One of the highlights on our calendar each year is the first Friday in October, which is Manufacturing Day here in the United States. This event offers us the perfect opportunity to celebrate the centrality of manufacturing as a driver of the economy, innovation, automation, education, and lots more.

Data science and machine learning have surged in prominence during the past few years, and digital transformation seems to be on everyone’s agenda. Have you ever wondered why? Even though quality engineering has long been a data-driven pursuit, we now have the potential to get even deeper insights from our data because of several recent innovations.

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. 

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.

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.