Chapter 11 Lecture

1. Chapter 11 Lecture Quality, TQM/Six Sigma, Control Charts

2. TPS House (Liker, The Toyota Way.) Developed by Jim Grayson, Ph.D.

3. What is quality?

4. Deming Chain Reaction Improve Quality Costs Decrease Productivity Improves Capture the Market Stay in Business Provide jobs and more jobs Developed by Jim Grayson, Ph.D. Source: W. Edwards Deming, Out of the Crisis, p. 3.

5. Customer satisfaction Degree of Achievement Source: adapted from material presented by Kurt Hofmeister, ASI, in a 3-day QFD workshop at Texas Instruments in 1989. Developed by Jim Grayson, Ph.D.

6. Organizing for quality

7. Source: adapted from material presented by Kurt Hofmeister, ASI, in a 3-day QFD workshop at Texas Instruments in 1989. Developed by Jim Grayson, Ph.D.

8. Organizational Quality Models (Implementation Models) • Malcolm Baldrige National Quality Award • Six Sigma Developed by Jim Grayson, Ph.D.

9. Baldrige Criteria Organizational Profile: Environment, Relationships and Challenges 5 Human Resource Focus 2 Strategic Planning 1 Leadership 7 Business Results 3 Customer and Market Focus 6 Process Management 4 Measurement, Analysis and Knowledge Management Developed by Jim Grayson, Ph.D. http://www.quality.nist.gov/index.html

10. GE Six Sigma Brochure Developed by Jim Grayson, Ph.D.

11. What Makes Six Sigma Different? 1. Integrating the human and process elements of improvement. 2. Focusing on the bottom line. 3. Linking improvement tools in an overall approach. (Define - Measure - Analyze - Improve - Control) • Human Issues • Bottom line • Management leadership • Sense of urgency • Customer focus • Project teams • Culture change • Process Issues • Process improvement • Analysis of variance • Disciplined approach • Quantitative measures • Statistical methods • Process management Developed by Jim Grayson, Ph.D. Ronald Snee, Why Should Statisticians Pay Attention to Six Sigma, Quality Progress, September 1999, pp. 100-3.

12. “More and more, the language of GE is the language of Six Sigma, the quality initiative begun in late 1995. It has become central to GE’s ability to operate as a global whole. ‘Six Sigma’ refers to a standard of excellence defined as having no more than 3.4 defects per million - in anything, whether it’s manufacturing, billing or loan processing. GE says it will spend $500 million on Six Sigma projects this year and will get more than $2 billion in benefits.” “See Jack. See Jack run.” Thomas Stewart. Fortune, September 27, 1999, p. 132. (emphasis added) “Jack Welch tells his young management charges to master the Six Sigma discipline that leads to black belts if they want to move up at General Electric.” “This Kind of Black Belt Can Help You Score Some Points at Work.” Hal Lancaster. Wall Street Journal, Tuesday, September 14, 1999, p. B1. (emphasis added) Developed by Jim Grayson, Ph.D.

13. Why Six Sigma Works • Bottom line results created. • Senior management leadership is active. • A disciplined approach (DMAIC) is used. • Rapid project completion (3-6 months). • Clearly defines success. • Infrastructure (MBB, BB, GB) established. • Customers and processes are the focus. • A sound statistical approach is used. Ronald Snee, Why Should Statisticians Pay Attention to Six Sigma, Quality Progress, September 1999, pp. 100-3. Developed by Jim Grayson, Ph.D.

14. Motorola’s Six Steps to Six Sigma note: adapted from Motorola six step process 1. Define your product or service 2. Identify customers and their needs. 3. Determine how to satisfy the customer. 4. Identify the process for creating your product. 5. Eliminate waste and defects from the process. 6. Measure your results for continuous improvement. Developed by Jim Grayson, Ph.D.

15. Tools of quality

16. 7 QC Tools: The Lean Six Sigma Pocket Toolbook • Flowchart [p. 33-41] • Check Sheet [p. 78-81] • Histogram [p. 111-113] • Pareto [p. 142-144] • Cause-and-Effect [p. 146-147] • Scatter [p. 154-155] • Control Chart [p. 122-135] Developed by Jim Grayson, Ph.D.

17. Pareto Diagram Developed by Jim Grayson, Ph.D.

18. Cause and Effect Diagram Developed by Jim Grayson, Ph.D.

19. “Failure to understand variation is the central problem of management.” Developed by Jim Grayson, Ph.D.

20. Stable vs. Unstable process Stable process: a process in which variation in outcomes arises only from common causes. Unstable process: a process in which variation is a result of both common and special causes. Developed by Jim Grayson, Ph.D. source: Moen, Nolan and Provost, Improving Quality Through Planned Experimentation

21. Red Bead experiment Developed by Jim Grayson, Ph.D.

22. Red Bead Experiment What are the lessons learned? 1. 2. 3. 4. Developed by Jim Grayson, Ph.D.

23. Statistical Process Control: Control Charts Process Parameter • Track process parameter over time - mean - percentage defects • Distinguish between - common cause variation (within control limits) - assignable cause variation (outside control limits) • Measure process performance: how much common cause variation is in the process while the process is “in control”? Upper Control Limit (UCL) Center Line Lower Control Limit (LCL) Time Developed by Jim Grayson, Ph.D.

24. Exercise An automatic filling machine is used to fill 16 ounce cans of a certain product. Samples of size 5 are taken from the assembly line each hour and measured. The results of the first 25 subgroups are that X-double bar = 16.113 and R-bar = 0.330. What are the control limits for this process? Source: Shirland, Statistical Quality Control, problem 5.2. If the specification limits are USL = 16.539 and LSL = 15.829 is the process capable? Developed by Jim Grayson, Ph.D.

25. Given these charts, how do we know if the process is “in control”? Developed by Jim Grayson, Ph.D.

26. Conceptual view of SPC Developed by Jim Grayson, Ph.D. source: Donald Wheeler, Understanding Statistical Process Control

27. Process Stability vs. Process Capability Wheeler, Understanding Statistical Process Control Developed by Jim Grayson, Ph.D.

28. Advantages of Statistical Control 1. Can predict its behavior. 2. Process has an identity. 3. Operates with less variability. 4. A process having special causes is unstable. 5. Tells workers when adjustments should not be made. 6. Provides direction for reducing variation. 7. Plotting of data allows identifying trends over time. 8. Identifies process conditions that can result in an acceptable product. Developed by Jim Grayson, Ph.D. source: Juran and Gryna, Quality Planning and Analysis, p. 380-381.

29. Identifying Special Causes of Variation source: Brian Joiner, Fourth Generation Management, pp. 260. See also Lean Six Sigma Pocket Toolbook, p. 133-135. Developed by Jim Grayson, Ph.D.

30. Strategies for Reducing Special Causes of Variation • Get timely data so special causes are signaled quickly. • Put in place an immediate remedy to contain any damage. • Search for the cause -- see what was different. • Develop a longer term remedy. Developed by Jim Grayson, Ph.D. source: Brian Joiner, Fourth Generation Management, pp. 138-139.

31. “In a common cause situation, there is no such thing as THE cause.” Brian Joiner Developed by Jim Grayson, Ph.D.

32. Improving a Stable Process • Stratify -- sort into groups or categories; look for patterns. (e.g., type of job, day of week, time, weather, region, employee, product, etc.) • Experiment -- make planned changes and learn from the effects. (e.g., need to be able to assess and learn from the results -- use PDCA .) • Disaggregate -- divide the process into component pieces and manage the pieces. (e.g., making the elements of a process visible through measurements and data.) Developed by Jim Grayson, Ph.D. source: Brian Joiner, Fourth Generation Management, pp. 140-146.

33. A Conversation with Joseph Juran “Take this example: In finance we set a budget. The actual expenditure, month by month, varies - we bought enough stationery for three months, and that’s going to be a miniblip in the figures. Now, the statistician goes a step further and says, ‘How do you know whether it’s a miniblip or there’s a real change here?’ The statistician says, ‘I’ll draw you a pair of lines here. These lines are such that 95% of the time, you’re going to get variation between them.’ Now suppose something happens that’s clearly outside the lines. The odds are something’s amok. Ordinarily this is the result of something local, because the system is such that it operates in control. So supervision converges on the scene to restore the status quo. Notice the distinction between what’s chronic [common cause] and what’s sporadic [special cause]. Sporadic events we handle by the control mechanism. Ordinarily sporadic problems are delegable because the origin and remedy are local. Changing something chronic requires creativity, because the purpose is to get rid of the status quo - to get rid of waste. Dealing with chronic requires structured change, which has to originate pretty much at the top.” Source: A Conversation with Joseph Juran, Thomas Stewart, Fortune, January 11, 1999, p. 168-170. Developed by Jim Grayson, Ph.D.