Case study

Predictive maintenance focused on the highest-cost equipment

Heavy industrial · Demonstrates business relevance

Context

A multi-site industrial operator was running thousands of pieces of rotating equipment across several plants. Unplanned downtime was costing roughly seven figures per quarter in lost throughput. Sensor data was being collected and logged, but no one had a defensible business case for which AI investment to make first. Several vendors had pitched general predictive-maintenance models — useful in principle, but none with a clear path to ROI.

Approach

The work began with a cost-based ranking of failures rather than the conventional frequency-based ranking. The 20% of assets producing 80% of downtime cost became the focus. Lower-impact ideas — cosmetic anomaly detection, peripheral conveyor optimization — were explicitly deprioritized.

Models were trained on historic sensor and maintenance-log data for the high-cost assets, with failure-window predictions tied directly to maintenance scheduling decisions. A small operations team was set up to act on alerts within a defined response window, so the predictions had a route into action from day one.

Outcome

  • Roughly 30% reduction in unplanned downtime on the targeted equipment in the first year of operation.
  • Maintenance ROI tracked per asset, so further rollout decisions could be made on evidence rather than vendor pitches.
  • The team was able to sequence the next phases of AI work — vibration-spectrum analysis, fleet-level optimization — against verified return rather than speculative scope.

Principle illustrated

Business relevance. The decision wasn't whether to use AI; it was where AI investment had the largest defensible return. Picking the right starting point made the project's value visible inside the first year and protected the wider AI roadmap from drift.

Used with permission from the client and shared here for illustrative purposes. Specific commercial details remain confidential and are available on request to qualified counterparties under NDA.

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