Manufacturing
Predictive Maintenance System
Automotive Parts Manufacturer
-45%
Downtime Reduction
Fewer unplanned outages
+60%
Maintenance Efficiency
Better resource allocation
$4M
Cost Savings
Annual savings from reduced downtime
92%
Prediction Accuracy
Failure prediction accuracy
The Challenge
Unplanned equipment downtime was costing millions in lost production. Reactive maintenance was inefficient, and scheduled maintenance was often unnecessary or missed critical issues.
Our Solution
We deployed IoT sensors across 200+ machines and built ML models that analyze sensor data to predict equipment failures 2-3 weeks in advance. The system provides maintenance teams with prioritized alerts and recommended actions.
Technology Stack
PythonPyTorchInfluxDBGrafanaMQTTEdge Computing
Project Timeline
6 months including sensor deployment
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