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.

翻译：供应链运营传统上涉及多种复杂的决策问题。在过去几十年中，供应链得益于计算技术的进步，实现了从人工处理到自动化及成本效益优化的转变。尽管如此，业务运营者仍需投入大量精力向利益相关者解释和解读优化结果。受大语言模型（LLMs）最新进展的启发，我们研究这一颠覆性技术如何帮助弥合供应链自动化与人类理解及信任之间的鸿沟。我们设计了一种名为\name{}的框架——该框架接受纯文本查询作为输入，并输出关于底层优化结果的洞察。我们的框架并未摒弃最先进的组合优化技术，而是利用其定量回答假设场景问题（例如，若针对特定需求改用供应商B而非供应商A，成本将如何变化？）。重要的是，我们的设计无需将专有数据发送至LLMs，这在某些情况下可能引发隐私担忧。我们在微软云供应链的真实服务器部署场景中验证了框架的有效性。在此过程中，我们开发了一个通用评估基准，可用于评估其他场景下LLM输出的准确性。