AI-Powered Fleet Optimization and Mining Optimization for a Natural Resources Giant

Challenge

Mining operations faced significant challenges in optimizing daily site activities, particularly in coordinating fleet scheduling and managing real-time analytics.

The complexity of large-scale operations required precise scheduling to minimize delays, reduce idle time, and maximize equipment utilization.

However, achieving this level of efficiency was difficult, especially in remote locations where connectivity issues hindered real-time data transmission and decision-making.

Solution

We developed an AI/ML-driven solution that addressed two key challenges: mining site analysis and fleet optimization.

For mining site analysis, the solution utilized machine learning to analyze historical data and site analytics data to provide accurate predictions of site and seams value, enabling to assess profitability and make informed decisions to mine or not. By continuously refining its models, the system ensured improved accuracy over time.

For fleet optimization, our solution employed intelligent scheduling algorithms to maximize equipment utilization and reduce idle time. It dynamically adjusted fleet assignments based on real-time conditions, even in low-connectivity environments, to ensure smooth operations and reduce fuel consumption, downtime, and operational costs.

The solution was initially deployed at a single site as a pilot project, demonstrating its potential to drive data-informed decision-making. Following its successful implementation, plans are underway for a broader rollout across multiple locations to maximize its impact.