title: Large Language Models for Supply Chain Optimization
summary: 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.
paper_authors._id: 64acb03c1aee69ece03b06ed
paper_authors.name: Beibin Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-12T07:29:57.012Z
paper_authors._id: 639a48fe327d97c59f141535
paper_authors.avatarUrl: /avatars/8d381f7f450eb961b4fe792ec72d5bd8.svg
paper_authors.fullname: Beibin Li
paper_authors.type: user
paper_authors.user: beibin79
paper_authors._id: 64acb03c1aee69ece03b06ee
paper_authors.name: Konstantina Mellou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-12T07:29:53.326Z
paper_authors._id: 64adc7a5c29542a745c0eed3
paper_authors.avatarUrl: /avatars/bad6f1fc6a8d97edb54f565c83870e2e.svg
paper_authors.fullname: Konstantina Mellou
paper_authors.type: user
paper_authors.user: kmellou
paper_authors._id: 64acb03c1aee69ece03b06ef
paper_authors.name: Bo Zhang
paper_authors._id: 64acb03c1aee69ece03b06f0
paper_authors.name: Jeevan Pathuri
paper_authors._id: 64acb03c1aee69ece03b06f1
paper_authors.name: Ishai Menache
paper_title: Large Language Models for Supply Chain Optimization
paper_summary: 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.