Summary of Agentic Llms in the Supply Chain: Towards Autonomous Multi-agent Consensus-seeking, by Valeria Jannelli et al.
Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking
by Valeria Jannelli, Stefan Schoepf, Matthias Bickel, Torbjørn Netland, Alexandra Brintrup
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores how Large Language Models (LLMs) can automate consensus-seeking in supply chain management (SCM), overcoming challenges in traditional human-based decision-making. Existing solutions have faced limitations due to high entry barriers, limited capabilities, and adaptability issues. Recent advances in Generative AI, particularly LLMs, show promise in addressing these gaps by negotiating, reasoning, and planning at scale with minimal entry barriers. The paper identifies key limitations and proposes autonomous LLM agents, introducing novel consensus-seeking frameworks tailored for LLMs. A case study in inventory management validates the effectiveness of this approach. To accelerate progress, the code is open-sourced, providing a foundation for further advancements in LLM-powered supply chain solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines can help make decisions in supply chains. Right now, people have to agree on things like how much inventory to keep and when to deliver products. This can be slow and costly. Researchers found that some methods for making these decisions automatically have problems because they’re hard to use or not very good at handling complex situations. But newer technology called Large Language Models (LLMs) might be able to help. LLMs are trained on lots of data and can think like humans, but faster and more efficiently. The paper shows how LLMs could make decisions in supply chains, making it easier for small companies to use this kind of automation. |
