Predictive Maintenance

Predictive maintenance is a maintenance strategy that uses condition data and analytics to forecast when equipment is likely to fail, so that servicing is carried out just before a failure rather than on a fixed schedule or after a breakdown. Sensor data such as vibration, temperature, current or acoustic signals is gathered, often via IoT connectivity, and evaluated with statistical models or machine learning to estimate remaining useful life. By acting on the actual condition of an asset, predictive maintenance aims to cut unplanned downtime and avoid both premature and overdue interventions in DACH manufacturing.

How predictive maintenance works

Predictive maintenance rests on a continuous loop of sensing, analysing and acting. Sensors on machines record condition signals; these are transmitted, often through IoT gateways and the OPC UA standard, to an analytics layer. There, models compare current readings against normal patterns and known failure signatures to detect anomalies and estimate the remaining useful life of a component. When a prediction crosses a defined threshold, a maintenance order is created and scheduled at a convenient time, ideally before the failure occurs but late enough to use the component fully.

Distinguishing it from other strategies

• Reactive (run-to-failure): maintenance only after a breakdown, with maximum unplanned downtime.
• Preventive (time-based): maintenance on a fixed calendar or usage interval, regardless of actual condition.
• Condition-based: maintenance triggered when a measured parameter exceeds a limit, observing the present state.
• Predictive: maintenance triggered by a forecast of future failure derived from trends and models, looking ahead rather than only at the present.

Role of ERP and the digital twin

Predictive maintenance only delivers value when its predictions translate into action, which is where the ERP system and maintenance management come in. A predicted failure should generate a maintenance order, reserve spare parts, allocate labour and feed cost and downtime back into asset records. Integration with MES data improves the model by linking condition to actual load and output. A digital twin of the asset can refine predictions by simulating behaviour under observed conditions, and analytics platforms or a data warehouse hold the historical data the models learn from.

Practical considerations

Predictive maintenance is data-intensive and not universally justified. It pays off most on critical assets where unplanned downtime is expensive and failure modes are detectable through measurable signals. It requires sufficient historical and failure data to train reliable models, appropriate sensors, and a clear path from prediction to maintenance order. Organisations should pilot on a small set of critical machines, validate that predictions are accurate enough to act on, and avoid treating every asset as a candidate. The strategy should stay vendor-neutral about sensors, analytics tools and the surrounding ERP or maintenance system, and predictions should always be sanity-checked against engineering judgement.

Related Topics

• OPC UA
• AI in ERP
• Digital twin