Companies must assure that their processes are capable of producing products and services that consistently meet customer specifications.
This webinar discusses methods for estimating process capability for both normal and non-normal data. Pre-requisites for estimating process capability (e.g. establishing process stability) are discussed first. Distributions are briefly described and methods for estimating ppm levels are presented. The use and limitations of common process capability indices (e.g. Cpk and Ppk) are discussed.
It is vital that appropriate methods are used for estimating capability when the data is not well described by a normal distribution. Failure to do so often results in overly optimistic process capability estimates. Methods for testing for normality are discussed. Both transformations and distribution fitting are presented as methods to assess capability for non-normal data. The webinar includes several examples to illustrate the methods.
Why Should You Attend:
Considerable misunderstanding exists related to methods for assessing process stability and capability. As a result many companies incur excessive risks of customer dissatisfaction, warranty, recalls, and litigation.
This webinar covers proper methods (and prerequisites) for estimating process capability. Additionally, the shortcomings of popular process capability indices are exposed.
Following the webinar, participants will be able to quickly adopt the methods presented to improve their quality management system and the use of supporting statistical methods.
Areas Covered in the Webinar:
Who Will Benefit:
The target audience includes personnel involved in product/process development and manufacturing
Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.
Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty.
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