Managing Quality

1. Managing Quality

2. Chapter Objectives Be able to: • Discuss the various definitions and dimensions of quality and why quality is important to operations and supply chains. • Describe the different costs of quality, including internal and external failure, appraisal, and prevention costs. • Know what TQM is, along with its seven core principles. • Calculate process capability ratios and indices and set up control charts for monitoring continuous variables and attributes. • Describe the key issues associated with acceptance sampling, as well as the use of OC curves. • Distinguish between Taguchi’s quality loss function and the traditional view of quality.

3. Managing Quality • Quality defined • Total cost of quality • Total quality management (TQM) • Statistical quality control • Managing quality across the supply chain.

4. Definitions of Quality • ASQ: • The characteristics of a product or service that bear on its ability to satisfy stated or implied needs • Fitness for use (value perspective) • Free from defects (conformance perspective) • How would you evaluate the quality of the following? • Software package • Hand-held vacuum cleaner • No-frills air flight

5. Strategic Quality Quality as a Competitive Advantage

6. Dimensions of Quality • Performance • Features • Reliability • Durability • Conformance • Aesthetics • Serviceability • Perceived Quality Which dimensions doyou think are directlyaffected by Operationsand Supply Chain activities?

7. Quality Dimension Examples

8. Defensive Quality • Quality analyzed in economic terms • Total Cost of Quality: • Failure Costs • Appraisal Costs • Prevention Costs

9. Total Cost of Quality — Traditional View

10. Zero Defects View The total costs of quality fall as defect levels decrease

11. Total Quality Management (TQM) Managing the entire organizationso that it excels in all dimensions important to the customer. Product developmentMarketing OperationsSupply chain Support services

12. TQM Principles • Customer focus • Leadership involvement • Continuous improvement • Employee empowerment • Quality assurance (including SQC or SPC) • Strategic partnerships • Strategic quality plan

13. TQM Principles Expanded • Customer focus • Each employee has a customer whether internal or external to the company • Leadership involvement • Must be ‘top’ down, throughout the company • If not, major cause of TQM failures • Continuous improvement • Supports other core principles

14. Continuous Improvement (CI) versus “Leaps” Forward Performance Time

15. TQM Principles Expanded • Employee empowerment • Key to success • Lack of empowerment major cause of TQM/SPC failures • Quality assurance • Quality Function Deployment (QFD) discussed in Chapter 6 • Statistical quality control (SQC), also called statistical process control (SPC) • Acceptance sampling (OC curve)

16. TQM to Quality Assurance “Did we do it right?” Switching Focus . . .

17. We Noted That Organizations Must ... • Understand which quality dimensions are important • Develop products and services that will meet users’ quality needs • Put in place business processes capable of meeting these needs • Verify that business processes are meeting the specifications

18. Six Sigma Methodology Core value is having less than 3.4 defects per million opportunities (DPMO). Key elements are: • Understanding and managing customer requirements • Aligning key business processes to achieve those requirements • Using rigorous data analysis to understand and ultimately minimize variation in those processes • Driving rapid and sustainable improvement to business processes.

19. Six Sigma Methodology Two basic Six Sigma processes are: • DMAIC (Define-Measure-Analyze-Improve-Control) — an updated version of the PDCA process promoted by Deming. • DMADV (Define-Measure-Analyze-Design-Verify)

20. The PDCA Cycle Do Plan Check Act

21. Common Improvement Tools • Cause and effect diagrams (aka “Fishbone” or Ishikawa diagrams) • Check sheets • Pareto analysis • Run charts and scatter plots • Bar graphs • Histograms

22. Flight delays at Midway A Services Example • Cause and Effect Diagrams • Check Sheets • Pareto Analysis

23. Problem: Delayed Flights • No one is sure why, but plenty of opinions • “Management by Fact” • CI Tools we will use: • Fishbone diagram • Check sheets • Pareto analysis

24. ASKS: What are the possiblecauses? Root cause analysis — open and narrow phases Cause and Effect Diagram

25. Generic C&E Diagram

26. Midway C&E diagram

27. Check Sheets (root cause analysis -- closed phase)

28. Pareto Analysis(sorted histogram) Late passengers Late arrivals 100 Late baggage to aircraft 85 Weather 70 Other (160) 65

29. Percent of each out of 480 total incidents ... Late passengers 21% Late arrivals 18% Late baggage to aircraft 15% Weather 14% Other 33%

30. Run Charts and Scatter Plots Measure Run Time Variable Y Scatter Variable X

31. Histograms Frequency Measurements

32. Answers the Question:Can the process provide acceptable quality consistently? Process Capability

33. Process Capability Ratio (Cp) Upper Tolerance Limit – Lower Tolerance Limit 6σ Where σ is the estimated standard deviation for the individual observations

34. Shown Graphically: Process Capability ratio of 1 (99.7% within tolerance range)

35. “Six Sigma Quality” When a process operates with 6σ variation centered between the tolerance limits, only 2 parts out of a billion will be unacceptable.

36. Process Capability Index (Cpk) • Used when the process is not precisely centered between the tolerance limits.

37. Discovering “problems” • Inspect every item • Expensive to do • Testing can be destructive, should be simply unnecessary • Statistical techniques Statistical process control (SPC) Acceptance Sampling

38. Statistical Process Control • “Representative” samples are measured • good, but not perfect, picture of process • Sampling by Variable (continuous values — length, weight, area, volume, etc.) • Sampling by Attribute (good, bad, # defects/unit, %)

39. Example: Fabric Dyeing • Rolls of fabric go through dyeing process • Target temperature of 140 degrees Too low . . . ? Too high . . . ? • Temperature must be “monitored” and action taken when something is “unusual” • Is temperature a “variable” or an “attribute”?

40. Step 1: Sampling the Process Observation Things should be working OK when we do this . . .

41. Step 2: Calculate the Mean and Range for Each Sample X = 139.8°R = 5.3°

42. Step 3A: Use These Values to Set Up X and R charts Upper control limit for X chart: UCLX = X + A2×R = 142.9 Lower control limit for X chart: LCLX = X – A2× R = 136.7

43. Step 3B: Use These Values to Set Up X and R charts (cont’d) Upper control limit for R chart: UCLR = D4× R = 11.2 Lower control limit for R chart: LCLR = D3× R = 0

44. Use the Charts to Plot the Following Data . . . Out of Control Sample

45. What conclusions can you draw? What is the process capability ratiofor our dyeing example? σ = 2.41 from sample data

46. What would need to be for us to have “” quality ? 12σ = UTL – LTL = 148 – 132 σ = 16/12 = 1.33

47. Sampling by Attribute • Gonzo Pizza is interested in tracking the proportion (%) of late deliveries • Like before, you take several samples of say, 50 observations each when things are “typical” • For each sample, you calculate the proportion of late deliveries and call this value p. For example: p = (8 late)/(50 deliveries) = 0.16

48. Gonzo Pizza (cont’d) For all samples, calculate the average p: 0.16 0.20 0.00 0.14 0.10 p = 0.10

49. Gonzo Pizza (cont’d) • Calculate standard deviation for the p-chart as follows: Where n = size of each sample = 50

50. Gonzo Pizza (cont’d) And the control limits are:UCLp = p + z× Sp = 0.226LCLp = p–z× Sp =– 0.026, or zeroHere zis 3, but can be chosen as other values to increase the sensitivity of the chart to changes in the process.