In my first article, the merits and cautions of AS9138 c=0 sampling plans were discussed and a simple formula was provided to determine the required sample size to detect nonconforming units.

In my previous article, I discussed the merits and cautions of the “acceptance number” equal zero (c=0) sampling plans contained within AS9138. A simple formula was provided to determine appropriate sample size, and it was illustrated that twice the inspection does not provide twice the consumer protection.

Aerospace standard AS9138—“Quality management systems statistical product acceptance requirements” was issued this year (2018), a few years after its accompanying guidance materials in section 3.7 of the International Aerospace Quality Group’s (IAQG) Supply Chain Management Handbook.

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.

“Strengthening the STEM Workforce Pipeline Through Outreach”

Parts one and two of this series illustrated four problems with using a model-building approach when data snooping. This column will present an alternative approach for data snooping that is of proven utility. This approach is completely empirical and works with all types of data.

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. He never missed an opportunity to appropriately pull us right back to reality with his bluntly truthful observations and guaranteed follow through on every commitment he made.

In the foreword of Mark Graban’s book, Measures of Success: React Less, Lead Better, Improve More (Constancy Inc., 2018), renowned statistician, Donald J. Wheeler, writes about Graban: “He has created a guide for using and understanding the data that surround us every day.

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. Other forms of variation are, however, equally important—in terms of their ability to cause what Prussian general Carl von Clausewitz called friction, a form of muda, or waste—in production control and also many service-related activities. This article shows how variation affects the latter as well as the former applications.

A few months back, I was reading a really good article from The Wall Street Journal, titled “Stop Using Excel, Finance Chiefs Tell Staffs.” Even though it was geared toward accounting and corporate operations, the message of the article struck home: Excel shouldn’t be used as an enterprisewide financial platform. That’s as true for the manufacturing quality world as it is for finance.