This study examines delays in processing returned shipments at a beverage distribution center in Bogotá, Colombia. Returned trucks undergo reception, inspection, and reloading processes, which can result in congestion at the processing docks. We develop a discrete-event simulation model based on historical operational data and a focused time-and-motion study to represent the current system. Two improvement scenarios are evaluated: (1) increased verification staffing, and (2) regulated truck arrivals (i.e., arrival scheduling). Performance is assessed in terms of average turnaround time, average waiting time, processing time, queue length, resource utilization, and number of external waiting vehicles (i.e., congestion caused around the distribution center to citizens). Simulation results indicate that the addition of staff reduces truck turnaround by approximately 30%, decreases the average queue length by over 60%, and lowers peak dock utilization. Meanwhile, managed arrivals yield more consistent throughput, with a 20% reduction in waiting times and decreased variability in resource use. Both interventions enhance driver work–life balance, improve service levels, and mitigate community impacts caused by external queuing. The proposed framework offers a replicable, data-driven approach for distribution centers to mitigate dock-level bottlenecks, enabling informed decision-making on resource allocation and scheduling strategies.