2088 The scope also includes the development of advanced risk indicators, such as the project's Value at Risk (VaR) and the probability of reaching profitability thresholds. Furthermore, it incorporates a variance contribution analysis, which identifies and quantifies the relative weight of each variable in the overall financial risk. This disaggregation allows for prioritizing control, optimization, and monitoring efforts, integrating the model within a data-driven decision governance framework that supports the corporate strategy. From the Congress's perspective, the paper demonstrates how advanced analytics and probabilistic modeling techniques, typically associated with data science, can be integrated into the mining project evaluation process. This transforms simulation into a tool that not only estimates possible outcomes but also predicts complete distributions, identifies risk drivers, and makes financial decision-making a systematic, transparent, and quantifiable process. In practical terms, the model can be implemented on accessible platforms such as Excel® or specialized software like simulation 5.0, allowing its adoption by finance, planning, geology, and operations teams. In this way, the work offers a replicable methodological framework that strengthens the organization's ability to manage uncertainty, optimize investments, and align with the global trend toward smart mining and data-driven decision-making. 3. STATE OF THE ART 1. From deterministic evaluation to probabilistic analytics in mining The economic evaluation of mining projects has historically relied on DCF, NPV, and IRR with sensitivity analysis and scenario planning to capture uncertainty. However, such univariate or deterministic approaches underestimate the joint variability and interdependencies of value drivers (metal price, grades/recovery, costs, exchange rate), especially over long horizons and in volatile contexts. Industry literature supports the adoption of Monte Carlo Simulation (MCS) to quantify risk through probability distributions on inputs and obtain NPV distributions, moving beyond a single reading of the base case. In mining, this shift is consolidated in reviews and methodological guides that compare DCF, decision trees, and MCS, and recommend the probabilistic approach for capital-intensive investments with significant geological, market, and operational risks. (Mirakovski et al., 2009) 2. Monte Carlo in mining: Foundational contributions and adoption Classic works in the mining sector introduced risk management with MCS early on, highlighting its applicability to decision-making under uncertainty and bringing these techniques closer to practice through spreadsheet models and specialized add-ins. Heuberger's (2005) contribution is frequently cited as a general introduction to MCS in mining; recent publications continue this line of thought, demonstrating implementations in Excel® + @Simulation 5.0 and discussing the value of simulation in supporting financial and operational decisions. (Mathey, 2022) More recently, comparative studies applied to gold projects and mining cash flows have shown that, with 10,000 iterations and realistic assumptions, MCS delivers probabilistic forecasts (p-curves) of NPV, highlights gaps compared to DCF, and facilitates executive discussion of downside risk, validating its use for pre-feasibility and feasibility. (Ahmed et al., 2024) 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 loading-hauling 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)
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