A Novel Approach to Calculating Process Capability for Non-Normal Data 2020-11-10T23:51:57+00:00

Webinar: A Novel Approach to Calculating Process Capability for Non-Normal Data

Process capability indices have been used for decades to quantify the amount of material not meeting specification. These calculations rely upon several assumptions, one of which is normality of the data. The more the normality assumption is violated accuracy of how much material exceeds specification becomes grossly distorted.

Historically statisticians have provided a variety of techniques to assist. Most of these require transformation of the data (Box-Cox, Johnson, distributional fits, etc.). However, applying transformations blindly without understanding the behavior of the data can lead to equally poor estimates. Unless one is a statistician, applying transformations and making any sense of it is at best confusing and still often not accurate. The percentile method is subject to sensitivity to sample size.

A different approach that is robust to all common problems (except a non-stable process) will be introduced. These common problems include outliers, non-normal data, multiple distributions, 0’s and negative values, threshold values, limitations to sample size, and more. It will allow the practitioner to more accurately estimate the defect levels than other approaches. No statistical software is required; all of this can be done using software such as Excel®️.

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Kevin Keller
Kevin Keller Principal - Keller Statistical Consulting

About Kevin Keller

Kevin Keller has over 35 years of industrial experience.  He spent the first 20 years in the electronic materials world making high tech circuit boards for military equipment as well as silicon wafers for the semiconductor industry.  Kevin retired a few years ago from Anheuser-Busch InBev, where he served as a Master Black Belt.  Kevin was responsible for Quality Systems in the manufacture of cans and lids for the beverage market.  He now serves as an independent consultant for both global and local companies in Lean Six Sigma, Quality, and Quality Management.  He has a BS degree in Chemical Engineering from the University of Missouri-Rolla and a Masters in Applied Statistics from The Ohio State University.  He and his wife, Ann, reside in St. Peters, MO.

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