SCSimulator: An Exploratory Visual Analytics Framework for Partner Selection in Supply Chains through LLM-driven Multi-Agent Simulation

Executive Summary

Supply chains (SCs), complex networks spanning from raw material acquisition to product delivery, play a pivotal role in organizational success. Optimizing SCs remains challenging, particularly in partner selection, a key bottleneck shaped by competitive and cooperative dynamics. This challenge constitutes a multi-objective dynamic game requiring a synergistic integration of Multi-Criteria Decision-Making (MCDM) and Game Theory (GT). Traditional approaches often fail to capture real-world intricacies and risk introducing subjective biases. Multi-agent simulation (MAS) offers promise, but prior research has largely relied on fixed, uniform agent logic, limiting practical applicability. Recent advances in Large Language Models (LLMs) create new opportunities to represent complex SC requirements and hybrid game logic. However, challenges persist in modeling dynamic SC relationships, ensuring interpretability, and balancing agent autonomy with expert control. SCSimulator integrates LLM-driven MAS with human-in-the-loop collaboration for SC partner selection, simulating SC evolution via adaptive network structures and enterprise behaviors. By combining Chain-of-Thought (CoT) reasoning with explainable AI (XAI) techniques, the framework generates multi-faceted, transparent explanations of decision trade-offs. Users can iteratively adjust simulation settings to explore outcomes aligned with their expectations and strategic priorities.