Predictive Maintenance AI for an Automotive Parts Manufacturer
An IoT and machine-learning program helped the manufacturer predict equipment failure before breakdowns occurred.
Challenge
Unplanned equipment downtime was costing the manufacturer heavily in lost production, emergency maintenance, and wasted materials. Existing maintenance scheduling was calendar-based and not responsive to real asset condition.
Approach
EvoTech deployed IoT sensors across critical equipment and built a time-series ML pipeline that scored equipment health and predicted likely failures in advance. Work orders were integrated into the maintenance process.
Results
- Unplanned downtime reduced by 64 percent.
- Maintenance costs reduced by 31 percent.
- First-year savings reached $7.4M on a $1.2M implementation cost.
- The model was rolled out across all four plants.
Solution structure
Predictive maintenance AI implementation
Predictive Models
Developed predictive maintenance models using equipment sensor data and historical failure patterns.
Real-time Monitoring
Built real-time monitoring dashboard with alerting and maintenance scheduling.
CMMS Integration
Integrated with CMMS and created automated work order generation.
Outcome metrics
Measurable business impact
Building predictive maintenance AI for your manufacturing operations?
EvoTech can develop AI-powered predictive maintenance systems that reduce downtime and optimize maintenance resources.