Stockpile Dynamics Excel Model (with Monte Carlo simulation)

At its core, the Stockpile Dynamics Excel Model operationalizes material flow physics within constrained physical infrastructure—e.g., stacking and reclaiming equipment capacity, stockpile geometry limits, and segregation effects—using discrete-time simulation (typically daily or shift-based timesteps). Monte Carlo simulation introduces stochasticity by sampling from empirically calibrated probability distributions for key drivers: incoming ore tonnage (Poisson or lognormal), feed grade (multivariate normal or historical histogram-based), moisture content (beta-distributed), and equipment availability (Bernoulli or Weibull failure models). Each simulation iteration propagates these random inputs through deterministic mass-balance equations—tracking cumulative inflow, outflow, and net stockpile change—while enforcing hard constraints (e.g., maximum stockpile height, minimum reclaim rate, FIFO/LIFO stacking logic). Outputs include probabilistic time-series forecasts (e.g., 5th–95th percentile stockpile volume envelopes), grade distribution histograms at drawpoint, and KPIs like expected stockout days per quarter or probability of violating metallurgical feed specifications. The model is typically built using Excel’s native tools (Data Tables, RAND(), NORM.INV()) enhanced with add-ins like @RISK or Crystal Ball for advanced sampling, sensitivity analysis, and scenario comparison—enabling practitioners to evaluate trade-offs between buffer sizing, blending strategy robustness, and capital expenditure on additional stockpiling infrastructure.