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AI USE CASE

Predictive maintenance for equipment

Anticipate equipment failures days or weeks before they happen.

Typical budget
€40K–€150K
Time to value
16 weeks
Effort
10–24 weeks
Monthly ongoing
€1K–€5K
Minimum data maturity
advanced
Technical prerequisite
data engineer
Industries
Manufacturing, Logistics
AI type
ml regression

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.

Vendors to consider

This use case is part of a larger Data & AI catalog built from 50+ enterprise transformation programs. Take the free diagnostic to see how it ranks against your specific context.