# Integrating Large Language Models with Network Optimization for Interactive and Explainable Supply Chain Planning: A Real-World Case Study

## TL;DR

This paper introduces a novel framework combining large language models with network optimization to enhance supply chain planning through interactive, explainable decision support, demonstrated via a real-world case study.

## Contribution

It presents an integrated system that merges optimization models with LLMs for improved stakeholder communication and decision-making in supply chain management.

## Key findings

- Improved planning outcomes by preventing stockouts
- Reduced costs and maintained service levels
- Enhanced stakeholder understanding through natural language explanations

## Abstract

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.