Simulation and optimization are fundamental strategies employed in supply chain management. Optimization involves identifying the optimal solution based on specific criteria, but its effectiveness is limited by the high complexity, dynamics, and randomness inherent in processes. On the other hand, simulation, not reliant on a closed-form mathematical model, facilitates multi-attribute dynamic risk analysis for systems characterized by high randomness, though it is not inherently an optimization approach. The integration of optimization with simulation forms an efficient tool for planning and controlling supply chains. In this study, we have developed an integrated simulation-based optimization algorithm to provide decision support for inventory management. The developed procedure was applied to address a real-world problem within the realm of inventory management. Additionally, we highlight the advantages of incorporating the proposed algorithm into the concept of digital twins.