2067 Unscheduled equipment failures. Both mobile equipment and MHS equipment are subject to unscheduled failures that take them out of service for variable periods. Their modelling requires timebetween-failure and repair time distributions fitted to historical maintenance data, differentiating between failure types according to their cause and typical duration. Planned operational interferences. Shift changes, meal breaks, fuel loading, safety inspections, equipment cleaning, and scheduled maintenance are planned events that, collectively, represent a significant fraction of shift time. Their omission from the model produces systematically optimistic capacity estimates that do not reflect operational reality. Variability in operating parameters. Loading times, travel speeds, and equipment maneuvering times are not fixed values, but distributions influenced by operator experience, infrastructure condition, and working point conditions. Their representation through statistical distributions rather than average values allows the model to correctly reproduce the variability observed in real records. 3.4 Calibration Process and Number of Replications Model calibration follows an iterative process: the model is built with parameters derived from available data, an initial number of replications is run, results are compared with historical records using formal statistical tests, and parameters are adjusted until statistical equivalence is achieved. The standard calibration and validation approach, described by Banks et al. (2005) and elaborated by Law (2015), uses formal statistical tests to compare model output distributions with historical distributions. The Student's t-test evaluates the equivalence of means; Anderson-Darling or KolmogorovSmirnov tests evaluate distributional similarity. A model that passes these tests on the calibration period data, and then correctly reproduces an independent validation period, provides a solid foundation for future scenario projections. The number of replications required to obtain statistically robust results is determined empirically, monitoring the convergence of the cumulative mean and variance of the output variable of interest. Replications are added until both statistics stabilize, which in practice typically requires between 15 and 40 independent replications depending on the variability of the subsystem analyzed. This criterion ensures that reported results do not depend on random number sequences. 4. VERIFICATION OF PRODUCTIVE CAPACITIES 4.1 From Nominal Calculation to the Distribution of Results Verification of productive capacities through simulation fundamentally differs from the analytical calculation of nominal capacity. While the analytical calculation delivers a single value — the expected production under average conditions — simulation delivers a complete distribution of results that reflects the actual variability of the system. Applying the principle developed in Section 3.3, simulation delivers the cumulative distribution function of daily production, enabling targets to be set with explicit probability of compliance rather than point nominal capacities. 4.2 Performance Indicators Verified by Simulation The calibrated model enables verification of a broad set of performance indicators that characterize the actual productive capacity of the system: At the production level: daily and per-shift tonnage, loading equipment cycle times, effective operating hours, number of completed cycles, percentage of time in each operational state (active extraction,
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