Leveraging Large Language Models for Supply Chain Management Optimization: A Case Study

This research examines the transformative potential of Large Language Models (LLMs) and Generative AI (GAI) in supply chain management (SCM) and operations research (OR). By leveraging advanced Natural Language Capabilities (NLP) capabilities in models such as OpenAI’s GPT-4o, we explore how these technologies can support modeling tasks and streamline complex supply chain problems for optimization steps. Our study specifically focuses on translating mathematical formulations into executable code and interpreting solver outputs. Our work shows that LLMs complement and augment traditional solvers by speeding up the end-to-end process of problem formulation, solution, and interpretation, while also enhancing overall efficiency and reducing the need for manual adjustments. This work systematically identifies the strengths and limitations of LLMs in SCM applications, highlighting their ability to enhance efficiency, accuracy, and decision-making. We conducted a proof-of-concept demonstration using GPT-4o to prepare the model for solving and interpretation of three increasingly complex transportation problems. The results demonstrate that LLMs not only excel at providing error-free code but also exhibit enhanced capabilities in reasoning and interpreting complex outputs from optimization solvers. The proposed framework provides a practical guide for practitioners and researchers in SCM and OR, demonstrating how LLMs can automate and improve optimization tasks.