SABIC Jubail AI Predictive Maintenance
500+ Assets Monitored 24/7
65% Downtime Reduction -65%
$12M Cost Savings Annual
Project Overview
Project Overview
SABIC’s Jubail complex is one of the world’s largest petrochemical facilities. We implemented AI-driven predictive maintenance to prevent costly equipment failures.
The Challenge
Unplanned equipment failures caused production losses exceeding $50M annually. Traditional time-based maintenance was inefficient and missed critical issues.
Our Solution
We deployed an integrated predictive maintenance platform:
- IoT Sensor Network: 2,000+ sensors monitoring vibration, temperature, and pressure
- AI Prediction Engine: Machine learning models trained on 10+ years of failure data
- Real-time Dashboards: Equipment health scores and maintenance recommendations
- Mobile Alerts: Instant notifications for predicted failures
Results
- 65% reduction in unplanned downtime
- $12M annual savings in maintenance costs
- 95% accuracy in failure predictions
- 50% optimization of maintenance schedules
Project Gallery
Project Journey
Project Timeline
September 2023
Data Collection
Deployed IoT sensors across critical equipment
February 2024
AI Training
Trained ML models on historical failure data
May 2024
Production
Achieved 95% prediction accuracy
Results
Project Impact
Measurable outcomes and improvements delivered
500+
Assets Monitored
24/7
65%
Downtime Reduction
-65%
$12M
Cost Savings
Annual
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