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

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.

In 2021, container ships idled for weeks outside the Port of Los Angeles, a stark visual reminder of just how fragile modern supply-chain reliability had become. The backlog sent shockwaves across industries.

The quality systems most medtech teams are stuck with aren’t built for how they work today. 21 CFR Part 820 was authorized by the Federal Food, Drug, and Cosmetic Act of 1978, long before the software industry even existed.

What can we learn about human intelligence by studying how machines “think?” Can we better understand ourselves if we better understand the artificial intelligence systems that are becoming a more significant part of our everyday lives?

Implementing a new quality management system (QMS) is no small task, especially for life science companies faced with stringent regulatory requirements and a high validation burden.

The arrival of artificial intelligence (AI) in quality management has been met with a mixture of hype and skepticism. Is it just a faster anomaly detector, or is it truly transformative?

When organizations implement an enterprise quality management system (EQMS), the instinct is often to begin with high-visibility processes like corrective and preventive action (CAPA) or supplier quality.