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CBE / MET 433

This document explores the principles of achieving world-class quality through statistical process control, emphasizing the necessity of operating "On-Target" and with "Minimum Variance." It discusses the importance of controlling variation in processes, illustrating the difference between good and bad parts. Highlighting Shewhart's concepts, it distinguishes between controlled and uncontrolled variations, explains the use of control charts, and underscores the significance of statistical measures in ensuring predictability and stability within manufacturing processes. Real-world examples from industry provide context and practical applications. ###

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CBE / MET 433

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  1. CBE / MET 433 20 April 2012 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”

  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”

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

  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.

  7. 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. 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.”

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

  10. Statistical Measures • Location: What can we use? • Dispersion: What can we use?

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

  12. = 1.375146 = 1.374978 = 1.374820 R = 0.00080 = 0.000750 = 0.00120 S = 0.0001697 = 0.0001697 = 0.0003576

  13. Controlled Variation 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.

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

  15. Questions?

  16. Questions?

  17. Questions?

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