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.

Omnex began working in the automotive industry by assisting Ford powertrain suppliers in 1986. The U.S. automotive industry’s Big Three used GM’s Targets for Excellence, Ford’s Q 101, and Chrysler’s SQA standards to qualify its supply bases. The automotive industry was making deep reductions in its supply bases based on the results of these assessments and other factors. Though many on the Omnex team at that time came from General Motors, Ford’s Q 101 was the standard that really received our attention.

LNS Research published its research, “Driving Operational Performance With Digital Innovation: Connecting Risk, Quality, and Safety for Superior Results” to address fundamental challenges quality and safety leaders face today. 

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.

Historically, quality in a process was something that was done at the end of the line. You inspected your widget once it was made, and if it had flaws, you fixed it or threw it out.

As in many modern manufacturing environments, quality in software has become a process you do from start to finish. Moving from that end-of-line quality control mindset to a more user-focused, whole-team quality advocacy helps deliver a better product where quality is baked in from the start, and part of the job of all roles on a software team.

I recently sat down with Doug Fair, the chief operating officer of InfinityQS, for a discussion about the uses (and sometimes, the misuses) of industrial statistical analysis. Ours was a lengthy conversation, so we’re splitting the account into two parts. In this installment, we cover data gluttony and the power of sampling. In part 2, coming up in October, Fair and I will compare and contrast a pair of interesting case studies and consider what manufacturers are missing when they fail to consider the data obtained from in-spec processes.