<![CDATA[Bottlenecks in apparel manufacturing often cause unbalanced production flows, increased waiting times, and reduced system performance. This study aims to analyze and eliminate bottlenecks by integrating Design of Experiments (DOE) and Discrete Event Simulation (DES). Four workstations (X1–X4) were selected as experimental factors, while system performance was evaluated using bottleneck indicators across six production stages (Y1–Y6). DOE was used to design capacity scenarios, and DES assessed system performance under each configuration. Results show that partial capacity increases at selected workstations are insufficient to fully eliminate bottlenecks. Complete elimination was achieved only in specific scenarios (Experiments 13–16), where all bottleneck indicators reached zero. Among these, Experiment 13 was identified as the optimal solution, as it eliminated all bottlenecks with the minimum additional capacity. These findings indicate that targeted capacity enhancement at critical workstations is an effective and economical strategy. The integration of DOE and DES proves to be a reliable data-driven approach for identifying bottlenecks and selecting optimal capacity improvements. This study also provides a structured and replicable framework for bottleneck analysis in apparel manufacturing, contributing to the limited application of DOE–DES integration in this sector.]]>
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