SCOPE: Supply-Chain Operations through Coupled Poli... | AI Research

SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination introduces a unified AI framework designed to replace the fragmented, department-specific systems currently used in supply-chain management. By treating supply-chain entities as tokens within a shared operational representation, the model coordinates assortment, replenishment, and routing decisions to optimize the entire system rather than individual silos.

The Problem of Isolated Decision Modules

Modern supply chains typically separate decision-making into functional layers: merchandising teams select products, replenishment teams determine delivery frequency, and logistics teams manage routes. The authors argue that this decomposition is "algorithmically incomplete" because decisions in one layer create ripple effects in others. For instance, a decision to increase replenishment frequency may lower inventory exposure but simultaneously increase transportation costs and capacity pressure. Because these departments often operate in isolation, they fail to account for these "latent operational couplings," leading to stockouts, inefficient transportation, and hidden costs that are not captured by local optimization tools.

How SCOPE Works

SCOPE (Supply-Chain Operations through Coupled Policies for End-to-End Coordination) functions as a composite policy model that maps different supply-chain entities—such as products, facilities, and demand—into a shared operational representation. The framework follows a sequential decision pipeline:

1. Assortment Selection: Determines which products are carried, influencing the demand and load for later stages. 2. Source Assignment: Assigns locations to upstream supply nodes. 3. Replenishment Planning: Sets the frequency of deliveries. 4. Routing: Constructs delivery routes based on the preceding choices.
   Each stage builds upon the partial plan created by the previous ones. Instead of calculating the ideal value for every possible configuration, which is computationally expensive, SCOPE uses stage-specific surrogates to train conditional policies. A shared system-level utility function then evaluates the completed plan, allowing the model to learn how upstream choices reshape downstream outcomes.

Performance and Validation

The researchers evaluated SCOPE using real-world operational data from two large-scale supply chains: Dingdong and JD.com. These datasets represent different replenishment echelons, ranging from depot-to-store to regional distribution center (RDC) to front distribution center (FDC) networks.
According to the study, SCOPE consistently outperformed methods that optimize each decision stage separately, as well as common practice-oriented baselines. The results indicate that by learning and coordinating these cross-departmental couplings, the model achieves more effective end-to-end operational planning.

Key Considerations

The authors note that while their framework improves cross-department visibility, it relies on proxy definitions and normalization to handle business quantities that may be unavailable in standard operational datasets. The model is designed to be flexible, allowing for structural reuse where the backbone-and-interface architecture can adapt to different supply-chain topologies by adding specific interfaces. The study emphasizes that this approach is intended to help planners visualize how assortment and service-frequency choices propagate into capacity and routing burdens before those costs materialize in the actual supply chain.