Unplanned downtime is one of the largest hidden costs in manufacturing. Predictive maintenance replaces "fix it when it breaks" and "replace it on a fixed schedule whether it needs it or not" with "fix it just before it fails" — and modern software finally makes that practical for mid-sized plants, not just industrial giants.
Condition-based first, machine learning later
You do not need a neural network to start. Simple condition-based rules — temperature, vibration, and current thresholds — catch a surprising share of failures. Ship those first. They build trust in the data and give you the labelled failure history you will need before any ML model can earn its place.
What good predictive maintenance software does
- Ingests sensor and maintenance-log data into one asset history.
- Flags anomalies early and ranks them by likely cost of failure.
- Generates work orders automatically and routes them to the right technician.
- Tracks whether its predictions were right, so the model keeps improving.
The goal is not to predict every failure — it is to convert the expensive surprises into scheduled, cheap interventions.
Measure it in avoided hours
Judge the system on downtime avoided and emergency call-outs eliminated, not on model accuracy in isolation. A model that is only decent but wired into your work-order flow beats a brilliant model whose alerts land in an inbox and die there.
