Deployed predictive maintenance platform reducing unplanned downtime by 78% across 2000+ devices
78% reduction
Unplanned Downtime
Predicted failures before they occurred
$18M annual savings
Warranty Costs
Reduced warranty claims through preventive maintenance
2000+
Device Fleet
Successfully monitored and managed across 40 countries
94%
Prediction Accuracy
Accurately predicted failures 72 hours in advance
A manufacturer of industrial equipment faced escalating warranty costs due to unexpected failures. They wanted to predict failures before they happened and move to predictive maintenance. Their equipment was deployed globally, making centralized monitoring and remote updates critical.
We built an end-to-end IoT platform: - Firmware for real-time equipment monitoring with edge analytics - Time-series prediction models identifying failure patterns - Automated alerting and maintenance scheduling - Over-the-air update system for global device fleet - Customer dashboard for maintenance insights
Industrial equipment failure was expensive—both in warranty replacement costs and customer relationship damage. The company wanted to shift from reactive repairs to predictive maintenance.
We built a platform combining edge computing, ML, and fleet management:
**Edge Layer**: Lightweight firmware monitoring vibration, temperature, and electrical characteristics **ML Layer**: Predictive models identifying failure patterns before they occur **Management Layer**: Fleet management, OTA updates, and maintenance scheduling **Analytics Layer**: Customer dashboard for insights and maintenance planning
Deployed across 2000+ devices globally, reducing unplanned downtime by 78% and saving $18M annually in warranty costs. Prediction accuracy of 94% with 72-hour advance notice gave customers time to schedule preventive maintenance.
Heavy Equipment Manufacturer
Industrial IoT
Unplanned Downtime
Warranty Costs
Device Fleet
Prediction Accuracy