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AI USE CASE
Predictive maintenance for equipment
Anticipate equipment failures days or weeks before they happen.
What it is
Sensor data plus historical maintenance logs feed an ML model that predicts time-to-failure for critical equipment. Maintenance switches from reactive to scheduled, cutting downtime and emergency callouts.
Data you need
IoT sensor streams (temperature, vibration, pressure) and 12+ months of maintenance logs.
Required systems
- erp
- data warehouse
Why it works
- Pilot on one critical asset class first
- Pair every alert with an inspection checklist
How this goes wrong
- Sensor data quality issues that no one investigates
- Maintenance team treats the model as a black box
When NOT to do this
Skip if you don't have IoT sensors or a budget for them, this is a multi-year programme.
Example vendors
This page describes an idea. It gives no budget or timeline: those depend on your company. The free diagnostic tells you which ideas fit your situation.