Will bots take over the supply chain? Revisiting Agent-based supply chain automation
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Abstract (arXiv)
Agent-based systems have the capability to fuse information from many distributed sources and create better plans faster. This feature makes agent-based systems naturally suitable to address the challenges in Supply Chain Management (SCM). Although agent-based supply chains systems have been proposed since early 2000; industrial uptake of them has been lagging. The reasons quoted include the immaturity of the technology, a lack of interoperability with supply chain information systems, and a lack of trust in Artificial Intelligence (AI). In this paper, we revisit the agent-based supply chain and review the state of the art. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains; are filling in gaps, leaving the concept applicable to a wider range of functions. 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. Digital ledgers help securely transfer data between third parties, making agent-based information sharing possible, without the need to integrate Enterprise Resource Planning (ERP) systems. Learning functionality in agents enables agents to move beyond automation and towards autonomy. We note this convergence effect through conceptualising an agent-based supply chain framework, reviewing its components, and highlighting research challenges that need to be addressed in moving forward.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 1 of its 161 references that are in the map. First posted on arXiv 3 Sep 2021.
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1 of 161 references.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 4.
7 papers.
- 2025From Glue-Code to Protocols: A Critical Analysis of A2A and MCP Integration for Scalable Agent Systemscited by 14
- 2023Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approachcited by 6
- 2023Towards Autonomous Supply Chains: Definition, Characteristics, Conceptual Framework, and Autonomy Levelscited by 3
- 2026Helicase: Uncertainty-Guided Supply Chain Knowledge Graph Construction with Autonomous Multi-Agent LLMscited by 1
- 2026LLM Agents for Combinatorial Efficient Frontiers: Investment Portfolio Optimizationcited by 0
- 2026Agentic AI Autonomy Assessment: A Decision-Support Framework Towards Governed Supply Chain Systemscited by 0
- 2026A Hybrid Agentic AI Framework for Intelligent Supply Chain Analyticscited by 0
- 2026A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics11 Sep 2026
- 2026Agentic AI Autonomy Assessment: A Decision-Support Framework Towards Governed Supply Chain Systems28 Jul 2026
- 2026Helicase: Uncertainty-Guided Supply Chain Knowledge Graph Construction with Autonomous Multi-Agent LLMs26 May 2026
- 2026LLM Agents for Combinatorial Efficient Frontiers: Investment Portfolio Optimization2 Jan 2026
- 2025From Glue-Code to Protocols: A Critical Analysis of A2A and MCP Integration for Scalable Agent Systems6 May 2025
- 2023Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach26 Oct 2023
- 2023Towards Autonomous Supply Chains: Definition, Characteristics, Conceptual Framework, and Autonomy Levels13 Oct 2023