3. “Microscopic” (DES) versus “macroscopic” (finance) approaches The use of MCS in mining has followed two complementary trends: • Microscopic (operations, DES): Models processes at the discrete-event level loadinghauling cycles, availability, queues to evaluate productivity, costs, and operational KPIs. Underground and open-pit mining research integrates DES with MCS or ML to predict production, validate haul routes, size fleets, and analyze the stochastic variability of cycle times; this literature emphasizes empirical distributions, calibration with mine data, and integration with @Risk or other simulation environments. (Tabesh et al., 2016) • Macroscopic-financial (project value): This approach uses aggregate variables (metal price, recovery, mine and plant cash costs, CAPEX) to construct simulated cash flows and derive the NPV or IRR distribution. This is the approach your paper adopts; recent literature shows specific applications in gold mining projects and continuous flow models, confirming that MCS improves the robustness of valuation and risk assessment compared to DCF. (Sarsoruo et al., 2019) 4. Modeling practices: distributions, correlations and tools Selection of distributions. In finance and mining costs, Normal, Lognormal, and Triangular distributions are common, depending on the nature of the data (e.g., prices and multiplicative factors are usually modeled lognormally; costs/times with limited information are modeled triangularly). There are guides and educational resources that show how to simulate lognormal and triangular distributions, and when to prefer one over the other based on skewness and tailings. (Lampe, 2015) Correlation between inputs. The appropriate correlation of variables (e.g., price-exchange rate, grades, recovery, interrelated costs) is critical; treating inputs as independent can over or underestimate the variance of the NPV. In the literature on multivariate correlation matrices applied to finance and infrastructure, correlation matrices, multivariate matrices, or copulas are recommended to preserve dependencies and improve the consistency of scenarios. (Zavala et al., 2025) Tools. In professional practice, the use of @simulacion 5.0 integrated with Excel stands out for defining distributions, correlations, and running thousands of iterations, with outputs such as histograms, cumulative curves, sensitivity tornadoes , and dashboards for stakeholders. (Varela, 2025)
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