从供应链网络推断库存动态：一种具有自主验证的图学习方法
Inferring Inventory Dynamics from Supply Chain Networks: A Graph Learning Approach with Autonomous Validation
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中文总结 AI 辅助
针对中小企业库存数据稀缺致预测难问题，提出多智能体半监督推理框架，构建图机器学习模型结合多个计量模型，能从供应链网络推断库存动态，有效验证预测，且与经济理论相符，解决无真实观测时的预测验证难题。
中文摘要 AI 辅助
供需不匹配是供应链管理中的根本挑战，对中小企业而言直接测量尤其困难，因其缺乏系统库存记录，标注训练数据稀缺。传统监督学习方法依赖标注样本，难以在数据稀缺时可靠验证企业层面预测。为此，我们开发了多智能体半监督推理框架，将标签稀缺问题重构为跨专业智能体的结构化协作任务。先构建受生产函数约束的图机器学习模型从供应链网络拓扑推断企业层面库存变化，再由计量验证智能体加载五个计量模型生成结构化经济证据，专家审查智能体综合证据并解决跨智能体不一致性以产生统一一致性评估。实证结果表明在库存变化预测任务上有稳定预测性能，多智能体计量验证还证实预测库存动态在因果结构和网络传输机制上与经济理论相符，该框架即使在无真实观测时也能有效进行预测验证。
英文摘要
Supply-demand mismatch represents a fundamental challenge in supply chain management, yet its direct measurement remains particularly elusive for small and medium-sized enterprises (SMEs).These firms typically lack systematic inventory records, leaving labeled training data critically scarce. Conventional supervised learning methods rely heavily on labeled samples, rendering them ill-equipped to reliably validate firm-level predictions under such data-scarce conditions. To resolve this unlabeled-data dilemma, we develop a multi-agent semi-supervised inference framework that reframes the label-scarcity problem as a structured, collaborative task distributed across specialized agents. We first construct a production-function-constrained graph machine learning model that infers firm-level inventory changes directly from supply chain network topology. A dedicated econometric validation agent then concurrently loads five econometric models (spanning spatial spillovers, dynamic persistence, causal direction, shock transmission, and supply-demand forecasting) to generates structured economic evidence from complementary dimensions. An expert review agent synthesizes the structured econometric evidence and produces a unified consistency assessment by resolving cross-agent inconsistencies. Empirical results demonstrate stable predictive performance on inventory-change forecasting tasks. Multi-agent econometric validation further confirms that predicted inventory dynamics align closely with established economic theory in terms of causal structure and network transmission mechanisms. Critically, the proposed agent framework enables effective prediction verification even when ground-truth observations are unavailable.
