How Generative AI Improves Supply Chain Management

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 authors draw from Microsoft’s cloud business experience to explore how LLMs can be used to optimize supply chains. They also identify obstacles firms will need to overcome to deploy LLMs effectively.