2100 • Which variables are worth mitigating or controlling? Enabling boards today to make decisions based on P50, P90 and more than just a single NPV. 5. Monte Carlo reveals the "left tail" (extreme risks) that classical methods conceal. Since knowing the average NPV is not sufficient. • How bad can the worst situation get? • How much venture capital should I protect? • Could this project jeopardize the entire operation? • Monte Carlo allows us to measure VaR (Value at Risk). This is critical for corporate financial and strategic management. 6. Monte Carlo simulation allows the identification of variables that explain risk (Risk Drivers). • What factors most affect NPV? • Where to invest to reduce volatility • Which parameters essentially impact the value? Monte Carlo doesn't just calculate the possible future: it reveals all possible futures, their risks and opportunities. ACKNOWLEDGEMENTS The author expresses his gratitude, primarily to my wife and children, who have allowed me to share their time in the preparation of this research. I would also like to thank APUCORP for allowing me to use this tool in decision-making for the projects we are developing. I am also grateful to the entire executive team whose ongoing feedback makes each analysis better. REFERENCES Ahmed, H. M., Adewuyi, S., & Ahmed, H. M. A. (2024). Continuous Cash Flow Modeling for Economic Evaluation and Risk Analysis in Mine Planning Using Monte Carlo Simulation. Journal of King Abdulaziz University: Engineering Sciences, 34(2), 81–93. https://doi.org/10.4197/eng.34-2.6 Lampe, J. (2015). Triangular Distributions and Correlations. 1–29. Malz, A. M. (n.d.). Value-at-Risk. Mathey, M. (2022). Simulation of production processes and associated costs in mining using the Monte Carlo method. Journal of the Southern African Institute of Mining and Metallurgy, 122(12), 697–703. https://doi.org/10.17159/2411-9717/2079/2022 Mirakovski, D., Krstev, B., Krstev, A., & Petrovski, F. (2009). MINE PROJECT EVALUATION TECHNIQUES [Técnicas de evaluación de proyectos mineros]. Park, S., Jung, D., & Choi, Y. (2023). Prediction of Ore Production in a Limestone Underground Mine by Combining Machine Learning and Discrete Event Simulation Techniques. Minerals, 13(6). https://doi.org/10.3390/min13060830 Sarsoruo, C., Gebo, R., & Anderson, P. K. (2019). Simulating a Lognormal Distribution: A Monte Carlo method. Electronic Journal of Informatics, 1, 73–89. Tabesh, M., Upadhyay, S. P., & Askari-Nasab, H. (2016). Discrete Event Simulation of Truck-Shovel Operations in Open Pit Mines. 1–17.
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