How Rio Tinto reduced lost production time by up to 94% through predictive analytics
Cross-site models anticipate downtime drivers and trigger prevention. Teams recover hours that previously disappeared.
Context
The operation was losing significant production time due to equipment and process events that were not detected early enough. Traditional monitoring approaches could not identify the root causes of downtime quickly or consistently. This created avoidable performance losses across multiple circuits.
Solution
Predictive analytics were deployed at scale to track equipment behavior, process stability, and early indicators of failure. Models analyzed data across fleets and plant systems to surface leading indicators of production loss.
Results
According to case data, the approach enabled up to a 94% reduction in lost production time, showing substantial improvement in operational continuity and event prevention.
Data Inputs
Operational, maintenance and sensor data