In this all-manufacturing episode, we look at the STEM pipeline into manufacturing, supplier development, how to make sense of manufacturing data and, no, manufacturing is not dead.

Parts One and Two of this series illustrated four problems with using a model-building ap

During the early 1990s, I was president of the Twin Cities Deming Forum. I had a wonderful board whose members were full of great ideas. One member, Doug Augustine, was a 71-year-old retired Lutheran minister and our respected, self-appointed provocateur.

In the foreword of Mark Graban’s book, Measures of Success: React Less, Lead Better, Improve More (Constancy Inc., 2018), renowned statistician, Donald J.

I must admit, right up front, that this is not a totally unbiased review.

In “Data Snooping Part 1” (Quality Digest, Aug. 6, 2018) we discovered the basis for the first caveat of data snooping.

Data mining is the foundation for the current fad of “big data.” Today’s software makes it possible to look for all kinds of relationships among the variables contained in a database.

Quality and manufacturing practitioners are most familiar with the effect of variation on product quality, and this is still the focus of the quality management and Six Sigma bodies of knowledge.

The ultimate purpose for collecting data is to take action. In some cases the action taken will depend upon a description of what is at hand. In others the action taken will depend upon a prediction of what will be.

A few months back, I was reading a really good article from The Wall Street Journal, titled “Stop Using Excel, Finance Chiefs Tel