide by total chairs inspected; Plot • Chair is either defective or not defective

  18. z = 2 for 95.5% limits; z = 3 for 99.7% limits # Defective Items in Sample i Size of sample i p Chart Control Limits

  19. c Chart • Type of attributes control chart • Discrete quantitative data • Shows number of nonconformities (defects) in a unit • Unit may be chair, steel sheet, car etc. • Size of unit must be constant • Example: Count # defects (scratches, chips etc.) in each chair of a sample of 100 chairs; Plot

  20. Use3 for 99.7% limits # Defects in Unit i # Units Sampled c Chart Control Limits

  21. Process Capability Cpk • Assumes that the process is: • under control • normally distributed

  22. Meanings of Cpk Measures Cpk = negative number Cpk = zero Cpk = between 0 and 1 Cpk = 1 Cpk > 1

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