Predictive Maintenance That Prevented a ₹2Cr Failure

NALCO's plant floor generates more sensor data than any operations team can watch manually — vibration, temperature, load, and wear signals streaming continuously off equipment that can't afford unplanned downtime. Traditional maintenance schedules catch failures after the fact, or replace parts that didn't need replacing yet.

The problem

Reactive maintenance means equipment fails, then gets fixed. Scheduled maintenance means parts get replaced on a calendar instead of on actual condition. Neither approach uses the sensor data already being generated on the floor to see a failure coming before it happens.

The build

We deployed a predictive-maintenance model trained on NALCO's own equipment sensor data, flagging abnormal wear patterns and load conditions days before they'd trip a conventional threshold alarm. Instead of reacting to a failure or maintaining on a fixed schedule, the operations team gets an early warning tied to the specific machine and the specific risk.

The outcome

"Technocracy's predictive maintenance AI prevented a ₹2Cr equipment failure in the first month alone. The ROI was evident immediately. Their team's technical depth and domain understanding are outstanding." — Vijay Prakash, Head of Operations, NALCO

One flagged failure, caught days ahead of the fact, more than covered the cost of the rollout — in the first month.