SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulation

2026-06-23

AI Summary

This work proposes a novel approach to supply chain simulation that integrates contextual graph structures with multi-agent simulation, addressing the limitations of traditional systems that rely on abstract data and fail to support active exploration of complex dynamics. For the first time, it combines a large language model–driven multi-agent framework, an interactive network visualization, branching timelines, and a task-oriented analytical console to construct an actionable decision space and a structured feedback mechanism. User studies demonstrate that this integrated system significantly enhances learner engagement and deepens understanding of supply chain dynamics.

Abstract

Simulation has long supported supply chain management instruction by letting learners observe network behavior and test decision strategies. Recent progress in LLM-driven agents opens new possibilities for richer, more adaptive simulations, but many existing systems still present abstract, opaque data that overwhelms learners and discourages active exploration. We introduce \textit{SupplyNet}, a gamified visual simulation system built on a contextual graph-based LLM multi-agent framework that models interdependent supply chain dynamics and provides responsive feedback through tiered challenges. \textit{SupplyNet} turns the simulation into a manipulable decision space by integrating an interactive network view of system state, a branching timeline for "what-if" exploration and comparison, and a task-oriented analysis console for structured performance breakdowns. Together, these visual components support counterfactual exploration, causal tracing, and comparative reasoning about outcomes. A user study suggests that \textit{SupplyNet} increases engagement and supports users' perceived understanding of supply chain dynamics, highlighting the potential of pairing contextual multi-agent simulation with visualization to advance operational comprehension.

Problem

• supply chain simulation
• visual exploratory learning
• multi-agent simulation
• learner engagement
• decision support

Innovation

• contextual multi-agent simulation
• LLM-driven agents
• visual exploratory learning
• interactive network visualization
• what-if analysis