Predictive Maintenance at Scale: Lessons from 12 Manufacturing Deployments
After deploying predictive maintenance systems across twelve manufacturing sites — ranging from automotive to pharmaceuticals — we've learned what works, what fails, and why the first 90 days of a deployment determine whether it survives long-term.
Norvik Research & Practice Team
Predictive maintenance is the most commercially mature application of AI in manufacturing. The ROI case is unambiguous — unplanned downtime costs between £10,000 and £500,000 per hour depending on the production line. A model that catches 60% of failures 48 hours in advance pays for itself in the first prevented incident.
What We've Learned
Across twelve deployments, the pattern is consistent: the technical problem is rarely the hard part. Sensor data collection, model training, and alert generation are solved problems. The hard parts are organisational: getting maintenance teams to trust model alerts, integrating predictions into existing work order systems, and building the feedback loop that lets the model improve over time.
- Start with a single asset class — don't try to predict failures across all equipment types at once
- Build the maintenance team's trust before removing human judgment from the loop
- Alert fatigue kills adoption — calibrate precision over recall until false positive rate is below 10%
- Integrate with your existing CMMS from day one, not as a retrofit
The deployments that achieved the highest sustained ROI were those where maintenance team leads were involved in defining the alert logic — not just the data science team.
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