IoT Analytics for Predictive Maintenance
Reducing unplanned downtime through real-time sensor data analytics
The Challenge
A metal fabrication facility with 200+ CNC machines was experiencing costly unplanned equipment failures. Reactive maintenance led to 12-15 unexpected breakdowns monthly, each resulting in 4-8 hours of lost production. With each hour of downtime costing $18,000, the financial impact was severe. The maintenance team needed a way to predict failures before they happened, but sensor data from machines was siloed and not being analyzed effectively.
Our Solution
IoT Data Integration
Connected 200+ machines via IoT sensors tracking vibration, temperature, power consumption, and cycle time
Real-Time Analytics
Built dashboards showing machine health scores, anomaly detection, and failure probability predictions
Predictive Alerts
Implemented ML models to predict equipment failures 48-72 hours in advance, enabling proactive maintenance
Maintenance Scheduling
Integrated alerts with CMMS system to automatically generate work orders for maintenance team
Results
- Unplanned downtime reduced by 67% (from 12-15 incidents to 4-5 monthly)
- $2.4M annual savings from avoided downtime costs
- 89% accuracy rate in predicting equipment failures 48+ hours in advance
- Maintenance costs reduced 22% by scheduling work proactively during planned downtime
- Overall Equipment Effectiveness (OEE) increased from 71% to 88%
Technologies Used
"The predictive analytics have revolutionized our maintenance operations. We're now fixing issues before they become problems, saving millions in downtime costs."
— VP of Operations, Metal Fabrication Company
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