In modern manufacturing environments, the increasing complexity of production processes demands innovative strategies for optimizing assembly operations. This study presents a systematic methodology for planning and executing simulation experiments aimed at minimizing production lead time. Utilizing a 23 factorial design integrated with discrete event simulation, the proposed approach identifies and quantifies the individual and interactive effects of three key factors: assembly time at a critical workstation, the number of machines, and the number of operators. The experimental design ensures a manageable number of simulation runs while capturing both main effects and interactions, and advanced statistical techniques such as ANOVA and regression analysis are employed to validate the model and interpret the data. Results indicate that optimizing operator count and machine capacity can significantly reduce production lead time, whereas variations in assembly time have a comparatively smaller impact. Moreover, the methodology addresses practical challenges related to data quality and model validation, providing a robust framework for integrating simulation outputs into decision-making processes.