How SABIC achieved 25-35% downtime reduction and 30% repair cost savings through IoT-powered predictive maintenance and digital twin technology.
Unexpected equipment failures caused costly unplanned downtime, resulting in billions of dollars in lost production annually.
Reactive maintenance strategies led to excessive repair costs and inefficient resource allocation across facilities.
Equipment failures posed significant safety risks to personnel and environmental compliance challenges.
Deployed comprehensive IoT sensor networks across industrial assets to capture real-time operational data including vibration, temperature, pressure, and performance metrics.
Created digital twin replicas of critical assets to simulate performance, predict failures, and optimize maintenance schedules before physical intervention.
Implemented machine learning models to analyze sensor data patterns, predict equipment failures 2-4 weeks in advance, and recommend optimal maintenance actions.
Built centralized platform integrating all asset data, maintenance workflows, and predictive insights with mobile access for field technicians.
2-4 weeks advance warning of equipment failures
Accurate estimation of asset lifespan and replacement timing
Real-time identification of abnormal operating conditions
Optimal scheduling of preventive maintenance activities