发表机构
Dingdong（叮咚）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SCOPE是将供应链实体建模为token的复合策略模型，在生鲜零售补货数据上验证了其比分阶段优化及传统基线更优的端到端供应链决策效果。
AI 中文摘要
供应链AI能否从孤立的决策模块转向统一的运营规划？完整的补货计划需明确各地点的产品种类、上游供应设施、补货频率及配送路线，这些决策在运营上相互耦合：选定的品类会改变后续环节的需求与负荷，源分配和补货频率会重塑配送请求，而路线可行性与成本又会决定前期决策的系统价值。然而现代供应链中，这些决策常由不同部门处理并通过独立系统优化，易导致缺货、库存积压及不必要的运输。本文提出SCOPE（Supply-Chain Operations through Coupled Policies for End-to-End Coordination），这是一种复合策略模型，将供应链实体表示为token，通过共享运营表示对其进行上下文建模，并将每种token类型映射到对应决策接口；每个决策基于前期决策形成的部分计划构建，完成的计划则通过共享系统级效用进行评估。我们将该框架应用于城市生鲜零售补货场景，该场景中服务频率、品类、容量压力与路网配送交互性强，并在来自叮咚（Dingdong）及this http URL（两个不同补货层级的大规模供应链）的真实运营数据上进行评估。在两种场景下，SCOPE均显著优于各决策阶段单独优化的方法，以及供应链运营中常用的实践导向基线。这些结果表明，学习并协调跨部门运营耦合关系可实现更有效的端到端供应链决策。
英文摘要
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.
