1. CBE 491 / CBE 433 3 Dec 2012 1 Statistical Process Control

2. World Class Quality (Shewhart..1960) “On-Target with Minimum Variance.” Operating “On-Target” requires a different way of thinking about our processes. Operating with “Minimum Variance” is achieved only when a process displays a reasonable degree of statistical control. “Good Parts” vs “Bad Parts” 2

3. World Class Quality (Shewhart..1960) “On-Target with Minimum Variance.” Operating “On-Target” requires a different way of thinking about our processes. Operating with “Minimum Variance” is achieved only when a process displays a reasonable degree of statistical control. “Good Parts” vs “Bad Parts” 3

4. 4 World Class Quality (Shewhart..with an in-control process) MAKE  PACK  SHIP

5. 5 World Class Quality (Shewhart..w/in-control process) MAKE  PACK  SHIP Challenges: Examples from industry: Polymer gum in fiberpac Corn sweetener “Crystal Clear”

6. Shewhart: “While every process displays variation, some processes display controlled variation while others display uncontrolled variation.” Controlled Variation: Stable, consistent pattern of variation over time. “Chance” causes. 6

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8. Shewhart: “While every process displays variation, some processes display controlled variation while others display uncontrolled variation.” Controlled Variation: Stable, consistent pattern of variation over time. “Chance” causes. Uncontrolled Variation: Pattern of variation that changes over time. “Assignable” causes. 8

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10. Shewhart: “While every process displays variation, some processes display controlled variation while others display uncontrolled variation.” Controlled Variation: Stable, consistent pattern of variation over time. “Chance” causes. Uncontrolled Variation: Pattern of variation that changes over time. “Assignable” causes. Shewhart: “A phenomenon will be said to be controlled when, through the use of past experience, we can predict, at least within limits, how the phenomenon may be expected to vary in the future.” 10

11. Statistical Control Predictability Use statistics to highlight uncontrolled variations Eliminate uncontrolled variations Next: Control Charts as a way to see uncontrolled variations. 11

12. Statistical Measures Location: What can we use? Dispersion: What can we use? 12

13. Statistical Measures Location: What can we use? Dispersion: What can we use? Look at example (Camshaft Bearings) 13

14. = 1.375146 = 1.374978 = 1.374820 R = 0.00080 = 0.000750 = 0.00120 S = 0.0001697 = 0.0001697 = 0.0003576 14

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16. Controlled Variation 16 Subgroups used (4-5) Reason: use of mean tends to normalize information (distribution of the subgroup average) Shewhart: pick subgroups to organize the data into a rational manner.

17. Uncontrolled Variation 17 Gather 20 – 30 subgroups before calculating the control limits

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24. 24 PS Exercise: Control Charts

25. 25 SPC Quick Summary

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27. 27 Control Chart Examples Data from 99 samples of the silicon content drawn from a blast furnace. Data organized into 33 subgroups of size 3.

28. 28 Control Chart Examples

29. 29 Control Chart Examples

30. 30 Control Chart Examples Data from 99 samples of the silicon content drawn from a blast furnace. Upon further inspection it was discovered that each sample was the first tap from each shift…thus 3 silicon values for each day. The sample subgrouping was supposed to represent each day, however it was recorded in columns not rows, thus 144, 150 and 180 was the 1 st day’s samples. The data was reorganized and rerun.

31. 31 Control Chart Examples

32. 32 Control Chart Examples

33. 33 Control Chart Examples Subgroups of 5 samples from each shift.

34. 34 Control Chart Examples Subgroups of 5 samples from each shift.

35. 35 Control Chart Examples Extruded vinyl sheet to be used for car dashboard. Thickness controlled by automatic system.

36. 36 Questions?

37. 37 Questions?