The conversation about generative AI (gen AI) is unavoidable in today’s business land

In this article I’m looking at a question that’s rarely asked in management: What if the most responsible course of action isn’t to maximize benefit, but to minimize harm? In decision theory, this is expressed as the minimax principle.

At the corner of quality and assembly, design engineers are frequently confronted with unexpected, complex fluid process issues in the prototyping phase.

Even the smallest manufacturer would never consider using a typewriter to develop an invoice, or manage a sales prospect list from a Rolodex.

Amidst uncertainty in manufacturing, AI adoption, labor market fluctuations, and salary disparities across industries and geographic regions, quality professional compensation can be difficult to navigate.

One of the principles for understanding data is that while some data contain signals, all data contain noise. Therefore, before you can detect the signals you’ll have to filter out the noise. This act of filtration is the essence of all data analysis techniques.

Staff meetings can be incredibly productive. Or unproductive—and more often the latter. If your staff meetings are terrible, it’s your fault because you’re not structuring them well.

Quality has always been a defining metric in manufacturing when it comes to industry trust, brand longevity, and customer loyalty. Manufacturers are already expected to abide by stringent regulations.

From manufacturing and mining to hospitality and healthcare, computerized maintenance management systems (CMMS) have become all but essential.

Physical AI—the embedding of digital intelligence into physical systems—is a promising but sometimes polarizing technology.