Rethinking Supply Chain Planning: A Generative Paradigm

TL;DR

The paper addresses the inefficiencies of traditional, siloed supply chain planning in large-scale e-commerce. It proposes a Generative AI–powered agentic framework that acts as an intelligent organizational interface, combining semantic understanding, autonomous task decomposition, and iterative reasoning to orchestrate planning across vertical and horizontal boundaries. Through a field deployment at JD.com, the approach yields actionable gains: planning accuracy improves by $22\%$, in-stock rates rise by $2\%$, and weekly data-processing time decreases by about $40\%$, illustrating tangible operational value in high-velocity environments. The work reframes planning as a dynamic, knowledge-driven co-evolution of human and machine decision-making, enabling continuous optimization, resilience, and cross-functional coordination. This paradigm sets the stage for broader adoption of reasoning-enhanced foundation models and multi-agent orchestration across the supply chain landscape.

Abstract

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.

Figures (6)

• Figure 1: Workflow and Interconnections of Supply Chain Planning.
• Figure 2: The proposed agentic framework.
• Figure 3: Domain knowledge and data generation.
• Figure 4: Query enhancement.
• Figure 5: Prompt for Task Orchestration Agent