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

LLMs can reduce human bias in supply chain decisions: The framework uses AI reasoning to move beyond "managerial heuristics" and "subjective biases" that plague traditional partner selection methods, offering more objective analysis of complex trade-offs between competing business goals.

Human oversight remains essential for AI-driven supply chain optimization: SCSimulator implements "human-in-the-loop collaboration," allowing experts to iteratively adjust and validate AI simulations rather than accepting automated recommendations blindly, balancing AI capabilities with human judgment.

Transparency in AI decision-making is achievable for complex business problems: The system combines "Chain-of-Thought reasoning with explainable AI techniques" to generate "multi-faceted, transparent explanations" of why specific supply chain partners are recommended, making AI recommendations understandable to decision-makers.

Simulating realistic agent behavior improves AI reliability: Previous supply chain AI systems used "fixed, uniform agent logic," but this framework uses LLMs to model "adaptive" and realistic enterprise behaviors, better capturing how actual business partners actually behave in competitive and cooperative situations.

Visual interfaces help non-technical users understand complex AI simulations: The framework uses "interpretable interfaces" and visualization to help supply chain managers engage with multi-agent simulations without requiring technical expertise, democratizing access to sophisticated optimization tools.