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Global mining organisation
AI-driven predictive asset management driving productivity
With a focus on increasing digital capability to drive efficiency and safety across the client’s operations, autonomous mines, haul trucks and trains and upgraded port infrastructure have created a more efficient link from mine to port to mine.
With thousands of kilometres of rail network carrying trains on an almost continuous loop between multiple mines and ports, more than 20,000 kms of rail travel occurs every day. A challenge arose in the rail tracks themselves which struggled to keep pace with other digital and infrastructure improvements.
Multiple track issues slowed down the mine to port return journey, costing the business millions.
EY teams collaborated with the client and subject matter experts to develop an innovative, first-of-its-kind in Asia-Pacific, AI deep machine learning solution that could predict and prioritize rail track condition issues and develop prescriptive maintenance recommendations that returned the highest business value.
The swathes of detailed data from individual autonomous train journeys, was overlaid with other critical performance data such as track condition monitoring, rail maintenance schedules, and track obstructions, and fed into AI deep learning models to help determine exactly where there are performance constraints, down to one hundred metre lengths of track.
The project was given three specific objectives to enable:
- Fix underlying track problems and remove rail track issues safely and quickly
- A solution that could help to identify which issues were impacting the bottom line the most.
- Be in a position to develop an optimized, predictive maintenance schedule that prioritised and addressed the highest impact network issues first.
All three objectives have been achieved with the solution now reducing time off the mine to port return journey, which is translated into savings to the client every day.