Effective decision-making in manufacturing necessitates rigorous analytical methodologies and realistic process modeling. This study investigates a discrete event-based simulation approach to optimize the Gate Valve Value Stream (GVVS) production line. The proposed simulation integrates statistical tools to evaluate key performance indicators, including cycle times, throughput, and resource utilization, ensuring data-driven insights underpin all decisions. The digital twin of the manufacturing system is realized by the simulation software FlexSim, incorporating Three-Dimensional Computer-Aided Design (3D CAD) models of realistic entities to enhance visualization accuracy and interpretability. These high-fidelity representations facilitate a better understanding of the dynamics of the manufacturing system and can support further validations and decision-making for operational changes. In addition, interactive dashboards enable stakeholders to conduct “what-if” experimentations and analyses, allowing the evaluation/assessment for alternative decisions, such as new layouts, adjusting operator numbers, or reallocating resources without disrupting operations. The presented simulation-driven approach aligns with lean manufacturing principles by systematically identifying inefficiencies, minimizing trial-and-error iterations, and promoting continuous process improvement. The adaptability of the proposed framework ensures scalability across different production environments, reinforcing its applicability in dynamic manufacturing settings. Scenario-based simulation analyses revealed a 40% improvement in throughput across two work cells, demonstrating the tangible impact of the proposed approach. This improvement directly contributes to enhanced production efficiency and supports data-backed decision-making for strategic resource planning. This research highlights the synergy between the simulation-driven analytic approach, manufacturing system visualizations, and operational flexibility to support strategic decision-making and advance cyber twin technologies in the digitization of conventional manufacturing systems.