Chapter 7

1. Chapter 7 Quality Tools:From Process Performance to Process Perfection

2. Learning Objectives • Explain the function of the general-purpose quality analysis tools. • Explain how each quality tool aids in the QI story and DMAIC processes. • Explain how statistical process control can be used to prevent defects • from occurring. • Calculate control limits for X-bar charts, R-charts, P-charts, and C-charts. • Construct and interpret X-bar Charts, R-Charts, P-charts, and C-charts. • Describe and make computations for process capability using Cp and Cpk capability indices. • Describe how acceptance sampling works and the role of the operating characteristics curve. • Explain how Six Sigma quality relates to process capability. • Describe how moment-of-truth analysis can be used to improve service quality. • Describe Taguchi’s quality loss function and its implications. • Explain how customer relationship management systems relate to customer satisfaction • Describe how “recovery” applies to quality failures.

3. Quality Analysis Tools • Six Sigma’s DMAIC and TQM’s QI Story provide structure, but neither defines how activities are to be accomplished. That can be determined through the use of a broad set of analysis tools. Insert exhibit 7.1 DMAIC and QI

4. General-Purpose Quality Analysis Tools • Process Maps • Run Charts • Cause & Effect Diagram • Pareto Charts • Histograms • Check Sheets • Scatter Diagrams • Control Charts

5. General-Purpose Quality Analysis Tools:Process Maps • A visual representation of a process. • A Process Map for an Internet Retailer

6. General-Purpose Quality Analysis Tools:Run Charts Run Charts: Plotting a variable against time.

7. General-Purpose Quality Analysis Tools: Cause & Effect Diagram Machine Man Effect Environment Method Material Possible causes: • Can be used to systematically track backwards to find a possible cause of a quality problem (or effect) The results or effect

8. General-Purpose Quality Analysis Tools:Cause & Effect Diagram Also known as: Ishikawa Diagrams Fishbone Diagrams Root Cause Analysis

9. Data Analysis Example Exhibit 7.6: SleepCheap Hotel Survey Data

10. General-Purpose Quality Analysis Tools:Histogram • Can be used to identify the frequency of quality defect occurrence and display quality performance

11. General-Purpose Quality Analysis Tools:Pareto Analysis • A Variant of histogram that helps rank order quality problems so that most important can be identified 50.5% of complaints are that something is dirty 63.5% of complaints are about the bathroom

12. General-Purpose Quality Analysis Tools:Scatter Plots

13. General-Purpose Quality Analysis Tools:Checksheet Monday • Can be used to keep track of defects or used to make sure people collect data in the correct manner • Billing Errors • Wrong Account • Wrong Amount • A/R Errors • Wrong Account • Wrong Amount

14. General-Purpose Quality Analysis Tools: Control Charts • Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality

15. Controlling Process Variability:Statistical Process Control (SPC) • Common cause variability versus assignable cause variability • Common cause variability comes from random fluctuation inherent to the process. • Assignable cause variability is avoidable and not part of the process. • SPC takes advantage of our knowledge about the standardized distribution of these measures. • Process Control • Identifies potential problems before defects are created by watching the process unfold • It uses X-bar Charts, R-Charts, P-charts, and C-charts

16. X-bar Chart Steps • Measure a sample of the process output • Four to five units of output for most applications • Many (>25) samples • Calculate sample means ( X-bar ), grand mean (X-double bar), & ranges (R) • Compare the “X-bars” being plotted to the upper and lower control limits and look for “assignable cause” variability. • Assignable cause variability means that the process has changed.

17. Process Control • Cp and Cpk tell us whether the process will produce defective output as part of its normal operation. • i.e., is it “capable”? • Control charts are maintained on an ongoing basis so that operators can ensure that a process is not changing • i.e., drifting to a different level of performance • i.e., is it “in control”

18. SPC Steps • Measure a sample of the process output • Four to five units of output for most applications • Many (>25) samples • Calculate sample means ( X ), grand mean (X), & ranges (R) • Calculate “process capability” • Can you deliver within tolerances defined by the customer • Traditional standard is “correct 99.74% of the time” • Monitor “process control” • Is anything changing about the process? • In terms of mean or variation

19. X-Bar and R-Chart Construction Insert Exhibit 7.17

20. Control Charts: X-bar • Distinguishing between random fluctuation and fluctuation due to an assignable cause. • X-bar chart tracks the trend in sample means to see if any disturbing patterns emerge. • Steps: • Calculate Upper & Lower Control Limits (UCL & LCL). • Use special charts based on sample size • Plot X-bar value for each sample • Investigate “Nonrandom” patterns ?? ?? Exhibit 7.18 X-bar Chart for Example 7.2

21. Nonrandom Patterns on Control Charts • Investigate the process if X-bar or R chart illustrates: • One data point above +3 or below -3 • 9 points in a row, all above or all below the mean • 6 points in a row, all increasing or all decreasing • 14 points in a row alternating up and down • 4 out of 5 points in a row in Zone B or beyond • 15 points in a row in Zone 3, above or below the center line • 8 points in a row in Zone B, A, or beyond, on either side of the center line with no points in Zone C

22. R-charts • R-charts monitor variation within each sample. • R-charts are always used with X-bar charts. • Steps • Calculate Upper & Lower Control Limits (UCL & LCL). • Use special tables based on sample size. • Plot the R value for each sample • Investigate “Nonrandom” patterns ?? ?? Exhibit 7.22 R-Chart for Example 7.4

23. Process Capability • Capability Index: Quantifies the relationship between control limits and customer specifications. • A process is “capable” when all of the common cause variability occurs within the customer’s specification limits. • Cp is used to determine “capability” when the process is centered.

24. Cp CalculationFor Centered Processes • Cp compares the range of the customer’s expectations to the range of the process to make sure that all common cause variability is inside of the customer’s specifications. • Cp = UCS - LCS 6σ • UCS - Upper control specification • LCS - Lower control specification •  - Standard deviation of process performance • If Cp > 1.000 the process is considered capable. `

25. Example 7.4: Cp Calculation • Customer specification • Mean of .375 inches • + or - .002 inches • Therefore, customer specification limits at .373 and .377 • Process performance • Actual mean is .375 • Standard deviation is 0.0024 Cp = 0.377 – 0.373 6(0.0024) = 0.27778 The process is not capable.

26. Process Capability for Uncentered Processes • Some processes are intentionally allowed to “shift.” • In these cases, the range of process variability moves toward one of the customer specifications as the process shifts.

27. Process Capability for Uncentered Processes • As soon as one of the “tails” of the process distribution crosses the customer specifications, the process is no longer capable

28. Process Capability for Uncentered Processes • The process capability index for uncentered processes checks both ends of the distribution to ensure that the process has not shifted beyond the customer specifications.

29. Cpk Calculation • LCS - Lower control specification • UCS - Upper control specification • X - “Grand” mean of process performance •  - Standard deviation of process performance • If Cpk is > 1.000 then the process is “Capable” • Translation, we will produce good parts at least 99.74% of the time

30. Example 7.3: Cpk Calculation • Customer specification • Mean of .375 inches • + or - .002 inches • Therefore, customer specification limits at .373 and .377 • Process performance • Actual mean is .376 • Standard deviation is 0.0003 Cpk = min[ 0.376 – 0.373 , 0.377 – 0.376 ] 0.0009 0.0009 = min [3.333, 1.111] = 1.111 The process is capable.

31. Process Control Charts for Attributes • P-charts • Used to monitor the proportion or percentage of items defective in a given sample. UCL= LCL= n = the sample size = the long-run average and center line Z is the number of normal standard deviations for the desired confidence

32. Process Control Charts for Attributes • C-charts • Used to monitor the counts of noncomformities per unit. UCL = LCL =

33. Acceptance Sampling • Purposes • Sampling to accept or reject the immediate lot of product at hand • Ensure quality is within predetermined level

34. Acceptance Sampling • Acceptable Quality Level (AQL) • Is the max. acceptable percentage of defectives that defines a “good” lot • Producer’s risk is the probability of rejecting a good lot • Lot tolerance percent defective (LTPD) • Is the percentage of defectives that defines consumer’s rejection point • Consumer’s risk is the probability of accepting a bad lot • The sampling plan is developed based on risk tolerance to determine size of sample and number in sample that can be defective Exhibit 7.26 Operating Characteristics Curve

35. Six Sigma Quality • “Six sigma” refers to the variation that exists within plus or minus six standard deviations of the process outputs Exhibit 7.28 Process Capability for Six Sigma Quality

36. Six Sigma Quality • In “process capability” terms, Six Sigma means that control limits set at plus or minus 6 σ will be inside of the customer’s specifications. • This greatly reduces the likelihood of a defect occurring from common cause variability.

37. Six Sigma Quality – Role of interdependencies • 6 is often needed when products are complex. • At 3 quality, for example, the probability that an assembly of interdependent parts works, given “n” parts and the need for all parts to work: • 1 part = .99741 = 99.74% • 10 parts = .997410 = 97.43% • 50 parts = .997450 = 87.79% • 100 parts = .9974100 = 77.08% • 267 parts = .9974267 = 49.90% • 1000 parts = .99741000 = 7.40%

38. Six Sigma and Failure Rates • The odds of common cause variability creating a result that is 6 from the mean are 2 in 1 billion • 99.9999998% confident of a good outcome • In practice, process mean is allowed to shift ±1.5 

39. Taguchi Method • As deviation from the target increases, customers get increasingly dissatisfied and costs increase. • Traditional views define deviation in terms of being “good” or “defective.” Taguchi views deviation in terms of costs that occur even if the deviation is slight, and increasing costs as deviation increases.

40. Moment-of-Truth Analysis • Moment-of-Truth Analysis: The identification of the critical instances when a customer judges service quality and determines the experience enhancers, standard expectations, and experience detractors. • Experience enhancers: Experiences that make the customer feel good about the interaction and make the interaction better. • Standard expectations: Experiences that are expected and taken for granted. • Experience detractors: Experiences viewed by the customer as reducing the quality of service.

41. Recovery • There will always be times when customers do not get what they want. • Failure to meet customers’ expectations does not have to mean lost customers. • Recovery plans: Policies for how employees are to deal with quality failures so that customers will return. • Example: A recovery for a customer who has had a bad meal at a restaurant might include eliminating the charges for the meal, apologizing, and offering gift certificates for future meals.