🤖 AI Summary
This study addresses the inefficiencies in traditional supply chains, where isolated optimization of individual decision modules often leads to inventory imbalances and elevated transportation costs. To overcome this limitation, the authors propose an end-to-end collaborative composite policy model that, for the first time, formulates multi-stage operational decisions—product assortment, sourcing allocation, replenishment frequency, and delivery routing—as a sequentially coupled token generation process. Supply chain entities are represented as tokens, and decisions are generated in sequence through a shared contextual representation and a serialized decision interface, enabling tight coupling across stages. The entire replenishment plan is evaluated holistically via a system-level utility function. Experiments on real-world datasets from Dingdong Maicai and JD.com demonstrate that the proposed approach significantly outperforms both stage-wise independent optimization and industry baselines, confirming the effectiveness of cross-module joint learning in enhancing overall supply chain performance.
📝 Abstract
Can supply-chain AI move beyond isolated decision modules toward unified operational planning? A complete replenishment plan specifies which products each location carries, which upstream facility supplies it, how often it is replenished, and how deliveries are routed. These decisions are operationally coupled: the selected assortment changes the demand and load passed to later stages; source assignment and replenishment frequency reshape the delivery requests; and route feasibility and cost, in turn, determine the system value of the earlier choices. Yet in modern supply chains, these decisions are often handled by separate departments and optimized through separate systems, which can lead to stockouts, inventory exposure, and avoidable transportation. We propose SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination, a composite policy model that represents supply-chain entities as tokens, contextualizes them through a shared operational representation, and maps each token type to the corresponding decision interface. Each decision builds on the partial plan formed by earlier decisions while the completed plan is evaluated using a shared system-level utility. We instantiate this framework in urban fresh-retail replenishment, where service frequency, assortment, capacity pressure, and road-network routing interact strongly, and evaluate it on real operational data from Dingdong and JD.com, two large-scale supply chains operating at different replenishment echelons. Across both settings, SCOPE consistently outperforms methods that optimize each decision stage separately, as well as practice-oriented baselines commonly used in supply-chain operations. These results show that learning and coordinating cross-department operational couplings lead to more effective end-to-end supply-chain decisions.