Cutting unplanned downtime by 35% with predictive maintenance
A multi-plant automotive parts manufacturer was losing an average of 60 production hours a month to unplanned equipment failure. ENVI4CAST deployed an IoT sensor network and predictive maintenance models across 14 production lines.
The Challenge
Maintenance was purely reactive, with failures discovered only after production stopped. Lack of equipment telemetry made root-cause analysis slow and inconsistent across plants.
The Solution
We instrumented critical machinery with vibration and thermal sensors, built a real-time ingestion pipeline, and trained failure-prediction models on two years of historical maintenance logs. Maintenance teams receive prioritized alerts 48–72 hours before likely failure.
35%
Reduction in unplanned downtime
21%
Lower maintenance cost per unit
14
Production lines instrumented