platforms — fleet management systems, IoT sensors, digital twins — that already form part of the technological ecosystem of modern mining operations. In this context, the value of DES does not diminish but multiplies: a calibrated model fed with real-time data constitutes the analytical foundation for operational decision-making at shift level, not only for long-term planning. 8. CONCLUSIONS This paper presented a methodological framework for the use of discrete-event simulation as a decision-making tool in underground mining, structured around three capabilities: verification of productive capacities, generation and evaluation of scenarios, and validation of production plans. The main conclusions are: Discrete-event simulation overcomes the fundamental limitations of traditional analytical methods by explicitly capturing system stochastic variability, saturation and interference effects between resources, and interactions between subsystems. These capabilities make it the most suitable tool to support investment and operational decisions in environments of high complexity and high economic impact. The decomposition of the system into three interdependent subsystems — production level, haulage level, and material handling system — allows the dominant system constraint to emerge naturally from simulation, rather than being assumed a priori. This capability is especially valuable in expansion contexts, where the dominant constraint changes as new production sectors are incorporated. Statistical calibration and validation of the model against real historical data is a necessary condition for simulation results to have value as support for high-impact decisions. A calibrated model that faithfully reproduces the historical behavior of the system provides a solid foundation for projecting the impact of structural changes with quantifiable confidence. The continuous use of the calibrated model — periodically updating it with the most recent operational data — transforms simulation from a one-time engineering deliverable into a permanent operational management tool, capable of accompanying the operation's decision cycle throughout its entire useful life. ACKNOWLEDGEMENTS The authors thank the operational teams of the underground mining operations whose historical information has supported the development and validation of the methodological framework presented, for their openness in sharing operational data and for their contribution to the validation of model logic.
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