Discrete-Event Simulation for Waste Minimization and Productivity Enhancement in Coupling Manufacturing

Achieving operational excellence in metalworking industries demands tools that accurately model complex production dynamics and guide improvement strategies. This study applies a discrete-event simulation (DES) framework to optimize productivity and reduce steel waste in coupling manufacturing for oil pipeline applications. A six-phase methodology was implemented, covering system characterization, conceptual modeling, statistical data fitting, Python-based simulation, model verification and validation, and experimental scenario analysis. Four improvement scenarios, preventive maintenance, operator training, material quality control, and integrated optimization, were evaluated through ANOVA. Results show that the integrated scenario increased throughput by 14.5%, improved OEE by 8.6%, reduced scrap generation by 35.4%, and shortened lead time by 11.5% compared with the base model. The validated DES model achieved less than 5% deviation from actual plant data, confirming its precision and reliability. The study establishes DES as a robust decision-support tool for industrial optimization and sustainable waste reduction. Future research should integrate real-time data and digital twin architectures to enable adaptive improvement in smart manufacturing.