We integrated the simulation module for our distribution center in Córdoba. The initial setup required several iterations to align the input parameters with our actual lead time distributions. The team provided clear documentation on the expected data format, but the first run produced a 12% deviation from our historical stockout rate. After adjusting the demand variance threshold and re-running the Monte Carlo simulation, the model converged to within 3% of observed values. The communication during this tuning phase was direct and technical, without unnecessary delays. What I valued most was the willingness to explain why certain statistical assumptions were made, rather than just delivering a black-box output. For a logistics operation handling perishable goods, this level of transparency is essential. The only friction point was the initial data mapping: our ERP exports inventory snapshots in a non-standard timestamp format, which required a custom preprocessing script. Once resolved, the weekly simulation cycles ran without issues.