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This paper introduces a framework integrating network optimization with LLMs to enhance supply chain planning by providing interactive and explainable decision support. The system uses a mixed-integer programming model for tactical inventory redistribution across a distribution network, coupled with LLM-generated natural language summaries and visualizations. A case study demonstrates improved planning outcomes through stockout prevention, cost reduction, and service level maintenance.
Stop wrestling with inscrutable optimization outputs: this system uses LLMs to translate complex supply chain plans into plain English, tailored KPIs, and interactive visualizations.
This paper presents an integrated framework that combines traditional network optimization models with large language models (LLMs) to deliver interactive, explainable, and role-aware decision support for supply chain planning. The proposed system bridges the gap between complex operations research outputs and business stakeholder understanding by generating natural language summaries, contextual visualizations, and tailored key performance indicators (KPIs). The core optimization model addresses tactical inventory redistribution across a network of distribution centers for multi-period and multi-item, using a mixed-integer formulation. The technical architecture incorporates AI agents, RESTful APIs, and a dynamic user interface to support real-time interaction, configuration updates, and simulation-based insights. A case study demonstrates how the system improves planning outcomes by preventing stockouts, reducing costs, and maintaining service levels. Future extensions include integrating private LLMs, transfer learning, reinforcement learning, and Bayesian neural networks to enhance explainability, adaptability, and real-time decision-making.
