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Chapter 12 Managing Production Operations

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  1. Chapter 12Managing Production Operations

  2. Managing Engineering and Technology Management Functions Managing Technology Personal Technology Planning Research Time Management Design Ethics Decision Making Production Career Organizing Quality Leading Marketing Controlling Project Management Advanced Organizer

  3. Chapter Objectives • Explain and be able to use the statistics of quality • Describe the quality revolution • Recognize the methods of work measurement

  4. What Is Quality? • “The degree of excellence of a thing” (Webster’s Dictionary) • “The totality of features and characteristics that satisfy needs” ( ASQC) • Fitness for use

  5. Definitions of Quality Fitness for use, or customer satisfaction • Quality of design • Quality of conformance ( or Quality of production)

  6. The Meaning of Quality Consumer’s Producer’s Perspective Perspective Quality of Conformance Quality of Design • Conformance to Spec. • Quality Char. Production Marketing • Cost • Price Fitness for Consumer Use The Meaning of Quality

  7. Quality Of Conformance • Ensuring product or service produced according to design • Depends on • design of production process • performance of machinery • materials • training

  8. Dimensions of Product Quality 1. Performance -- basic operating characteristics 2. Features --“extra” items added to basic features 3. Reliability -- probability product will operate over time 4. Conformance --meeting pre-established standards 5. Durability -- life span before replacement 6. Serviceability -- ease of getting repairs, speed & competence of repairs 7. Aesthetics -- look, feel, sound, smell or taste 8. Safety --freedom from injury or harm 9. Other perceptions--subjective perceptions based on brand name, advertising, etc

  9. Service Quality 1. Time & Timeliness -- customer waiting time, completed on time 2. Completeness -- customer gets all they asked for 3. Courtesy -- treatment by employees 4. Consistency -- same level of service for all customers 5. Accessibility & Convenience -- ease of obtaining service 6. Accuracy -- performed right every time 7. Responsiveness -- reactions to unusual situations

  10. The Cost of Quality Cost of Achieving Good Quality • Prevention costs • Quality planning costs • Product design costs • Process costs • Training costs • Information costs • Appraisal costs • Inspection and testing • Test equipment costs • Operator costs Cost of Poor Quality • Internal failure costs • Scrap costs • Rework costs • Process failure costs (Diagnostic) • Process downtime costs • Price-downgrading costs • External failure costs • Customer complaint costs • Product return costs • Warranty claims costs • Product liability costs • Lost sales costs

  11. $ Increasing Quality Quality Improvement and Quality Cost Total Quality Cost Failure Cost Appraisal Cost Prevention Cost

  12. Quality Control Approaches • Statistical process control (SPC) • Monitors production process to prevent poor quality • Acceptance sampling • Inspects random sample of product to determine if a lot is acceptable

  13. Statistical Process Control • Take periodic samples from process • Plot sample points on control chart • Determine if process is within limits • Prevent quality problems

  14. Variation • Common Causes • Variation inherent in a process • Can be eliminated only through improvements in the system • Special Causes • Variation due to identifiable factors • Can be modified through operator or management action

  15. Probability Distribution • Central tendency • Mean, Mode, Median • Dispersion • Std. deviation, Variance • Frequency function • Normal, Binomial, Poisson

  16. Types Of Data • Attribute data • Product characteristic evaluated with a discrete choice • Good/bad, yes/no • Variable data • Product characteristic that can be measured • Length, size, weight, height, time, velocity

  17. SPC Applied To Services • Nature of defect is different in services • Service defect is a failure to meet customer requirements • Monitor times, customer satisfaction

  18. Service Quality Examples • Hospitals • Timeliness, responsiveness, accuracy • Grocery Stores • Check-out time, stocking, cleanliness • Airlines • Luggage handling, waiting times, courtesy • Fast food restaurants • Waiting times, food quality, cleanliness

  19. Control Charts Commonly based on   3 • Sample mean: x-bar-charts • Sample range: R-charts • Sample std. deviation: s-charts • Fraction defective: p-charts • Number of defects: c-charts

  20. The Normal Distribution 95% 99.73% -1 -3 -2 =0 1 2 3

  21. Z Values in Control Charts • Smaller Z values make more sensitive charts (Type I error) • Z = 3.00 is standard • Compromise between sensitivity and Type II errors

  22. 10 9 1 2 3 4 5 6 7 8 Process Control Chart Upper control limit Central Line Lower control limit Sample number

  23. Interpretation of Control Charts No evidence of out-of-control, if • No sample points outside limits • Most points near process average • About equal number of points above & below centerline • Points appear randomly distributed

  24. Development of Control Charts • Based on in-control data • If non-random causes present, discard data • Correct control chart limits

  25. Control Charts For Attributes • p Charts • Calculate percent defectives in sample • c Charts • Count number of defects in item

  26. p-Chart

  27. p-Chart Example 20 samples of 100 pairs of jeans

  28. p-Chart Calculations

  29. 0.20 0.18 0.16 0.14 0.12 0.10 Proportion defective 0.08 0.06 0.04 0.02 0 0 0 0 8 8 10 10 2 2 4 4 6 6 12 12 14 14 16 16 18 18 20 20 Example p-Chart Sample number

  30. c-Chart

  31. c-Chart Example Count # of defects in 15 rolls of denim fabric

  32. c-Chart Calculations

  33. 24 21 18 15 Number of defects 12 9 6 3 0 6 8 2 4 10 12 14 Example c-Chart Sample number

  34. Control Charts for Variables • Mean chart (X-Bar Chart) • Monitors central tendency • Dispersion chart • R-Chart • s-Chart • Monitors amount of variation

  35. Range (R) Chart

  36. Sample Obs. 1 Obs. 2 Obs. 3 Obs. 4 Obs. 5 R 5.02 5.01 4.94 4.99 4.96 5.01 5.03 5.07 4.95 4.96 4.99 5.00 4.93 4.92 4.99 : : : : : : : : 5.01 4.98 5.08 5.07 4.99 R-Chart Example Slip-ring diameter (cm) (sample size =5) 1 4.98 0.08 2 5.00 0.12 3 4.97 0.08 10 5.03 0.10  50.09 1.15

  37. 3 Control Chart Factors Sample size -chart R-chart n A2 D3 D4 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

  38. R-Chart Calculations

  39. 0.30 0.25 0.20 Range 0.15 0.10 0.05 0.00 1 2 3 4 5 6 7 8 9 10 Example R-Chart Sample

  40. X-bar Chart Calculations

  41. 5.100 5.050 5.000 4.950 4.900 4.850 1 2 3 4 5 6 7 8 9 10 Example X-bar Chart X-bar Sample

  42. Using X-bar and R-Charts Together • Each measures process differently • Process average and variability must be in control

  43. Indications of “Process out of Control” • Sample data fall outside control limits • Theory of runs • 2 out of 3 beyond the warning limits • 4 out of 5 beyond the 1 limits • 8 consecutive on one side • Patterns

  44. UCL Zone A Zone B Zone C CL Zone C Zone B Zone A LCL Zones For Pattern Tests

  45. Control Chart Patterns • 8 consecutive points on one side of the center line. • 8 consecutive points up or down across zones. • 14 points alternating up or down. • 2 out of 3 consecutive points in zone A but still inside the control limits. • 4 out of 5 consecutive points in zone A or B.

  46. UCL UCL LCL LCL Control Chart Patterns Sample observations consistently below the center line Sample observations consistently above the center line

  47. UCL UCL LCL LCL Control Chart Patterns Sample observations consistently increasing Sample observations consistently decreasing

  48. Inspection & Sampling • 100% inspection • only with automated inspection • Sampling inspection • Single sampling • Double sampling • Multiple sampling

  49. Acceptance Sampling • Accept/reject entire lot based on sample results • Measures quality in percent defective • Not consistent with TQM of Zero Defects • Not suitable for JIT

  50. Sampling Plan • Guidelines for accepting lot • Single sampling plan • N = lot size • n = sample size (random) • c = acceptance number • d = number of defective items in sample • If d <= c, accept lot; else reject