SupChain-Bench: Benchmarking Large Language Models for Real-World Supply Chain Management

Shengyue Guan; Yihao Liu; Lang Cao

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arXiv · 2602.07342

SupChain-Bench: Benchmarking Large Language Models for Real-World Supply Chain Management

Shengyue Guan·Yihao Liu·Lang Cao

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Abstract

Large language models (LLMs) have shown promise in complex reasoning and tool-based decision making, motivating their application to real-world supply chain management. However, supply chain workflows require reliable long-horizon, multi-step orchestration grounded in domain-specific procedures, which remains challenging for current models. To systematically evaluate LLM performance in this setting, we introduce SupChain-Bench, a unified real-world benchmark that assesses both supply chain domain knowledge and long-horizon tool-based orchestration grounded in standard operating procedures (SOPs). Our experiments reveal substantial gaps in execution reliability across models. We further propose SupChain-ReAct, an SOP-free framework that autonomously synthesizes executable procedures for tool use, achieving the strongest and most consistent tool-calling performance. Our work establishes a principled benchmark for studying reliable long-horizon orchestration in real-world operational settings and highlights significant room for improvement in LLM-based supply chain agents.

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Shengyue Guan, Yihao Liu, Lang Cao. 2026-02-07. SupChain-Bench: Benchmarking Large Language Models for Real-World Supply Chain Management. https://arxiv.org/abs/2602.07342

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