Maintenance is no longer reactive: it's predictive

For decades, industrial maintenance worked according to a simple model: the machine breaks, you fix it. Then came preventive maintenance, based on calendar schedules. Today, thanks to the integration of MES, IIoT and machine learning algorithms, it is possible to predict a failure before it happens.

How predictive maintenance works

Vibration, temperature, current draw and pressure sensors continuously monitor every production asset. The data is sent to the MES via OPC-UA or MQTT, where machine learning algorithms — trained on each machine's historical behavior — recognize anomalous patterns that precede a failure by hours, days or weeks.

The real numbers

Documented implementations show a reduction in unplanned downtime of between 35% and 50%. The cost of maintenance interventions drops by 25-30%: action is taken on the exact machine, at the right time, with the spare part already available.

A Key4 customer in the food sector reduced unplanned downtime by 67% in the first year of using the AI-Powered MES, with estimated annual savings of over €180,000.

Key4's MES as a predictive hub

Key4's MES natively integrates the Maintenance module, which aggregates IIoT data, manages preventive and predictive maintenance plans, and automatically generates work orders with the correct priority. Operators receive the alert on their tablet on the shop floor before the problem becomes critical.