Supply chain planning is the critical process of anticipating future demand and coordinating operational activities across the logistics network. However, within the context of contemporary e-commerce, traditional planning paradigms, typically characterized by fragmented processes and static optimization, prove inadequate in addressing dynamic demand, organizational silos, and the complexity of multi-stage coordination. To address these challenges, this study proposes a fundamental rethinking of supply chain planning, redefining it not merely as a computational task, but as an interactive, integrated, and automated cognitive process. This new paradigm emphasizes the organic unification of human strategic intent with adaptive execution, shifting the focus from rigid control to continuous, intelligent orchestration. To operationalize this conceptual shift, we introduce a Generative AI-powered agentic framework. Functioning as an intelligent cognitive interface, this framework bridges the gap between unstructured business contexts and structured analytical workflows, enabling the system to comprehend complex semantics and coordinate decisions across organizational boundaries. We demonstrate the empirical validity of this approach within JD.com's large-scale operations. The deployment confirms the efficacy of this cognitive paradigm, yielding an approximate 22% improvement in planning accuracy and a 2% increase in in-stock rates, thereby validating the transformation of planning into an adaptive, knowledge-driven capability.

翻译：供应链规划是预测未来需求并协调物流网络内运营活动的关键流程。然而，在当代电子商务背景下，传统规划范式通常以流程碎片化和静态优化为特征，难以应对动态需求、组织孤岛以及多阶段协调的复杂性。为应对这些挑战，本研究提出对供应链规划进行根本性反思，将其重新定义不仅为一项计算任务，更是一个交互式、集成化、自动化的认知过程。这一新范式强调人类战略意图与自适应执行的有机统一，将焦点从刚性控制转向持续、智能的协同编排。为实现这一概念转变，我们引入了一个基于生成式人工智能的智能体框架。该框架作为智能认知接口，弥合了非结构化业务情境与结构化分析工作流之间的鸿沟，使系统能够理解复杂语义并协调跨组织边界的决策。我们在京东的大规模运营中验证了该方法的实证有效性。部署结果证实了这一认知范式的效能，规划准确率提升了约22%，现货率提高了2%，从而验证了规划向自适应、知识驱动能力的转型。