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Large Language Models for Supply Chain Decisions
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Large Language Models for Supply Chain Decisions
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Supply Chain Management requires addressing a variety of complex decision-making challenges, from sourcing strategies to planning and execution. Over the last few decades, advances in computation and information technologies have enabled the transition from manual, intuition and experience-based decision-making, into more automated and data-driven decisions using a variety of tools that apply optimization techniques. These techniques use mathematical methods to improve decision-making. Unfortunately, business planners and executives still need to spend considerable time and effort to (i) understand and explain the recommendations coming out of these technologies; (ii) analyze various scenarios and answer what-if questions; and (iii) update the mathematical models used in these tools to reflect current business environments. Addressing these challenges requires involving data science teams and/or the technology providers to explain results or make the necessary changes in the technology and hence significantly slows down decision making. Motivated by the recent advances in Large Language Models (LLMs), we report how this disruptive technology can democratize supply chain technology - namely, facilitate the understanding of tools' outcomes, as well as the interaction with supply chain tools without human-in-the-loop. Specifically, we report how we apply LLMs to address the three challenges described above, thus substantially reducing the time to decision from days and weeks to minutes and hours as well as dramatically increasing planners' and executives' productivity and impact.
Forward citations
Cited by 7 Pith papers
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LLM agent translates user prompts into model patches and selects primal-aware re-optimization techniques for large-scale dynamic problems, shown on supply-chain and exam-scheduling cases.
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Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management
Autonomous generative AI agents outperform humans on average in supply-chain simulations but exhibit decision instability termed agent bullwhip, which GRPO-based post-training mitigates.
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Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches
An LLM agent converts user prompts into optimization-model patches and selects primal-based re-optimization methods from a toolbox to produce feasible solutions for dynamic supply-chain and exam-scheduling problems.
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Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management
AI agents in supply chain simulations outperform humans but exhibit decision instability that GRPO post-training reduces.
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GenAI-Driven Approach to RISC-V Supply Chain Exploration
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