From pit to port, mining is a chain of variable, capital-intensive steps. Stochastix models the whole value chain, so you can de-risk expansions, right-size fleets and hit shipping targets, instead of planning on averages that quietly destroy value.
Ore grades and commodity prices swing constantly. Plans built on a single expected case look fine on paper and underperform in reality.
Pit, crusher, rail and port rarely choke where you expect. Spending capital to debottleneck the wrong step is an expensive way to learn that.
Truck and shovel fleets are enormous capital and operating costs. Over-sizing wastes money; under-sizing strands tonnes.
Meeting product specs from variable ore while juggling stockpiles and cut-off grades is a continuous, high-stakes balancing act.
We combine simulation, optimisation and forecasting into tools your planners and executives use to make confident, defensible calls.
A discrete-event digital twin of the full chain, load, haul, crush, rail, stockpile, ship, to find the real constraint and test debottlenecking before you spend.
Optimal truck/shovel allocation, haul-route and dispatch decisions, plus blend and cut-off grade optimisation to hit specs at lowest cost.
Monte Carlo NPV and throughput analysis across price and grade scenarios, so capital decisions come with a probability, not just a point estimate.
Worked examples of how we approach mining and resources problems, and the decision each one informs.
A full-chain discrete-event model of load, haul, crush, rail, stockpile and ship pinpoints the true binding constraint, which often isn't where it's assumed, so capital goes to the step that actually limits tonnes.
Mixed-integer optimisation of truck and shovel allocation across shift patterns and pit phases, validated against a simulation twin and stress-tested over price and grade scenarios.
A Monte Carlo NPV model for a major expansion propagates commodity-price, grade and ramp-up uncertainty into a full distribution of outcomes, so the board sees the odds rather than a single point estimate.
Tell us about the chain, the constraint you suspect, and the decision in front of you. We'll tell you honestly whether a model will help, and how we'd build it.
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