Large Language Models for Supply Chain Optimization

by   Beibin Li, et al.

Supply chain operations traditionally involve a variety of complex decision making problems. Over the last few decades, supply chains greatly benefited from advances in computation, which allowed the transition from manual processing to automation and cost-effective optimization. Nonetheless, business operators still need to spend substantial efforts in explaining and interpreting the optimization outcomes to stakeholders. Motivated by the recent advances in Large Language Models (LLMs), we study how this disruptive technology can help bridge the gap between supply chain automation and human comprehension and trust thereof. We design OptiGuide – a framework that accepts as input queries in plain text, and outputs insights about the underlying optimization outcomes. Our framework does not forgo the state-of-the-art combinatorial optimization technology, but rather leverages it to quantitatively answer what-if scenarios (e.g., how would the cost change if we used supplier B instead of supplier A for a given demand?). Importantly, our design does not require sending proprietary data over to LLMs, which can be a privacy concern in some circumstances. We demonstrate the effectiveness of our framework on a real server placement scenario within Microsoft's cloud supply chain. Along the way, we develop a general evaluation benchmark, which can be used to evaluate the accuracy of the LLM output in other scenarios.


page 2

page 8

page 9

page 10

page 12

page 15

page 30


Will bots take over the supply chain? Revisiting Agent-based supply chain automation

Agent-based systems have the capability to fuse information from many di...

Towards A Sustainable and Ethical Supply Chain Management: The Potential of IoT Solutions

Globalization has introduced many new challenges making Supply chain man...

An Image Processing Pipeline for Automated Packaging Structure Recognition

Dispatching and receiving logistics goods, as well as transportation its...

An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failures

As we increasingly depend on software systems, the consequences of breac...

Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach

Organisations often struggle to identify the causes of change in metrics...

Traceability Technology Adoption in Supply Chain Networks

Modern traceability technologies promise to improve supply chain managem...

Reinforcement Learning for Multi-Truck Vehicle Routing Problems

Vehicle routing problems and other combinatorial optimization problems h...

Please sign up or login with your details

Forgot password? Click here to reset