The Challenge

Unplanned downtime causing production losses of over $10M annually.

The Solution

Deployed a custom Machine Learning model on Azure IoT Edge that ingests real-time sensor data (vibration, temperature, pressure) and uses a Random Forest classifier to predict component failure with 92% accuracy.

The Results

• 22% increase in Overall Equipment Effectiveness (OEE)
• 60% reduction in unplanned downtime
• $7M in annual cost savings from reduced downtime and optimized maintenance schedules

Technologies Used

Azure IoT Hub, Azure Machine Learning, Python, Scikit-learn, Grafana