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?

LNS Research published its research, “Driving Operational Performance With Digital Innovation: Connecting Risk, Qu

I am a quality manager by profession. Thus, I think about quality a lot. How would one define “quality?” A simple view of quality is “conformance to requirements.” This simplistic view of quality lacks the complexity that it should have.

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.

The fourth industrial revolution is upon us.

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.

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.

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.

Science, technology, engineering and math (STEM) career outreach programs play a pivotal role in shaping the capabilities and makeup of the future workforce.

Team leaders often focus on product details. Founders obsess over fonts. Sales managers fixate on tough-to-wrangle customers and shop owners on the minutia of shelf displays.