31/08/2026
🤖 A beverage producer avoided losing 288,000 bottles. The tip-off came from its pumps.
That's the case for AI condition monitoring in hygienic food production. Unplanned downtime, like a pump failing mid-run, doesn't just stop the line. It wastes raw material, adds cleaning, burns extra water and chemicals, and puts product quality and safety at risk.
The economics are blunt. In a plant with around 50 pumps, a single unplanned stop per quarter can run up to €80,000 a year in downtime, losses and cleaning. Alfa Laval puts the cost of a monitoring system like Clariot at less than a tenth of that.
đź”’ How it works: sensors sit on pumps and other rotating equipment, even in wet, washdown environments, and send data over a standalone 4G gateway to an analytics platform. AI flags anomalies early and issues warnings with diagnostics, so maintenance gets planned before the breakdown. Because the sensors never touch the company's IT network, the cyber attack surface stays small.
"AI-based condition monitoring reliably predicts failures and enables effective predictive maintenance," says Torsten Pedersen, Head of Connectivity & Monitoring at Alfa Laval. The payoff he cites: higher plant availability and more sustainable use of energy, water and materials.
Predictive or reactive: how much unplanned downtime is your line still absorbing each quarter?
👉 Read the full interview in the Anuga FoodTec Magazine: https://bit.ly/4wkss1P
Research contributed by DLG e.V.
📍Anuga FoodTec 2027 · Cologne · 23–26 February