供應鏈優化的大型語言模型

摘要

傳統上，供應鏈運營涉及各種複雜的決策問題。在過去幾十年裡，供應鏈極大地受益於計算技術的進步，從手動處理過渡到自動化和成本效益優化。然而，企業運營者仍然需要花費大量精力來解釋和解讀優化結果以便與利益相關者溝通。受到最近大型語言模型（LLMs）技術的推動，我們研究這種顛覆性技術如何幫助橋接供應鏈自動化與人類理解和信任之間的差距。我們設計了一個框架，該框架接受純文本查詢作為輸入，並輸出有關基礑優化結果的見解。我們的框架並未放棄最先進的組合優化技術，而是利用它來定量回答假設情境（例如，如果我們對於特定需求使用供應商B而不是供應商A，成本將如何變化？）。重要的是，我們的設計不需要將專有數據傳送給LLMs，這在某些情況下可能涉及隱私問題。我們在微軟雲供應鏈內的真實伺服器放置情境中展示了我們框架的有效性。在此過程中，我們開發了一個通用的評估基準，可用於評估LLM輸出在其他情境中的準確性。

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.

供應鏈優化的大型語言模型

Large Language Models for Supply Chain Optimization

摘要

Support