Consider an AI tool that delivers consistently accurate results and demonstrates impressive capabilities.

Walk any well-run plant and you’ll find statistical process control doing quiet, faithful work: control charts on the critical dimensions; capability studies before a line change; gage R&R before anyone trusts a measurement; out-of-control signals investigated; r

Manufacturing has a people problem, but not in the way we usually talk about it.

The real test of a quality system isn’t what happens when everything works. It’s what happens when something doesn’t.

Poor quality rarely announces itself as a single, contained problem. In manufacturing environments, it may first appear as a rejected batch, a scrapped component, an unexpected line stoppage, or a customer complaint that requires investigation.

Recent cyberattacks highlight the growing threat to operational technology (OT) u

Quality management system software helps life sciences organizations manage documents, training, quality events, change controls, audits, and other regulated processes in one controlled environment.

In my last post, I reflected on a couple of different whys to interrogate a process—either to solve a problem or to create a process improvement.

A sound that’s slightly off before a bearing fails. A smell that shows up before a chemical reaction runs hot. A texture that feels wrong before any instrument confirms it.

At a typical manufacturer, the cost of poor quality runs to 15–20% of sales.