The Latest From Laminar

Featured in Processing Magazine, this piece explores how AI and real-time sensors are transforming Clean-in-Place from fixed-timer cycles into self-driving systems that cut waste and eliminate contamination risk.

Boston Climate Week 2026 highlighted the growing need for climate innovation inside F&B manufacturing. Food and beverage manufacturers are using process aware AI and ML models reduce water, energy, and chemical waste while improving operational efficiency and uptime.

Global manufacturing leaders gathered at Laminar to discuss how AI, machine learning, and real-time sensing are reshaping industrial operations and accelerating the shift toward autonomous manufacturing.

New flavors and fragrances are breaking the flavor wheel — forcing more full Clean-In-Place cycles, missed delivery windows, and lost capacity that F&F manufacturers can't afford.

Machine learning isn't replacing quality assurance in food & beverage manufacturing — it's giving QA teams the real-time process intelligence to go from reactive to predictive.

Most flavors and fragrances facilities clean longer than they need to — and they know it. See the real cost of CIP waste.

More SKUs mean more cleaning time and fewer production hours — a tension every F&B co-manufacturer experiences. Here's the operational pressure that comes with more cleaning hours.

With shifting consumer demand, food and beverage plants now run hundreds of SKUs per line. Rapid changeovers have become the bottleneck—driving product loss, longer cleaning cycles, and lost capacity.

Shift in consumer demand for fresh, minimally processed food is reshaping how F&B manufacturers operate — ingredients going into clean label must be provable down to the lot number.