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

Tariff unpredictability and logistic uncertainty are consistently becoming bigger challenges to supply chain planners as they attempt to evaluate reshoring options. Traditional evaluation methods using spreadsheet programs treat tariff and logistics costs as constant inputs and do not capture nonlinear interactions between component structures, routing decisions, and the assembly capacity. To formulate reshoring assessment as a digital twin-driven decision system, this paper presents a stochastic process optimization framework. The architecture combines automated tariff classification, stochastic landed cost simulation, and mixed-integer linear programming (MILP) to enable repeatable and auditable decision-making. Bills of materials are represented by dependency graphs, which allow one to reason at the process level about alternative assembly configurations. Operational uncertainties, such as variation in transportation, labor throughput, and volatility in tariffs, are factored into the optimization process through Monte Carlo simulation. With a synthetic yet realistic product scenario, experimental assessment shows that a cost reduction of about 9-16% and a major improvement in robustness is obtained over the static estimation methods. The findings establish that a stochastic decision process is better suited to the explicit modeling of reshoring evaluation, with respect to its scalability and resilience. The suggested framework offers a solid basis of decision support in adaptive supply chain systems.