Predictive Maintenance Saves Millions
The Challenge
A global manufacturing firm was facing millions in losses due to unplanned downtime. Critical machinery would fail unexpectedly, halting production lines and delaying shipments.
The Solution
We deployed an end-to-end IoT and ML solution:
- Sensor Integration: Connected legacy machines to the cloud.
- Anomaly Detection: Trained ML models on historical vibration and temperature data.
- Alerting System: Integrated with the maintenance team's mobile devices.
The Results
- 85% Accuracy in predicting failure 48 hours in advance.
- $2.5M saved in avoided downtime in the first year.
- Extended equipment lifespan by optimizing maintenance schedules.