D uring the past decade, manufacturers have wired their plants with sensors, robots, and software. Yet many “AI-driven” systems still miss the mark.

In this article I want to explore an observation on how we make distinctions and what this reveals about the structure of our thinking.

We’d be willing to bet your key collaborators aren’t all in the same building. Your team members, contract partners, clients, and suppliers are likely scattered across the globe.

The use of artificial intelligence (AI) models, specifically generative AI, is growing. This has raised concerns about the effects on jobs in various professions. The quality profession is among them.

(Shimadzu Scientific Instruments Inc.: Columbia, MD) -- Shimadzu Scientific Instruments announces its new ALTRACE energy dispersive X-ray fluorescence spectrometer, designed with the latest X-ray optical system and proprie

Growing up in Pittsfield, Massachusetts, I witnessed firsthand the heartbeat of American manufacturing. I remember the hum of machines, the rhythm of assembly lines, and the pride of workers crafting products that powered industries across the nation.

(Hexagon Manufacturing Intelligence: Cobham, England) -- Hexagon’s Manufacturing Intelligence division introduces

I grew up outside Pittsburgh, widely known as “Steel City.” Although the city is no longer the center of steel or heavy manufacturing in America, its past remains a proud part of its identity.

Ninety days to implementation vs. 12 to 18 months with traditional systems: That’s not just an incremental improvement—it’s a complete reimagining of what’s possible in life sciences quality management.

When businesses talk about customer experience, the conversation almost always focuses on the end user. That’s understandable, but dangerously narrow.