Process Capability: When It's Not a Bell Curve

Duration: 60 Minutes
Organizations must often perform process capability studies for customers and/or their own use. The information from the studies, such as the estimated process standard deviation, is then used to set control limits for statistical process control (SPC) charts. The textbook assumption is, however, that the process data follow the normal or bell curve distribution, which is far more common in textbooks than in the real world. This webinar will show how to handle situations in which the distribution is not a bell curve.
Process Capability
Product ID: 505754
Objectives of the Presentation
  • Know how variation in critical to quality characteristics affects outgoing quality in terms of the nonconforming fraction
  • Know the difference between the process capability index (based on short-term variation) and process performance index (based on long-term variation)
  • Know the definition of the rational subgroup as the sample that is subject to all variation sources (short and long term) from the process. When it is selected correctly, the process capability index and process performance index will be roughly identical
    • Batch processes, however, include between-batch as well as within-batch variation. It is important to account for the between-batch variation
    • Know the effect of non-normality on process performance indices, and also on SPC charts
    • Dysfunctional control charts show that (1) the rational subgroup has not been selected properly or (2) the distribution is nonnormal
  • Know off-the-shelf methods for calculating accurate process performance indices, and setting up usable control charts, for non-normal distributions
Why Should you Attend
Traditional process capability analysis assumes that the process data follow a bell curve (normal) distribution, but this distribution is far more common in textbooks than it is in the real world. When the distribution is non-normal, the traditional process performance index estimate can be off by several orders of magnitude in terms of the nonconforming fraction (defects per million opportunities). A purported Six Sigma process might not, in fact, even be capable. Off the shelf methods are however available for calculating accurate process performance indices and setting appropriate control limits for accompanying SPC charts.

Areas Covered
  • The process capability index and process performance index are essentially ratios of the specification width to the amount of variation in the process. Larger is better because it means there is more distance between the nominal and the specification width
  • The process capability index reflects short-term variation, and the process performance index reflects all variation sources. If the rational subgroup has been selected correctly, they will be identical. If not, the process capability index will be larger than the process performance index
  • Batch processes often have batch-to-batch (long term variation source) as well as within-batch (short term variation source) variation
  • When the process distribution is non-normal, the traditional process capability and performance indices will be inaccurate, while traditional SPC charts will have out of control signals even when the process is in control. If however the process data are fitted to the correct underlying distribution, accurate process performance indices and usable SPC charts can be constructed. This can be done with off the shelf software
  • The procedure for conducting a process capability study will be covered
Who will Benefit
The following titles across the manufacturing industry will benefit from this training:
  • Manufacturing
  • Production
  • Quality
  • Engineering
  • Product Management
  • Project Management
  • Technician
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  • Presentation handouts in downloadable PDF format will be updated on your OCP Account within 24 hours of the purchase of the product
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Instructor Profile:
William Levinson 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 holds degrees in chemistry and chemical engineering from Penn State and Cornell Universities, and night school degrees in business administration and applied statistics from Union College, and he has given presentations at the ASQ World Conference, ISO/Lean Six Sigma World Conference, and others.


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