# Rethinking Supply Chain Planning: A Generative Paradigm

arXiv: https://arxiv.org/abs/2509.03811 · HTML: https://mlsanity.com/paper/2509.03811/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Large Language Models for Supply Chain Optimization](https://mlsanity.com/paper/2307.03875/) (2023)
- [Large Language Model Enhanced Text-to-SQL Generation: A Survey](https://mlsanity.com/paper/2410.06011/) (2024)
- [InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains](https://mlsanity.com/paper/2407.11384/) (2024)
- [Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking](https://mlsanity.com/paper/2411.10184/) (2024)
- [Large Language Models for Supply Chain Decisions](https://mlsanity.com/paper/2507.21502/) (2025)
- [Towards Autonomous Supply Chains: Definition, Characteristics, Conceptual Framework, and Autonomy Levels](https://mlsanity.com/paper/2401.14183/) (2023)
- [Integrating Large Language Models with Network Optimization for Interactive and Explainable Supply Chain Planning: A Real-World Case Study](https://mlsanity.com/paper/2508.21622/) (2025)

## Built on by (most cited first, 4 of 4)

- [Ask, Clarify, Optimize: Human-LLM Agent Collaboration for Smarter Inventory Control](https://mlsanity.com/paper/2601.00121/) (2025)
- [Agentic AI Sustainability Assessment for Supply Chain Document Insights](https://mlsanity.com/paper/2511.07097/) (2025)
- [LLM Biases](https://mlsanity.com/paper/2604.26960/) (2026)
- [A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics](https://mlsanity.com/paper/2609.13561/) (2026)

## Abstract (arXiv)

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.