Change is inevitable in manufacturing. Controlling change effectively distinguishes industry leaders from quality-deficient, recall-plagued, and regulatory-troubled companies.

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.

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

AI has amazing capabilities, and it’s one of the best technologies for the future. It’s helping to change the world and bringing productivity enhancements across industries with its exceptional use cases. Quality assurance isn’t left out, either.

Many organizations have decided to automate their quality management system (QMS) or upgrade their currently automated QMS.

When an issue arises, it’s important to take quick action. Whether that means launching a software patch, pulling a batch, or halting the use of a reagent, it’s critical to tackle the immediate problem.

Many organizations list continuous improvement (CI) as a priority, but it often fails to take root in day-to-day work. It appears as a workshop or a one-off initiative, then fades without lasting change.

Capturing real-time data in all areas of the business is vital for maintaining long-term success. When manufacturers conduct business with separate systems, data end up in silos, and information can’t flow in an efficient manner.

A recent company meeting revealed what management called a “handoff problem.” The sales team would close deals, then toss them over the wall to the service team, which would promptly fumble the relationship because they didn’t understand what had been promised or why

In manufacturing, the term connected worker has quickly gone from emerging concept to executive mandate. As companies grapple with turnover, skill gaps, and increasing complexity, the urgency to modernize frontline work has never been clearer.