From reactive to predictive
Reactive maintenance fixes machines after failure. Preventive maintenance services on a calendar whether needed or not. Predictive maintenance uses vibration, temperature, current, cycle-time and quality data to predict failure and schedule work at the least disruptive time.
What data do you need?
You do not need a factory full of sensors to start. Existing PLC data, machine run-hours, rejection trends and operator logs are often enough for a first model on one critical asset.
- Machine run-hours and stoppage logs
- Vibration/temperature/current readings where available
- Quality rejection rates by machine and shift
- Maintenance history and spare-part lead times
A realistic pilot
Pick one critical machine, instrument it if needed, capture 4–8 weeks of baseline data, then build simple threshold and trend alerts before moving to ML models. Alert quality matters more than model sophistication.
The business case
A single avoided shift-level stoppage often pays for the pilot. Track avoided downtime hours, maintenance cost per asset and overtime reduction to quantify ROI.