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

● high · 2026-06-24intelligent-tutoring, llm, generative-ai, active-learning, professional-training, simulation-based-learning, visualization

SupplyNet: LLM Multi-Agent Simulation for Supply Chain Education

SupplyNet is a gamified visual simulation system that uses a contextual graph-based LLM multi-agent framework to model interdependent supply chain dynamics. Designed for Professional Training in supply chain management (SCM), it replaces traditional abstract simulations with a manipulable decision space combining an interactive network view, a branching timeline for "what-if" exploration, and a task-oriented analysis console.

Key Features

LLM-driven agents model realistic supplier, manufacturer, distributor, and retailer behaviors, responding adaptively to learner decisions. This moves beyond scripted simulation scenarios to generate emergent, context-sensitive dynamics.

Visual exploratory learning is supported through three integrated components: an interactive network view showing real-time system state, a branching timeline enabling counterfactual comparison, and a task-oriented console for structured performance breakdowns. Together these support causal tracing and comparative reasoning.

User study results suggest SupplyNet increases engagement and supports perceived understanding of supply chain dynamics, demonstrating the potential of pairing contextual multi-agent simulation with visualization for Active Learning in operational domains.

Implications for AI in Education

SupplyNet represents a novel application of Intelligent Tutoring principles beyond traditional academic subjects into professional education. The system's use of Generative AI agents to create adaptive, responsive simulation environments points toward broader applications in STEM Education and professional training where complex systems understanding is required.

Connected Concepts

  • LLM
  • Professional Training
  • Active Learning
  • Intelligent Tutoring
  • Generative AI
  • STEM Education

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    • Citation

    Li, Y., Han, K. K., Hu, T., Cao, Y.-F., Qu, H., & Song, S. (2026). SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulation. arXiv:2606.24694.