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Understand Statistical Process Control and its Pitfalls (MFG851L)

Presented By : William A. Levinson , P.E.
(*) Single User Price. For multiple users please call 1-800-223-8720
Pre Recorded Webinar
60 minutes
  •  Thu, August 6, 2015
Event Description

Learn to Use a Control Chart Simulator to Illustrate the Effects of Variation and Accuracy on Quality.

Statistical process control does for discrete manufacturing operations what automatic process control does for chemical process operations. A control chart tells production personnel when to adjust the process, and when to leave it alone. In this session by expert speaker William A. Levinson, you will receive a control chart simulator that uses gun targets side by side with histograms and control charts to illustrate the effects of variation and accuracy on quality and also the behavior of the charts. (The chart stimulator works in Windows 7; but may not be usable on Apple systems.)

The session will also explain the fundamental principles behind statistical process control, which includes particularly important variation and accuracy concepts and will highlight the difference that random variation and special or assignable causes have. This webinar will also go beyond the textbook assumption that processes follow normal or bell curve distributions by describing what happens when these assumptions are not met. If your control charts don't seem to work properly (e.g. due to excessive false alarms, or points outside both control limits), this presentation will explain you why.

Session Highlights:

  • Know what way can accuracy and variation affect quality.
    • It is useful to treat the product specification as a gun target. A hit on the target is in specification, and if you miss it will be out of specification (scrap or rework).
    • The best results are achieved when the gun is aimed at the center of the target, i.e. the nominal (halfway between the specification limits) for a process that follows a bell curve distribution.
    • Less variation, i.e. a rifle as opposed to a musket is always better.
  • Know how control charts reflect undesirable changes in variation and the process mean.
    • Has our rifle (capable process) been replaced by a musket? The sample range (R) and sample standard deviation (s) charts detect undesirable increases in process variation.
    • Have the rifle's sights gone out of adjustment? The sample average (x-bar) chart detects undesirable changes in the process mean.
    • Beware of tampering or over adjustment, i.e. trying to 'correct' the process for random variation. With the help of the control chart, you will know when to adjust the process or leave it alone.
  • Understand the concept of the control chart as a statistical hypothesis test.
    • The null hypothesis is that the process mean and variation are where they belong, and the Type I (false alarm) risk is the chance of concluding that the process has shifted when it has not. You may assume that the mean and variation are where they belong if there are no points outside the control limits (mandatory test), or failures of optional tests (Western Electric zone tests).
    • The alternate hypothesis talks about the process mean or variation has changed by a given amount, and the Type II risk is the chance of not detecting that process shift.
  • Understand the implications of the rational subgroup; a sample that reflects all sources of variation in the process. Batch processes and auto-correlated processes do NOT follow the textbook assumptions behind standard control charts.
  • Know the implications of processes that do not follow a normal distribution. Traditional control charts will produce misleading results for these applications, but off the shelf software (e.g. Minitab or StatGraphics) is available for handling them.

Who Should Attend

Manufacturing and quality professionals

About Our Speaker(s)

William A. Levinson, Manufacturing Guidelines ExpertWilliam A. Levinson P.E.
William A. Levinson, P.E., is the principal of Levinson Productivity Systems, P.C. He is an ASQ Fellow, Certified Quality Engineer, Quality Auditor, Quality Manager, Reliability Engineer, and Six Sigma Black Belt. He is also the author of several books on quality, productivity, and management, of which the most recent ... More info

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    Event Title: Understand Statistical Process Control and its Pitfalls
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