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Managing Quality

Managing Quality. 6. PowerPoint presentation to accompany Heizer and Render Operations Management, 10e Principles of Operations Management, 8e PowerPoint slides by Jeff Heyl. Outline. Defining Quality Implications of Quality Ethics and Quality Management Total Quality Management

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Managing Quality

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  1. Managing Quality 6 PowerPoint presentation to accompany Heizer and Render Operations Management, 10e Principles of Operations Management, 8e PowerPoint slides by Jeff Heyl

  2. Outline • Defining Quality • Implications of Quality • Ethics and Quality Management • Total Quality Management • Continuous Improvement • Six Sigma • Employee Empowerment • TQM in Services • Statistical Process Control (SPC) • Control Charts for Variables • Control Charts for Attributes • Process Capability • Process Capability Ratio (Cp) • Process Capability Index (Cpk)

  3. Learning Objectives Define quality and TQM Explain Six Sigma Explain the use of a control chart Build -charts and R-charts Build p-charts Explain process capability and compute Cp and Cpk

  4. Sales Gains via • Improved response • Flexible pricing • Improved reputation Improved Quality Increased Profits Reduced Costs via • Increased productivity • Lower rework and scrap costs • Lower warranty costs Two Ways Quality Improves Profitability Figure 6.1

  5. Defining Quality The totality of features and characteristics of a product or service that bears on its ability to satisfy stated or implied needs American Society for Quality • Different Views • User-based • Manufacturing-based • Product-based

  6. Performance Features Reliability Conformance Durability Serviceability Aesthetics Perceived quality Value Key Dimensions of Quality

  7. Ethics and Quality Management • Operations managers must deliver healthy, safe, quality products and services • Poor quality risks injuries, lawsuits, recalls, and regulation • Organizations are judged by how they respond to problems • All stakeholders much be considered

  8. Create consistency of purpose • Lead to promote change • Build quality into the product; stop depending on inspections • Build long-term relationships based on performance instead of awarding business on price • Continuously improve product, quality, and service • Start training • Emphasize leadership Deming’s Fourteen Points Table 6.2

  9. Drive out fear • Break down barriers between departments • Stop haranguing workers • Support, help, and improve • Remove barriers to pride in work • Institute education and self-improvement • Put everyone to work on the transformation Deming’s Fourteen Points Table 6.2

  10. Continuous Improvement • Represents continual improvement of all processes • Involves all operations and work centers including suppliers and customers • People, Equipment, Materials, Procedures

  11. Lower limits Upper limits 2,700 defects/million 3.4 defects/million 6 Mean ±3 ±6 Six Sigma Program • A highly structured program developed by Motorola • A discipline – DMAIC • Also, • Statistical definition of a process that is 99.9997% capable, 3.4 defects per million opportunities (DPMO) Figure 6.4

  12. Define critical outputs and identify gaps for improvement Measure the work and collect process data Analyze the data Improve the process Control the new process to make sure new performance is maintained Six Sigma DMAIC Approach

  13. Employee Empowerment • Getting employees involved in product and process improvements • 85% of quality problems are due to process and material • Techniques • Build communication networks that include employees • Develop open, supportive supervisors • Move responsibility to employees • Build a high-morale organization • Create formal team structures

  14. TQM In Services • Service quality is more difficult to measure than the quality of goods • Service quality perceptions depend on • Intangible differences between products • Intangible expectations customers have of those products

  15. 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

  16. 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”

  17. 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

  18. Types of Data Variables Attributes • Characteristics that can take any real value • May be in whole or in fractional numbers • Continuous random variables • Defect-related characteristics • Classify products as either good or bad or count defects • Categorical or discrete random variables

  19. 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 Control Charts for Variables

  20. For x-Charts when we know s Upper control limit (UCL) = x + zsx Lower control limit (LCL) = x - zsx where x = mean of the sample means or a target value set for the process z = number of normal standard deviations sx = standard deviation of the sample means = s/ n s = population standard deviation n = sample size Setting Chart Limits

  21. Hour 1 Sample Weight of Number Oat Flakes 1 17 2 13 3 16 4 18 5 17 6 16 7 15 8 17 9 16 Mean 16.1 s = 1 Hour Mean Hour Mean 1 16.1 7 15.2 2 16.8 8 16.4 3 15.5 9 16.3 4 16.5 10 14.8 5 16.5 11 14.2 6 16.4 12 17.3 n = 9 UCLx = x + zsx = 16 + 3(1/3) = 17 ozs LCLx = x - zsx = 16 - 3(1/3) = 15 ozs Setting Control Limits For 99.73% control limits, z = 3

  22. Variation due to assignable causes Out of control 17 = UCL Variation due to natural causes 16 = Mean 15 = LCL Variation due to assignable causes | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 Out of control Sample number Setting Control Limits Control Chart for sample of 9 boxes

  23. For x-Charts when we don’t know s 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 Setting Chart Limits

  24. 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 Control Chart Factors Table S6.1

  25. Process average x = 12 ounces Average range R = .25 Sample size n = 5 Setting Control Limits

  26. 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 Setting Control Limits

  27. Process average x = 12 ounces Average range R = .25 Sample size n = 5 UCLx = x + A2R = 12 + (.577)(.25) = 12 + .144 = 12.144 ounces UCL = 12.144 Mean = 12 LCLx = x - A2R = 12 - .144 = 11.857 ounces LCL = 11.857 Setting Control Limits

  28. UCL = 11.524 x – 10.959 LCL – 10.394 11.5 – 11.0 – 10.5 – x Bar Chart Sample Mean | | | | | | | | | 1 3 5 7 9 11 13 15 17 0.8 – 0.4 – 0.0 – Range Chart UCL = 0.6943 R = 0.2125 LCL = 0 Sample Range | | | | | | | | | 1 3 5 7 9 11 13 15 17 Restaurant Control Limits For salmon filets at Darden Restaurants

  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

  30. 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 Setting Chart Limits For R-Charts

  31. Average range R = 5.3 pounds Sample size n = 5 From Table S6.1 D4 = 2.115, D3 = 0 UCLR = D4R = (2.115)(5.3) = 11.2 pounds UCL = 11.2 Mean = 5.3 LCLR = D3R = (0)(5.3) = 0 pounds LCL = 0 Setting Control Limits

  32. (a) These sampling distributions result in the charts below (Sampling mean is shifting upward but range is consistent) UCL (x-chart detects shift in central tendency) x-chart LCL UCL (R-chart does not detect change in mean) R-chart LCL Mean and Range Charts Figure S6.5

  33. (b) These sampling distributions result in the charts below (Sampling mean is constant but dispersion is increasing) UCL (x-chart does not detect the increase in dispersion) x-chart LCL UCL (R-chart detects increase in dispersion) R-chart LCL Mean and Range Charts Figure S6.5

  34. Control Charts for Attributes • 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)

  35. p(1 - p) n sp = UCLp = p + zsp ^ ^ LCLp = p - zsp ^ where p = mean fraction defective in the sample z = number of standard deviations sp = standard deviation of the sampling distribution n = sample size ^ Control Limits for p-Charts Population will be a binomial distribution, but applying the Central Limit Theorem allows us to assume a normal distribution for the sample statistics

  36. 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 (.04)(1 - .04) 100 80 (100)(20) sp = = .02 p = = .04 ^ p-Chart for Data Entry

  37. .11 – .10 – .09 – .08 – .07 – .06 – .05 – .04 – .03 – .02 – .01 – .00 – UCLp = 0.10 UCLp = p + zsp = .04 + 3(.02) = .10 ^ Fraction defective p = 0.04 LCLp = p - zsp = .04 - 3(.02) = 0 ^ LCLp = 0.00 | | | | | | | | | | 2 4 6 8 10 12 14 16 18 20 Sample number p-Chart for Data Entry

  38. .11 – .10 – .09 – .08 – .07 – .06 – .05 – .04 – .03 – .02 – .01 – .00 – UCLp = 0.10 UCLp = p + zsp = .04 + 3(.02) = .10 ^ Fraction defective p = 0.04 LCLp = p - zsp = .04 - 3(.02) = 0 ^ LCLp = 0.00 | | | | | | | | | | 2 4 6 8 10 12 14 16 18 20 Sample number p-Chart for Data Entry Possible assignable causes present

  39. 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. Which Control Chart to Use Table S6.3

  40. Attribute Data Using the p-Chart • Observations are attributes that can be categorized as good or bad (or pass–fail, or functional–broken), that is, in two states. • We deal with fraction, proportion, or percent defectives. • There are several samples, with many observations in each. For example, 20 samples of n = 100 observations in each. Which Control Chart to Use Table S6.3

  41. UCL UCL UCL UCL UCL UCL Target Target Target Target Target Target LCL LCL LCL LCL LCL LCL Patterns in Control Charts Erratic behavior. Trends in either direction, 5 plots. Progressive change. Two plots very near lower (or upper) control. Run of 5 above (or below) central line. One plot out above (or below). Process is “out of control.” Normal behavior. Process is “in control.”

  42. 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

  43. Upper Specification - Lower Specification 6s Cp = Process Capability Ratio • 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

  44. Process mean x = 210.0 minutes Process standard deviation s = .516 minutes Design specification = 210 ± 3 minutes Upper Specification - Lower Specification 6s Cp = Process Capability Ratio Insurance claims process

  45. Process mean x = 210.0 minutes Process standard deviation s = .516 minutes Design specification = 210 ± 3 minutes Upper Specification - Lower Specification 6s Cp = 213 - 207 6(.516) = = 1.938 Process Capability Ratio Insurance claims process

  46. Process mean x = 210.0 minutes Process standard deviation s = .516 minutes Design specification = 210 ± 3 minutes Upper Specification - Lower Specification 6s Cp = 213 - 207 6(.516) = = 1.938 Process Capability Ratio Insurance claims process Process is capable

  47. UpperSpecification - xLimit 3s Lowerx - Specification Limit 3s Cpk = minimum of , Process Capability Index • 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

  48. New process mean x = .250 inches Process standard deviation s = .0005 inches Upper Specification Limit = .251 inches Lower Specification Limit = .249 inches Process Capability Index New Cutting Machine

  49. New process mean x = .250 inches Process standard deviation s = .0005 inches Upper Specification Limit = .251 inches Lower Specification Limit = .249 inches (.251) - .250 (3).0005 Cpk = minimum of , Process Capability Index New Cutting Machine

  50. New process mean x = .250 inches Process standard deviation s = .0005 inches Upper Specification Limit = .251 inches Lower Specification Limit = .249 inches (.251) - .250 (3).0005 .250 - (.249) (3).0005 Cpk = minimum of , .001 .0015 Cpk = = 0.67 Process Capability Index New Cutting Machine Both calculations result in New machine is NOT capable

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