Shell operates one of the world's largest energy infrastructure networks, including refineries, chemical plants, offshore platforms, and pipelines across six continents. Unplanned equipment failures at these facilities created significant safety risks, production losses, and environmental hazards. Traditional maintenance strategies relied on fixed schedules or reactive repairs after failures occurred, resulting in excessive downtime and maintenance costs while still missing critical failure precursors that developed between scheduled inspections.
Shell deployed an AI-powered predictive maintenance platform that monitored over 10,000 pieces of critical equipment using sensor data, operational parameters, and historical maintenance records. Machine learning models identified subtle patterns indicating impending failures weeks or months in advance, enabling maintenance teams to schedule interventions proactively during planned downtime windows. The system prioritized maintenance activities based on failure probability, criticality, and resource availability, optimizing maintenance spend while minimizing unplanned outages.
“AI predictive maintenance has transformed how we manage our assets. We have moved from reacting to failures to preventing them, improving both safety and economics.”— Wael Sawan, CEO, Shell
This case study is based on publicly available information about Shell.
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