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 \emph{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 \name{} -- 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.

通过 Large Language Models（LLMs）的最新进展，研究如何将这一颠覆性技术应用于供应链自动化，并解决人类理解和信任之间的间隙。设计了一个名为
ame {} 的框架，通过输入纯文本查询并输出有关底层优化结果的见解。同时，我们开发了一个通用的评估基准，用于评估 LLM 在其他情景中的准确性。