IoT-Powered Predictive Maintenance

The challenge

The client operated 12 manufacturing facilities across North America with aging equipment that failed unpredictably. Unplanned downtime cost an average of $270K per incident, and maintenance was entirely reactive—technicians only learned of failures after production stopped.

The solution

We deployed AWS IoT Core across all facilities, connecting 4,000+ sensors on critical equipment. Edge processing via AWS Greengrass enabled real-time anomaly detection. An ML model trained on 18 months of historical sensor data predicted failures 72 hours in advance with 89% accuracy. Maintenance teams received mobile alerts with prioritized work orders.

Outcomes

Unplanned downtime dropped 62% in the first year. The predictive maintenance program saves $3.2M annually in avoided downtime costs and reactive repairs. All 12 facilities are now fully connected, and the client is expanding the program to European operations.