LHD is added to the most productive sector? What happens to compliance if the hang-up frequency in the next quarter corresponds to the 90th percentile of historical difficulty? The answers to these questions, obtained by running the model under different parameter combinations, generate a plan sensitivity map that enables management to identify the most relevant risk factors and design specific contingency protocols for each. This map converts uncertainty — which in traditional methods is simply ignored — into actionable information for management. 6.3.Use of the Model in Continuous Operational Management The value of the model is not exhausted in the initial validation of the annual plan. A calibrated model can be updated periodically with the most recent operational data — cycle times from the previous month, newly observed failure rates, changes in hang-up frequency by sector — and reused to re-validate the plan under current system conditions. This continuous use of the model converts simulation into an operational management tool, not just an engineering design tool. It enables early detection of deviations between actual system behavior and projected behavior, identification of whether those deviations originate in the PL, HL, or MHS, and real-time evaluation of which corrective actions — fleet changes, adjustments to the extraction plan, prioritizations in the maintenance programme — have the greatest impact on plan compliance. 7. DISCUSSION 7.1.Value Proposition for Mining Management The systematic adoption of DES as a management tool represents a paradigm shift: it does not replace engineering judgement, but equips it with a more robust, communicable, and auditable quantitative foundation, reducing investment risk, improving the reliability of production commitments, and accelerating the decision cycle. 7.2.Enabling Conditions The realization of these benefits requires three conditions. First, availability of quality data: systematic records of cycle times, failures, hang-ups, and maintenance, whose availability grows with the digitalization of operations. Second, technical simulation competence: the capacity to build, calibrate, and interpret models, continuously available so that the model is a living tool rather than an ageing deliverable. Third, organizational willingness to base decisions on model results even when they contradict accumulated intuition. 7.3.Limitations and Future Evolution Simulation models are simplifications of reality and there will always be phenomena not captured, whether due to limitations in available data or the intrinsic complexity of certain operational behaviors. The natural evolution of the presented approach points towards dynamic update models integrated with real-time operational data
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