A Stochastic Process Optimization Framework for Reshoring Supply Chains: Integrating Digital Twins with Mixed-Integer Programming

Supply chain planners face increasing difficulty in evaluating reshoring decisions due to volatile tariff regimes and logistics uncertainty. Traditional spreadsheet-based evaluations treat tariffs and logistics costs as fixed inputs and fail to capture nonlinear interactions among component structures, routing choices, and assembly capacity. This paper presents a stochastic process optimization framework that models reshoring evaluation as a digital twin–based decision system. The architecture integrates automated tariff classification, stochastic landed-cost simulation, and mixed-integer linear programming (MILP) to support repeatable and auditable decision-making. Bills of Materials are mapped to dependency graphs, enabling process-level reasoning over alternative assembly configurations. Operational uncertainties—including transportation variability, labor throughput, and tariff volatility—are propagated through Monte Carlo simulation and incorporated into the optimization process. Experimental evaluation using synthetic but realistic product scenarios demonstrates cost reductions of approximately 9–16% and significant improvements in robustness compared to static estimation approaches. The results indicate that explicitly modeling reshoring evaluation as a stochastic decision process improves scalability and resilience. The proposed framework provides a rigorous foundation for operational decision support in adaptive supply chain systems.