A global manufacturer with 200+ production facilities needed to reduce equipment downtime and maintenance costs through predictive maintenance.
The Challenge
Equipment failures were unpredictable, causing $50k+ losses per hour of downtime. Maintenance was reactive, not proactive.
Our Solution
- •Deployed 10k+ IoT sensors across all facilities
- •Built real-time data pipeline collecting 100M+ sensor readings daily
- •Developed LSTM neural network predicting failures 5-14 days in advance
- •Implemented predictive maintenance scheduling system
- •Created mobile alert system for maintenance teams
Key Outcomes
- ✓35% reduction in equipment downtime ($8M annual savings)
- ✓92% accuracy in failure prediction
- ✓50% reduction in maintenance costs
- ✓20% increase in overall equipment effectiveness (OEE)
- ✓45-day ROI on IoT deployment
Technologies Used
Results
The predictive maintenance platform delivered 35% reduction in downtime, equivalent to $8M annual savings, with ROI achieved in just 45 days.
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