Predictive maintenance focused on the highest-cost equipment

Context

A multi-site industrial operator had thousands of pieces of rotating equipment in several plants. The cost of unplanned downtime was approximately seven figures each quarter in lost throughput. The operator collected and recorded sensor data. But no one had a defensible business case for the first AI investment.

Several vendors proposed general predictive-maintenance models. These models were useful in principle, but none had a clear path to return on investment (ROI).

Approach

The project team started with a list of failures in order of cost. The usual procedure is a list in order of frequency. The focus was the 20% of assets that caused 80% of the downtime cost. The project team explicitly gave lower priority to ideas with less effect, such as cosmetic anomaly detection and peripheral conveyor optimisation.

The project team trained models on historic sensor data and maintenance-log data for the high-cost assets. The failure-window predictions connected directly to decisions about the maintenance schedule. The project included a small operations team, which acted on alerts in a defined response window. As a result, the predictions had a path to action from the first day.

Outcome

• Approximately 30% decrease in unplanned downtime on the target equipment in the first year of operation.
• The team recorded maintenance ROI for each asset. As a result, decisions about further rollout used evidence, not vendor proposals.
• The team used verified return, not speculative scope, to put the next phases of AI work in sequence. These phases include vibration-spectrum analysis and fleet-level optimisation.

Principle

Business relevance. The decision was not whether to use AI. The decision was where an AI investment had the largest defensible return. The correct start point made the value of the project visible in the first year. It also protected the wider AI roadmap from drift.

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