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, waiting due to hung-up point, waiting for truck, in planned interference, in transit), and distribution of extraction among active drawpoints. At the haulage level: tonnage transported per shift, truck cycle times per route, fleet utilization, waiting time at loading stations and dump points, and percentage of time with congestion in the underground road network. In the material handling system: physical availability and utilization of each element in the chain, tonnage processed per period, frequency and duration of cascading stoppages, occupancy levels of intermediate storage systems, and distribution of daily production delivered to surface. 4.3.Identification of the Dominant Constraint or Bottleneck A highly practical result that emerges naturally from the integrated model is the identification of the dominant system constraint in each evaluated scenario. In a system with three interdependent subsystems, the dominant constraint — the subsystem that limits the overall productive capacity — is not always the same: it changes according to the fleet condition, MHS failure rates, and operational configuration. The model enables quantification of the relative contribution of each subsystem to production losses relative to plan, expressing how many tonnes per period are lost due to PL, HL, and MHS constraints respectively. This information has direct value for priority assignment in operations management: if 70% of losses originate in the MHS,
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