Description
Predict problems and recommend proactive maintenance for mining, drilling and support equipment
Benefits & ROI
1.6
Linked Case Studies
Case Study
BHP
BHP saves $5.5M by predicting mining truck maintenance requirements with machine learning
Case Study
Origin
Origin achieved 80% accuracy in identifying low production wells and $50M in savings using machine learning applications from C3 IoT
Case Study
Gazprom
Gazprom Neft to optimise drilling and well completion with the use of artificial intelligence
Case Study
Newcrest Mining
Newcrest Mining prevents mill downtime at its Lihir mine using machine learning to predict when an overload is about to occur
Case Study
Roy Hill
Roy Hill iron ore project is developing a transportation vehicle maintenance prediction and prevention recommendation platform
←Back to Use Case Library
Source: mckinsey.com · Editor: original-sdg