Application of Large Language Models (LLM’s) for Supply Chain Optimization

Application of Large Language Models (LLM’s) for Supply Chain Optimization

By Tonmoy Kumar Maiti

Abstract
In today’s interconnected and rapidly evolving global marketplace, businesses are constantly seeking innovative strategies to optimise their supply chains and gain a competitive edge. The rise of large language models (LLMs) and generative artificial intelligence (GenAI) presents a paradigm shift in supply chain management, offering powerful tools to identify and address inefficiencies that may otherwise go undetected.
Supply chain operations traditionally involve a variety of complex decision-making problems. Over the past few decades, advances in information technologies have allowed firms working to optimize their supply chains to move from decision-making on the basis of intuition and experience to more automated and data-driven methods, which has increased efficiency and reduced costs. Unfortunately, business planners and executives still need to expend considerable effort to understand the recommendations coming out of their systems, analyze various scenarios, and conduct what-if analyses. They often need to pull in data science teams or technology providers to explain results or make updates to the system. Now, advances in large language models (LLMs), a type of generative AI, are increasingly making it possible to perform those activities without such support. LLM-based technology can automate data discovery, insight generation, and scenario analysis, reducing the time to make decisions from days to minutes and dramatically increasing planners’ and executives’ productivity and impact.

The insights garnered from this white paper will help both researchers and managers, equipping them to harness the latest advancements in LLM technology and its role within supply chain management.