David Simchi-Levi, Professor, MIT and Purdue University, NAE Member

Supply Chain and Manufacturing Management require addressing a variety of complex decision-making challenges, from sourcing strategies to planning and execution all the way to detecting anomalies in manufacturing processes. Over the last few years, advances in computation and information technologies have enabled the transition from manual, intuition and experience-based decision-making, into more automated and data-driven decisions using a variety of tools that apply various analytical techniques.

Unfortunately, business planners and executives still need to spend considerable time and effort to (i) understand and explain the recommendations coming out of these technologies; (ii) analyze various scenarios and answer what-if questions; and (iii) update the mathematical models used in these tools to reflect current business environments. Addressing these challenges requires involving data science teams and/or the technology providers to explain results or make the necessary changes in the technology and hence significantly slows down decision making.

Motivated by the recent advances in Large Language Models (LLMs), we report how this disruptive technology can democratize manufacturing and supply chain technology – namely, facilitate the understanding of tools’ outcomes, as well as the interaction with tools without human-in-the-loop. Specifically, we report how we apply LLMs to address the three challenges described above, thus substantially reducing the time to decision from days and weeks to minutes and hours as well as dramatically increasing planners’ and executives’ productivity and impact.