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Quality Management

CHAPTER 6. Quality Management. Learning Objectives. After completing this chapter you will: Understand the concept of Total Quality Management Know about how quality is measured and the different dimensions of quality

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Quality Management

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  1. CHAPTER 6 Quality Management

  2. Learning Objectives After completing this chapter you will: • Understand the concept of Total Quality Management • Know about how quality is measured and the different dimensions of quality • Understand the Define, Measure, Analyze, Improve and Control (DMAIC) quality improvement process • Know how to calculate the capability of a process • Understand how processes are monitored with control charts • Be familiar with acceptance sampling concepts

  3. Total Quality Management (TQM) • Total quality management is defined as managing the entire organization so that it excels on all dimensions of products and services that are important to the customer

  4. Quality Specifications • Design quality: Inherent value of the product in the marketplace • Dimensions include: • Performance • Features • Reliability/Durability • Serviceability • Aesthetics • Perceived Quality. • Conformance quality: Degree to which the product or service design specifications are met

  5. Appraisal Costs External Failure Costs Prevention Costs Internal FailureCosts Costs of Quality Costs of Quality

  6. ISO 9000 • Series of standards agreed upon by the International Organization for Standardization (ISO) • Adopted in 1987 • More than 100 countries • A prerequisite for global competition? • ISO 9000 directs you to "document what you do and then do as you documented"

  7. Six Sigma Quality • A philosophy and set of methods companies use to eliminate defects in their products and processes • Seeks to reduce variation in the processes that lead to product defects • The name, “six sigma” refers to the variation that exists within plus or minus three standard deviations of the process outputs

  8. Six Sigma Quality (Continued) • Six Sigma allows managers to readily describe process performance using a common metric: Defects Per Million Opportunities (DPMO)

  9. Six Sigma Quality (Continued) So, for every one million letters delivered this city’s postal managers can expect to have 1,000 letters incorrectly sent to the wrong address. Example of Defects Per Million Opportunities (DPMO) calculation. Suppose we observe 200 letters delivered incorrectly to the wrong addresses in a small city during a single day when a total of 200,000 letters were delivered. What is the DPMO in this situation? Cost of Quality: What might that DPMO mean in terms of over-time employment to correct the errors?

  10. Six Sigma Quality: DMAIC Cycle • Define, Measure, Analyze, Improve, and Control (DMAIC) • Developed by General Electric as a means of focusing effort on quality using a methodological approach • Overall focus of the methodology is to understand and achieve what the customer wants • A 6-sigma program seeks to reduce the variation in the processes that lead to these defects • DMAIC consists of five steps….

  11. Six Sigma Quality: DMAIC Cycle (Continued) 1. Define (D) Customers and their priorities 2. Measure (M) Process and its performance 3. Analyze (A) Causes of defects 4. Improve (I) Remove causes of defects 5. Control (C) Maintain quality

  12. Example to illustrate the process… • We are the maker of this cereal. Consumer reports has just published an article that shows that we frequently have less than 15 ounces of cereal in a box. • What should we do?

  13. Step 1 - Define • What is the critical-to-quality characteristic? • The CTQ (critical-to-quality) characteristic in this case is the weight of the cereal in the box.

  14. 2 - Measure • How would we measure to evaluate the extent of the problem? • What are acceptable limits on this measure?

  15. 2 – Measure (continued) • Let’s assume that the government says that we must be within ± 5 percent of the weight advertised on the box. • Upper Tolerance Limit = 16 + .05(16) = 16.8 ounces • Lower Tolerance Limit = 16 – .05(16) = 15.2 ounces

  16. 2. Measure (continued) • We go out and buy 1,000 boxes of cereal and find that they weight an average of 15.875 ounces with a standard deviation of .529 ounces. • What percentage of boxes are outside the tolerance limits?

  17. Process Mean = 15.875 Std. Dev. = .529 Upper Tolerance = 16.8 Lower Tolerance = 15.2 What percentage of boxes are defective (i.e. less than 15.2 oz)? Z = (x – Mean)/Std. Dev. = (15.2 – 15.875)/.529 = -1.276 NORMSDIST(Z) = NORMSDIST(-1.276) = .100978 Approximately, 10 percent of the boxes have less than 15.2 Ounces of cereal in them!

  18. Step 3 - Analyze - How can we improve the capability of our cereal box filling process? • Decrease Variation • Center Process • Increase Specifications

  19. Step 4 – Improve – How good is good enough?Motorola’s “Six Sigma” • 6s minimum from process center to nearest spec

  20. Motorola’s “Six Sigma” • Implies 2 ppB “bad” with no process shift • With 1.5s shift in either direction from center (process will move), implies 3.4 ppm “bad”.

  21. Step 5 – Control • Statistical Process Control (SPC) • Use data from the actual process • Estimate distributions • Look at capability - is good quality possible • Statistically monitor the process over time

  22. Analytical Tools for Six Sigma and ContinuousImprovement: Flow Chart No, Continue… Material Received from Supplier Inspect Material for Defects Defects found? Yes Can be used to find quality problems Return to Supplier for Credit

  23. 0.58 Diameter 0.56 0.54 0.52 0.5 0.48 0.46 0.44 1 2 3 4 5 6 7 8 9 10 11 12 Time (Hours) Analytical Tools for Six Sigma and Continuous Improvement: Run Chart Can be used to identify when equipment or processes are not behaving according to specifications

  24. Analytical Tools for Six Sigma and Continuous Improvement: Pareto Analysis Can be used to find when 80% of the problems may be attributed to 20% of the causes 80% Frequency Design Assy. Instruct. Purch. Training

  25. Analytical Tools for Six Sigma and Continuous Improvement: Checksheet Can be used to keep track of defects or used to make sure people collect data in a correct manner Monday • Billing Errors • Wrong Account • Wrong Amount • A/R Errors • Wrong Account • Wrong Amount

  26. Analytical Tools for Six Sigma and Continuous Improvement: Histogram Can be used to identify the frequency of quality defect occurrence and display quality performance Number of Lots 0 1 2 3 4 Defectsin lot Data Ranges

  27. Effect Analytical Tools for Six Sigma and Continuous Improvement: Cause & Effect Diagram The results or effect Possible causes: Machine Man Environment Method Material Can be used to systematically track backwards to find a possible cause of a quality problem (or effect)

  28. UCL 1020 1010 1000 LCL 990 980 970 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Analytical Tools for Six Sigma and Continuous Improvement: Control Charts Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality

  29. Other Six Sigma Tools • Failure Mode and Effect Analysis (DMEA) is a structured approach to identify, estimate, prioritize, and evaluate risk of possible failures at each stage in the process • Design of Experiments (DOE) a statistical test to determine cause-and-effect relationships between process variables and output

  30. Basic Forms of Variation Assignable variationis caused by factors that can be clearly identified and possibly managed Example: A poorly trained employee that creates variation in finished product output. Common variationis inherent in the production process Example: A molding process that always leaves “burrs” or flaws on a molded item.

  31. Statistical Quality Control • The quantitative aspects of quality management • Processes usually exhibit some variation in their output • Some variation can be controlled and others are inherent in the process

  32. High High Incremental Cost of Variability Incremental Cost of Variability Zero Zero Lower Spec Target Spec Upper Spec Lower Spec Target Spec Upper Spec Traditional View Taguchi’s View Taguchi’s View of Variation Traditional view is that quality within the LS and US is good and that the cost of quality outside this range is constant, where Taguchi views costs as increasing as variability increases, so seek to achieve zero defects and that will truly minimize quality costs.

  33. Process Capability • Process limits • Specification limits • How do the limits relate to one another?

  34. Process Capability Index, Cpk Capability Index shows how well parts being produced fit into design limit specifications. As a production process produces items small shifts in equipment or systems can cause differences in production performance from differing samples. Shifts in Process Mean

  35. Process Capability – A Standard Measure of How Good a Process Is. A simple ratio: Specification Width _________________________________________________________ Actual “Process Width” Generally, the bigger the better.

  36. Process Capability This is a “one-sided” Capability Index Concentration on the side which is closest to the specification - closest to being “bad”

  37. The Cereal Box Example • We are the maker of this cereal. Consumer reports has just published an article that shows that we frequently have less than 15 ounces of cereal in a box. • Let’s assume that the government says that we must be within ± 5 percent of the weight advertised on the box. • Upper Tolerance Limit = 16 + .05(16) = 16.8 ounces • Lower Tolerance Limit = 16 – .05(16) = 15.2 ounces • We go out and buy 1,000 boxes of cereal and find that they weight an average of 15.875 ounces with a standard deviation of .529 ounces.

  38. Cereal Box Process Capability • Specification or Tolerance Limits • Upper Spec = 16.8 oz • Lower Spec = 15.2 oz • Observed Weight • Mean = 15.875 oz • Std Dev = .529 oz

  39. What does a Cpk of .4253 mean? • An index that shows how well the units being produced fit within the specification limits. • This is a process that will produce a relatively high number of defects. • Many companies look for a Cpk of 1.3 or better… 6-Sigma company wants 2.0!

  40. Types of Statistical Sampling • Attribute (Go or no-go information) • Defectives refers to the acceptability of product across a range of characteristics. • Defects refers to the number of defects per unit which may be higher than the number of defectives. • p-chart application • Variable (Continuous) • Usually measured by the mean and the standard deviation. • X-bar and R chart applications

  41. UCL Statistical Process Control (SPC) Charts Normal Behavior LCL 1 2 3 4 5 6 Samples over time UCL Possible problem, investigate LCL 1 2 3 4 5 6 Samples over time UCL Possible problem, investigate LCL 1 2 3 4 5 6 Samples over time

  42. Control Limits are based on the Normal Curve x m z -3 -2 -1 0 1 2 3 Standard deviation units or “z” units.

  43. x Control Limits We establish the Upper Control Limits (UCL) and the Lower Control Limits (LCL) with plus or minus 3 standard deviations from some x-bar or mean value. Based on this we can expect 99.7% of our sample observations to fall within these limits. 99.7% LCL UCL

  44. Example of Constructing a p-Chart: Required Data Number of defects found in each sample Sample No. No. of Samples

  45. Statistical Process Control Formulas:Attribute Measurements (p-Chart) Given: Compute control limits:

  46. Example of Constructing a p-chart: Step 1 1. Calculate the sample proportions, p (these are what can be plotted on the p-chart) for each sample

  47. Example of Constructing a p-chart: Steps 2&3 2. Calculate the average of the sample proportions 3. Calculate the standard deviation of the sample proportion

  48. Example of Constructing a p-chart: Step 4 4. Calculate the control limits UCL = 0.0924 LCL = -0.0204 (or 0)

  49. UCL LCL Example of Constructing a p-Chart: Step 5 5. Plot the individual sample proportions, the average of the proportions, and the control limits

  50. Example of x-bar and R Charts: Required Data

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