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Tools of quality

Tools of quality. 7 QC Tools: The Lean Six Sigma Pocket Toolbook. Flowchart [p. 116] Check Sheet [p. 95] Histogram [p. 129] Pareto [p. 178] Cause-and-Effect [p. 49] Scatter [p. 228] Control Chart [p. 75]. Pareto Diagram. Cause and Effect Diagram.

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Tools of quality

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  1. Tools of quality

  2. 7 QC Tools: The Lean Six Sigma Pocket Toolbook • Flowchart [p. 116] • Check Sheet [p. 95] • Histogram [p. 129] • Pareto [p. 178] • Cause-and-Effect [p. 49] • Scatter [p. 228] • Control Chart [p. 75] Developed by Jim Grayson, Ph.D.

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

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

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

  6. 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

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

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

  9. 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.

  10. Choosing the Appropriate Control Chart (MJ II, p. 37) Attribute (counts) Variable (measurable) The Lean Six Sigma Pocket Toolbook, p. 123 Six Sigma Mem Jogger p. 76 Defect Defective

  11. Different types of control charts Attribute (or count) data Situation Chart Control Limits Number of defects, accidents or flaws # of accidents/week # of breakdowns/week # of flaws on a product C U Six Sigma Memory Jogger, p. 78. source: Brian Joiner, Fourth Generation Management, p. 266-267.

  12. Different types of control charts Attribute (or classification) data Situation Chart Control Limits Fraction of defectives fraction of orders not processed perfectly on first trial (first pass yield) fraction of requests not processed within 15 minutes p np Six Sigma Memory Jogger, p. 78. source: Brian Joiner, Fourth Generation Management, p. 266-267.

  13. Different types of control charts Variables (or measurement ) data Situation Chart Control Limits Variables data, sets of measurements X-”BAR” CHART Xbar and R Charts R CHART See MJ II p. 42 for constants A2, D3 and D4. Six Sigma Memory Jogger, p. 79. source: Brian Joiner, Fourth Generation Management, p. 266-267.

  14. Different types of control charts Variables (or measurement ) data Situation Chart Control Limits Variables data, sets of measurements X-”BAR” CHART Xbar and R Charts R CHART See MJ II p. 42 for constants A2, D3 and D4. Six Sigma Memory Jogger, p. 79. source: Brian Joiner, Fourth Generation Management, p. 266-267.

  15. Parameters for Creating X-bar Charts Six Sigma Memory Jogger, p. 81 MSWD p. 204.

  16. 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. Developed by Jim Grayson, Ph.D.

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

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

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

  20. 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.

  21. Identifying Special Causes of Variation source: Brian Joiner, Fourth Generation Management, pp. 260. Six Sigma Memory Jogger, p. 84-85. Developed by Jim Grayson, Ph.D.

  22. 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.

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

  24. 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.

  25. 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.

  26. Statistical Process Control Capability Analysis Conformance Analysis Eliminate Assignable Cause Investigate for Assignable Cause • Capability analysis • What is the currently "inherent" capability of my process when it is "in control"? • Conformance analysis • SPC charts identify whencontrol has likely been lost and assignable cause variation has occurred • Investigatefor assignable cause • Find “Root Cause(s)” of Potential Loss of Statistical Control • Eliminate or replicate assignable cause • Need Corrective Action To Move Forward

  27. 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.

  28. Process capability EXCEL: =NORMDIST(x, mean, std dev,1) to calculate percent non-conforming material.

  29. The Statistical Meaning of Six Sigma X+2s X+1sA X X-1sA X-2sA X-3sA X X+3sA X+6sB X-6sB • Estimate standard deviation: • Look at standard deviation relative to specification limits • Don’t confuse control limits with specification limits: a process can be out of control, yet be incapable = / d s ˆ R 2 Process capability measure Upper Specification Limit (USL) Lower Specification Limit (LSL) Process A (with st. dev sA) x Cp P{defect}ppm 1 0.33 0.317 317,000 2 0.67 0.0455 45,500 3 1.00 0.0027 2,700 4 1.33 0.0001 63 5 1.67 0.0000006 0,6 6 2.00 2x10-9 0,00 3 Process B (with st. dev sB)

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