GATrESN: An AI-Driven Digital Twin Framework for Strategic Supply Chain and Logistics Network Management

Strategic logistics network management requires decision support that anticipates demand and disruption uncertainty and evaluates candidate decisions before implementation. This paper presents GATrESN, a simulation-based predictive–prescriptive framework that couples Echo State Networks, a fixed parameter-free multi-head attention mechanism and a genetic algorithm over a robust scenario objective into a single decision loop over capacity, cost, inventory, workforce, resilience, and supplier redundancy. The case study is a panel of 24 sites, 16 product families, and 640 periods with 1800 matched decision instances. Against a linear-programming benchmark corrected so that the evaluator’s quality threshold is visible to it, and scored against realized rather than forecast states, the framework improves the strategic score by 0.0236 (95% bootstrap CI 0.0222–0.0250); the apparent margin against the original, weaker benchmark specification was 0.0596. Four external optimizers of the same objective reach the same operating region, and replacing the predictive core with a ridge readout changes ex post decision quality by 0.0001, locating the advantage in the robust objective rather than in the optimizer or the forecasting architecture. A closed-loop rolling-horizon experiment, in which decisions alter installed capacity, inventory, and risk at the next period, widens the advantage to 0.0315 and raises the worst-period service level from 0.52 to 0.76. Without automated exchange with a physical counterpart, the implementation is a digital model; the paper specifies the data acquisition required to close that loop.