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The Great Blue Bead Company Quality Is Our Priority Based on a term project by Elizabeth A. Hoffman.

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Presentation on theme: "The Great Blue Bead Company Quality Is Our Priority Based on a term project by Elizabeth A. Hoffman."— Presentation transcript:

1 The Great Blue Bead Company Quality Is Our Priority Based on a term project by Elizabeth A. Hoffman

2 The Great Blue Bead Company Forman: Mr. John Mainieri 6 willing workers A recorder 2 inspectors 1 chief inspector Quality Is Our Priority

3 The Great Blue Bead Company Production Process Grasp the paddle and mix the raw materials. Insert the paddle into the bead mixture. Raise the paddle at a 20 degree angle so that as many depressions as possible will hold a bead. Gently angel to one side so excess production beads fall off. We want to produce 21 beads per shift (worker). Inspectors count the beads independently and record the counts. Count both defectives and total beads produced.

4 The Great Blue Bead Company The chief inspector checks the counts and announces the results, which are written down by the recorder. The chief inspector dismisses the worker. When all six willing workers have produced the days quota, the foreman evaluates the results.

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6 ZERO DEFECTS ZERO DEFECTS

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9 UCL =.47 LCL = 0.00 DAY 1DAY 2 DAY 3 DAY 4 Mean =.21

10 Red Bead Experiment- Lessons for Managers Variation exists in systems and, if stable, can be predicted. –Although the exact number of red beads in any particular paddle is not predictable, we can describe statistically what we expect from the system. Adapted from the Management and Control of QUALITY, 4 th ed. by Evans and Lindsay, South- Western

11 Red Bead Experiment- Lessons for Managers All the variation in the production of red beads, and the variation from day to day of any willing worker, came entirely from the process itself. –Neither motivation nor threats had any influence. –Many managers believe that all variation is controllable and place blame on those who cannot do anything about it. Adapted from the Management and Control of QUALITY, 4 th ed. by Evans and Lindsay, South- Western

12 Red Bead Experiment- Lessons for Managers Numerical goals are often meaningless. –Merit pay and probation actually reward or penalize the system and lead to worker frustration. –There is no basis for assuming that the best willing workers of the past will be the best in the future. Adapted from the Management and Control of QUALITY, 4 th ed. by Evans and Lindsay, South- Western

13 Red Bead Experiment- Lessons for Managers Management is responsible for the system. The experiment shows bad management. –Procedures are rigid. –Willing workers have no say in improving the process. –Management is responsible for the incoming material, but did not work with the supplier to improve the inputs to the system. Adapted from the Management and Control of QUALITY, 4 th ed. by Evans and Lindsay, South- Western

14 Red Bead Experiment- Lessons for Managers –Management designed the system and decided to rely on inspection to control the process. Three inspectors are probably as costly as the six workers and add practically no value to the input. Adapted from the Management and Control of QUALITY, 4 th ed. by Evans and Lindsay, South- Western

15 Chapter 12 Statistical Applications in Quality and Productivity Management Introduction to the History of Quality Deming’s 14 Points of Management Common Cause Variation and Special Cause Variation Control Charts for the Proportion of Nonconforming Items Control Charts for the Range and Mean Chapter Objectives

16 Chapter 12 Statistical Quality Management Read sections 12.1 through 12.4 and 12.6 (skip 12.5) Do quick results 12.4e and 12.6e Do problems 12.5 and – use excel for the charts, but double check the control limits by hand Chapter Homework

17 Quality Management Focus on process improvement System, not individual Teamwork Customer satisfaction Organizational transformation No fear Requires investment, saves money

18 Dr. W. Edwards Deming B.S. in Electrical Engineering University of Wyoming, 1921 M.S. Mathematics and Mathematical Physics University of Colorado, 1925 Ph.D. from Yale University, 1928

19 Control Charts Sequential collection of variable data Control Chart to monitor variation –Focus on time –Study nature of variability Causes of Variation –Special or Assignable –Chance or Common

20 Control Limits Used to evaluate variation for: –Variation outside established control limits –Patterns over time Process Average + 3 standard deviations –Upper Control Limit (UCL) = process average + 3 standard deviations –Lower Control Limit (LCL) = process average – 3 standard deviations

21 Control Chart Patterns Special or Assignable Cause Variation This is a “signal.” Outside Control Limit

22 Control Chart Patterns Pattern over Time Increasing Trend The magic number is 8!

23 Control Chart Patterns Common Cause Variation “Static” vs. a “Signal” Within Control Limits

24 The p Chart Attribute Control Chart –For Proportion of Non-Conforming Items Control Limits

25 The p Chart Obtaining Upper and Lower Control Limits

26 Example: 12.3 Pg. 740

27 Example: 12.3 cont.

28 Summary The p Chart Control Chart for the Proportion Used to determine whether special or common Control Limits:

29 Control Charts for the Range and Mean Used when characteristic is measured numerically – called Variables Control Charts More sensitive than p chart Two charts –Variation in a process (range) –Process Average (mean) Interpret variation chart first (range)

30 The R Chart Must be examined first Out of control variation could cause misinterpretation of the mean chart Control Limits for the Range From table E.9, page E-18 d 3 represents the relationship between the standard deviation and the standard error d 2 represents the relationship between the standard deviation and range

31 Control Limits for the R Chart

32 Control Limits for the R Chart The “Simple” Way

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35 The Chart If the Range is in Control Measures the variability of the mean Control Limits for the Mean Chart Subgroups of size n OR

36 Example Page Control Limits for the Range

37 Example cont. The R Chart

38 Example cont. Control Limits for the Mean Use These Equations

39 Example cont. The Mean Chart

40 Summary Control Charts for the Range and Mean Two charts –Variation in a process –Process Average Interpret variation chart first (range) Control Limits for the Range:

41 Summary Control Charts for the Range and Mean Interpret mean chart second Control Limits for the Range: Subgroups of size n

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43 OR

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