Large Language Models for Supply Chain Optimization
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.
64acb03c1aee69ece03b06ed
Beibin Li
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2023-07-12T07:29:57.012Z
639a48fe327d97c59f141535
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Beibin Li
user
beibin79
64acb03c1aee69ece03b06ee
Konstantina Mellou
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2023-07-12T07:29:53.326Z
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Konstantina Mellou
user
kmellou
64acb03c1aee69ece03b06ef
Bo Zhang
64acb03c1aee69ece03b06f0
Jeevan Pathuri
64acb03c1aee69ece03b06f1
Ishai Menache
Large Language Models for Supply Chain Optimization
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.
A framework leveraging Large Language Models to provide interpretable insights into supply chain optimization outcomes without sharing proprietary data.
Large Language Models combinatorial optimization what-if scenarios server placement evaluation benchmark