Predictive Maintenance Reduces Downtime by 75%
The Challenge
A regional manufacturer was experiencing significant production losses due to unexpected equipment failures. Unplanned downtime was costing them $180K per incident on average, and they were losing competitive bids due to unreliable delivery schedules. They needed a way to predict and prevent equipment failures before they happened.
Our Approach
We deployed IoT sensors across critical equipment and built an AI system that analyzes vibration, temperature, and operational patterns to predict failures 2-4 weeks in advance. The system integrates with their maintenance scheduling and parts inventory to automate preventive maintenance.
The Outcome
Unplanned downtime decreased by 75%. Annual savings exceeded $2.1M from prevented failures and optimized maintenance schedules. On-time delivery improved to 98%, helping win new contracts. The predictive system now covers 85% of critical equipment.