Leveraging LLM-Based Agents for Intelligent Supply Chain Planning

• URL: http://arxiv.org/abs/2509.03811v1
• Date: Thu, 04 Sep 2025 01:55:58 GMT
• Title: Leveraging LLM-Based Agents for Intelligent Supply Chain Planning
• Authors: Yongzhi Qi, Jiaheng Yin, Jianshen Zhang, Dongyang Geng, Zhengyu Chen, Hao Hu, Wei Qi, Zuo-Jun Max Shen,
• Abstract summary: Supply Chain Planning Agent (SCPA) is a framework that can understand domain knowledge, comprehend the operator's needs, decompose tasks, leverage or create new tools.<n>We deploy this framework in JD.com's real-world scenario, demonstrating the feasibility of LLM-agent applications in the supply chain.
• Score: 8.222411952670987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In supply chain management, planning is a critical concept. The movement of physical products across different categories, from suppliers to warehouse management, to sales, and logistics transporting them to customers, entails the involvement of many entities. It covers various aspects such as demand forecasting, inventory management, sales operations, and replenishment. How to collect relevant data from an e-commerce platform's perspective, formulate long-term plans, and dynamically adjust them based on environmental changes, while ensuring interpretability, efficiency, and reliability, is a practical and challenging problem. In recent years, the development of AI technologies, especially the rapid progress of large language models, has provided new tools to address real-world issues. In this work, we construct a Supply Chain Planning Agent (SCPA) framework that can understand domain knowledge, comprehend the operator's needs, decompose tasks, leverage or create new tools, and return evidence-based planning reports. We deploy this framework in JD.com's real-world scenario, demonstrating the feasibility of LLM-agent applications in the supply chain. It effectively reduced labor and improved accuracy, stock availability, and other key metrics.

Related papers

• Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs [66.63911043019294]
  Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them.<n>This paper focuses on the use of LLM techniques to prepare data for diverse downstream tasks.<n>We introduce a task-centric taxonomy that organizes the field into three major tasks: data cleaning, standardization, error processing, imputation, data integration, and data enrichment.
  arXiv  Detail & Related papers  (2026-01-22T12:02:45Z)
• Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects [57.53024716739594]
  Graph-augmented LLM Agents (GLA) enhance structure, continuity, and coordination in complex agent systems.<n>This paper offers a timely and comprehensive overview of recent advances and highlights key directions for future work.<n>We hope this paper can serve as a roadmap for future research on GLA and foster a deeper understanding of the role of graphs in GLA agent systems.
  arXiv  Detail & Related papers  (2025-07-29T00:27:12Z)
• Reinforcement Learning for Autonomous Warehouse Orchestration in SAP Logistics Execution: Redefining Supply Chain Agility [0.0]
  This research introduces a pioneering framework leveraging reinforcement learning to autonomously orchestrate warehouse tasks in SAP Logistics Execution.<n>A synthetic dataset of 300,000 LE transactions simulates real-world warehouse scenarios, including multilingual data and operational disruptions.<n>The analysis achieves 95% task optimization accuracy, reducing processing times by 60% compared to traditional methods.
  arXiv  Detail & Related papers  (2025-06-06T20:34:27Z)
• Large Language Models integration in Smart Grids [0.0]
  Large Language Models (LLMs) are changing the way we operate our society and will undoubtedly impact power systems as well.<n>This paper provides a comprehensive analysis of 30 real-world applications across eight key categories.<n>Critical technical hurdles, such as data privacy and model reliability, are examined, along with possible solutions.
  arXiv  Detail & Related papers  (2025-04-12T03:29:30Z)
• Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap [51.198001060683296]
  Large Language Models (LLMs) offer transformative potential to address transportation challenges.<n>This survey first presents LLM4TR, a novel conceptual framework that systematically categorizes the roles of LLMs in transportation.<n>For each role, our review spans diverse applications, from traffic prediction and autonomous driving to safety analytics and urban mobility optimization.
  arXiv  Detail & Related papers  (2025-03-27T11:56:27Z)
• The Potential of Large Language Models in Supply Chain Management: Advancing Decision-Making, Efficiency, and Innovation [0.5497663232622965]
  The integration of large language models (LLMs) into supply chain management (SCM) is revolutionizing the industry.<n>This white paper explores the transformative impact of LLMs on various SCM functions, including demand forecasting, inventory management, supplier relationship management, and logistics optimization.<n>Ethical considerations, including bias mitigation and data protection, are taken into account to ensure fair and transparent AI practices.
  arXiv  Detail & Related papers  (2025-01-26T05:41:50Z)
• Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models [49.898152180805454]
  This paper presents a novel framework leveraging Knowledge Graphs (KGs) and Large Language Models (LLMs) to enhance supply chain visibility. Our zero-shot, LLM-driven approach automates the extraction of supply chain information from diverse public sources. With high accuracy in NER and RE tasks, it provides an effective tool for understanding complex, multi-tiered supply networks.
  arXiv  Detail & Related papers  (2024-08-05T17:11:29Z)
• InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains [0.0]
  This study introduces a novel approach using large language models (LLMs) to manage multi-agent inventory systems.<n>Our model, InvAgent, enhances resilience and improves efficiency across the supply chain network.
  arXiv  Detail & Related papers  (2024-07-16T04:55:17Z)
• Cooperative Multi-Agent Reinforcement Learning for Inventory Management [0.5276232626689566]
  Reinforcement Learning (RL) for inventory management is a nascent field of research. We present a system with a custom GPU-parallelized environment that consists of one warehouse and multiple stores. We achieve a system that outperforms standard inventory control policies.
  arXiv  Detail & Related papers  (2023-04-18T06:55:59Z)
• Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers [41.293077032127904]
  We consider a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle is how these worker agents must coordinate their movement and actions in the warehouse to maximise performance in this task. We develop hierarchical MARL algorithms in which a manager agent assigns goals to worker agents, and the policies of the manager and workers are co-trained toward maximising a global objective.
  arXiv  Detail & Related papers  (2022-12-22T06:18:41Z)
• Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes [78.120734120667]
  This paper describes an agent-based decentralized and integrated scheduling approach. Part of the requirements is to develop a linearly scaling communication architecture. The approach is explained using an example based on industrial requirements.
  arXiv  Detail & Related papers  (2022-05-06T18:44:29Z)
• Will bots take over the supply chain? Revisiting Agent-based supply chain automation [71.77396882936951]
  Agent-based supply chains have been proposed since early 2000; industrial uptake has been lagging. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains are filling in gaps. For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation.
  arXiv  Detail & Related papers  (2021-09-03T18:44:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.