Why predictive maintenance is a production stability tool
In manufacturing, downtime is visible, but the larger cost is often hidden: scrap, rework, late shipments, and quality drift. Predictive maintenance is most valuable when it reduces unplanned stoppages and stabilises process quality by detecting degradation early.
Typical target assets
- CNC machines, presses, and rotating equipment
- pumps, compressors, and gearboxes
- tooling wear and fixture degradation
- critical stations that constrain throughput
- process sensors that reveal drift before defects occur
Data foundations
- PLC and SCADA telemetry
- vibration, temperature, pressure, current draw
- historian data aligned to shift and batch context
- maintenance work orders and parts usage
- quality records (defect rates, rework triggers)
A key requirement is aligning operating context (load, product mix, shift, batch) with condition signals to reduce false positives.
Modelling approaches
1) Condition-based anomaly detection
Detects early deviations that often precede failure.
2) Failure mode models
When historical data supports it, models can predict failure likelihood within defined horizons.
3) Quality-linked degradation signals
Some degradation shows up first as quality drift. Linking maintenance signals to quality outcomes improves prioritisation.
Deployment and operational adoption
Manufacturing teams adopt predictive maintenance when:
- alerts are low-noise and explainable
- recommended actions are specific and realistic
- the system integrates into existing maintenance planning
- drift is monitored as processes and equipment change
Metrics
- reduction in unplanned downtime
- improved OEE stability (not only peak OEE)
- reduction in scrap and rework attributable to equipment issues
- improved mean time between failures
- reduction in emergency corrective maintenance