mlsanityData to 14 Oct 2026

InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains

arXiv 2407.11384 ↗First posted 16 Jul 2024cs.CLcited by 22

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

Supply chain management (SCM) involves coordinating the flow of goods, information, and finances across various entities to deliver products efficiently. Effective inventory management is crucial in today's volatile and uncertain world. Previous research has demonstrated the superiority of heuristic methods and reinforcement learning applications in inventory management. However, the application of large language models (LLMs) as autonomous agents in multi-agent systems for inventory management remains underexplored. This study introduces a novel approach using LLMs to manage multi-agent inventory systems. Leveraging their zero-shot learning capabilities, our model, InvAgent, enhances resilience and improves efficiency across the supply chain network. Our contributions include utilizing LLMs for zero-shot learning to enable adaptive and informed decision-making without prior training, providing explainability and clarity through chain-of-thought, and demonstrating dynamic adaptability to varying demand scenarios while reducing costs and preventing stockouts. Extensive evaluations across different scenarios highlight the efficiency of our model in SCM.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 7 of its 34 references that are in the map. First posted on arXiv 16 Jul 2024.

Builds on

7 of 34 references.

References · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  3. 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 294
  4. 2020OR-Gym: A Reinforcement Learning Library for Operations Research Problemscited by 51
  5. 2024EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoningcited by 22
  6. 2023An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systemscited by 11
  7. 2023An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failurescited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 10; 2026: 11.

22 papers.

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  1. 2025Do as We Do, Not as You Think: the Conformity of Large Language Modelscited by 53
  2. 2024Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seekingcited by 16
  3. 2025Ask, Clarify, Optimize: Human-LLM Agent Collaboration for Smarter Inventory Controlcited by 5
  4. 2025Rethinking Supply Chain Planning: A Generative Paradigmcited by 4
  5. 2025Leveraging Large Language Models for Risk Assessment in Hyperconnected Logistic Hub Network Deploymentcited by 3
  6. 2025LLMs for Supply Chain Managementcited by 3
  7. 2025AutoBnB-RAG: Enhancing Multi-Agent Incident Response with Retrieval-Augmented Generationcited by 3
  8. 2026Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Managementcited by 3
  9. 2025Training-Free Multimodal Large Language Model Orchestrationcited by 2
  10. 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agentscited by 2
  11. 2025Agentic AI Sustainability Assessment for Supply Chain Document Insightscited by 2
  12. 2026Automating Supply Chain Disruption Monitoring via an Agentic AI Approachcited by 2
10 more
  1. 2026Contract2Plan: Verified Contract-Grounded Retrieval-Augmented Optimization for BOM-Aware Procurement and Multi-Echelon Inventory Planningcited by 1
  2. 2026InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guaranteescited by 1
  3. 2026Helicase: Uncertainty-Guided Supply Chain Knowledge Graph Construction with Autonomous Multi-Agent LLMscited by 1
  4. 2026STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oraclecited by 1
  5. 2026EconSkills: Studying Skill Transfer and Retrieval for Web Agents on Live Economic Datacited by 1
  6. 2025AgentBnB: A Browser-Based Cybersecurity Tabletop Exercise with Large Language Model Support and Retrieval-Aligned Scaffoldingcited by 0
  7. 2026SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulationcited by 0
  8. 2026Semantic Signal-Assisted Inspection and Recovery Allocation in Reverse Logisticscited by 0
  9. 2026A Hybrid Agentic AI Framework for Intelligent Supply Chain Analyticscited by 0
  10. 2026AutoMARS: Automatic and Scalable Multi-Agent Large Language Model Framework for Supply Chain Risk Assessmentcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026AI Agent Systems for Supply Chains: Structured Decision Prompts and Memory Retrieval
  2. 2026AI Agents for Inventory Control: Human-LLM-OR Complementarity
  3. 2025Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization
  4. 2025Integrating Large Language Models with Network Optimization for Interactive and Explainable Supply Chain Planning: A Real-World Case Study
  5. 2025AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager
  6. 2024Zero-shot Generalization in Inventory Management: Train, then Estimate and Decide
  7. 2026SupChain-Bench: Benchmarking Large Language Models for Real-World Supply Chain Management
  8. 2026RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments