The management of production bottlenecks has become a critical issue in the effective planning and control of manufacturing systems, as bottlenecks directly constrain throughput, increase lead times, and reduce overall system efficiency. Simulation techniques provide a powerful and practical tool for analyzing and improving such systems, offering opportunities to test alternative scenarios and evaluate decision-making strategies without disrupting real operations. This study investigates the application of discrete-event simulation to a job shop production environment with the objective of enhancing productivity by identifying and mitigating bottlenecks. Using Arena simulation software, an initial model of the production line was developed and validated through both rough-cut capacity planning (RCCP) and expert judgment from the planning department. The bottleneck analysis employed a heuristic approach based on the Shortest Processing Time (SPT) rule to minimize average queue length and waiting times. Results obtained from five simulation replications demonstrated that, after two adjustment attempts, the production line achieved a balanced flow, significantly reducing queuing and waiting times. Consequently, system throughput increased from baseline level A to A+448, corresponding to an estimated financial gain of approximately one million U.S. dollars. These findings highlight the potential of simulation-based approaches for supporting data-driven decision-making and continuous improvement in job shop production systems.