SmartFactory

A major Moroccan industrial group operating multiple factories faced frequent and unpredictable breakdowns on its production lines, causing substantial financial losses and chronic delivery delays. Despite installing hundreds of IoT sensors on critical equipment, the collected data remained largely untapped, stored in technical silos without analysis or correlation. Maintenance teams operated in reactive mode, intervening only after failures occurred. The client needed an intelligent platform capable of centralizing IoT data streams, detecting anomalies upstream through artificial intelligence, and planning maintenance predictively to maximize output and minimize production interruptions.

We architected a scalable IoT platform capable of ingesting and processing real-time data from over 500 connected sensors (temperature, vibration, pressure, energy consumption). Machine learning algorithms trained on historical breakdown data detect precursor patterns of anomalies and trigger predictive alerts before failures occur. Real-time dashboards provide a synthetic view of each machine's health status with intuitive visual indicators. A predictive maintenance module automatically generates planned work orders, optimizing technician allocation. The advanced reporting system correlates production data with energy consumption to identify savings opportunities. The distributed architecture ensures minimal latency and high availability.