# Large Language Models for Supply Chain Decisions

arXiv: https://arxiv.org/abs/2507.21502 · HTML: https://mlsanity.com/paper/2507.21502/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 1 of 1)

- [Prediction-Guided Active Experiments](https://mlsanity.com/paper/2411.12036/) (2024)

## Built on by (most cited first, 13 of 13)

- [Assured autonomy: How operations research powers and orchestrates generative AI systems](https://mlsanity.com/paper/2512.23978/) (2025)
- [OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents](https://mlsanity.com/paper/2602.19439/) (2026)
- [Rethinking Supply Chain Planning: A Generative Paradigm](https://mlsanity.com/paper/2509.03811/) (2025)
- [Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition](https://mlsanity.com/paper/2604.05523/) (2026)
- [Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management](https://mlsanity.com/paper/2605.17036/) (2026)
- [Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches](https://mlsanity.com/paper/2605.18692/) (2026)
- [Strategic Buying Agents](https://mlsanity.com/paper/2607.04708/) (2026)
- [Validating Generative Agent-Based Models for Logistics and Supply Chain Management Research](https://mlsanity.com/paper/2508.20234/) (2025)
- [CONDESION-BENCH: Conditional Decision-Making of Large Language Models in Compositional Action Space](https://mlsanity.com/paper/2604.09029/) (2026)
- [Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers](https://mlsanity.com/paper/2604.11507/) (2026)
- [Decisive: Guiding User Decisions with Optimal Preference Elicitation from Unstructured Documents](https://mlsanity.com/paper/2604.18122/) (2026)
- [LLM Biases](https://mlsanity.com/paper/2604.26960/) (2026)
- [GenAI-Driven Approach to RISC-V Supply Chain Exploration](https://mlsanity.com/paper/2605.15223/) (2026)

## Abstract (arXiv)

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.