Driller Solution
AI-powered fleet dispatch that dynamically allocates trucks and optimises haul cycles in real time, improving fleet utilisation, reducing idle and queue times, and maximising mine productivity
.png)
It integrates real-time telemetry, 3D spatial site mapping, and predictive algorithmic scheduling to optimise heavy equipment haulage cycles. By continuously analysing payload dynamics, excavator loading times, and pit-to-crusher traffic bottlenecks, the platform dynamically recalculates optimal routing for autonomous and manned fleets. Controllers gain complete end-to-end visibility through a unified dashboard, enabling automated dispatch decisions that minimise queue times and maximise tons per hour.
The system autonomously recalculates optimal cycle distribution, accounting for grade variations, fuel burn rates, and live excavation rates to prevent haul-road congestion. Operational dispatchers leverage deep-learning predictive models to proactively identify equipment failure risks and shovel-truck mismatches before productivity drops.
.png)
.png)
It integrates real-time telemetry, 3D spatial site mapping, and predictive algorithmic scheduling to optimise heavy equipment haulage cycles. By continuously analysing payload dynamics, excavator loading times, and pit-to-crusher traffic bottlenecks, the platform dynamically recalculates optimal routing for autonomous and manned fleets. Controllers gain complete end-to-end visibility through a unified dashboard, enabling automated dispatch decisions that minimise queue times and maximise tons per hour.
The system autonomously recalculates optimal cycle distribution, accounting for grade variations, fuel burn rates, and live excavation rates to prevent haul-road congestion. Operational dispatchers leverage deep-learning predictive models to proactively identify equipment failure risks and shovel-truck mismatches before productivity drops.
.png)
01
Advanced Assignment Optimisation
Smarter, Data-Driven Dispatch Decisions