Predictive Maintenance at an Auto Components Manufacturer

This reflects the type of challenge our consultants are built to solve, drawn from real industry experience. Three CNC machining lines were averaging 14.2 hours of unplanned downtime per month per line. The plant manager had already increased PM frequency. It hadn't helped. Two of the three Q2 breakdowns had occurred within a week of a scheduled maintenance check, which was the number that finally made him question whether the problem was prediction rather than frequency. At ₹1.6 Lakh per hour of downtime, the three lines were costing roughly ₹68 Lakh per year in unplanned stops.

Vibration and temperature sensors were deployed on 11 critical wear points across the three lines. Eighteen months of maintenance logs were manually digitised and mapped to sensor readings. A gradient boosting model was trained on 15 months of data, validated on the remaining 3, and deployed to generate 48-72 hour advance alerts. The false positive rate was 22%, one in five alerts was a non-event. That's acceptable when the cost of ignoring a real alert is ₹1.6 Lakh. Maintenance staff were trained to treat amber alerts as a trigger for a targeted inspection rather than a full shutdown.