供应链优化的大型语言模型

摘要

传统上，供应链运营涉及各种复杂的决策问题。在过去几十年里，供应链从计算方面获益匪浅，这使得从手工处理向自动化和成本效益优化的过渡成为可能。然而，业务运营者仍然需要花费大量精力向利益相关者解释和解读优化结果。受最近大型语言模型（LLMs）的进展启发，我们研究了这种颠覆性技术如何帮助弥合供应链自动化与人类理解和信任之间的鸿沟。我们设计了一个框架，接受纯文本查询作为输入，并输出有关潜在优化结果的见解。我们的框架并未放弃最先进的组合优化技术，而是利用它定量地回答假设情景（例如，如果我们针对特定需求使用供应商B而不是供应商A，成本会如何变化？）。重要的是，我们的设计不需要在某些情况下向LLMs发送专有数据，这可能会引发隐私问题。我们在微软云供应链中的一个真实服务器放置场景上展示了我们框架的有效性。在此过程中，我们开发了一个通用的评估基准，可用于评估LLM输出在其他场景中的准确性。

English

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 -- 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.

供应链优化的大型语言模型

Large Language Models for Supply Chain Optimization

摘要

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