用统一模型协同优化供应链各环节决策，提升整体效率。
SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination
- 将供应链各角色建模为可交互的智能体，通过共享表征实现端到端协同决策。
- 在叮咚买菜和京东的实际数据上，相比独立优化各环节，库存缺货率降低23%以上。
- 适合需要跨部门协同优化的零售、物流等复杂供应链场景。
供应链智能能否超越孤立的决策模块，实现一体化运营规划？一个完整的补货计划需明确各网点的商品组合、上游供应来源、补货频率及配送路径。这些决策相互耦合：商品选择影响后续需求与负载；供应分配和补货频率改变运输请求；而路线可行性和成本反过来决定前期决策的系统价值。然而在现代供应链中，这些决策常由不同部门分别处理，并通过独立系统优化，导致缺货、库存积压和不必要的运输成本。我们提出SCOPE：基于联合策略的端到端供应链协同框架，将供应链实体建模为令牌，通过共享运营表示进行上下文编码，并将每类令牌映射到对应决策接口。每个决策基于前序部分计划生成，完整计划通过共享系统级效用评估。我们在城市生鲜零售补货场景中实现该框架，此处服务频次、商品组合、容量压力与道路网络路由强耦合。基于叮咚买菜与京东两个大规模供应链的真实运营数据（分处不同补货层级）进行评估。在两种场景下，SCOPE均持续优于单独优化各阶段的方法，以及供应链实践中常用的基准方法。结果表明，学习并协调跨部门操作耦合关系，可带来更有效的端到端供应链决策。
原文摘要 · Abstract (English)
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.
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