Large language models improve automated inventory management policies
Automated Design of Inventory Policy with Large Language Models: An Exploratory Study
Artificial Intelligence
Summary
Managing inventory is tricky because businesses need to decide how much to order to avoid running out or having too much. The researchers combined big language models (LLMs) with computer optimization to create and improve inventory rules automatically. Their system tries out new kinds of rules and tweaks them to save costs, getting better over time. They found that mixing the language model with optimization works much better than using the language model alone. The improved rules are easy to understand and work well across many different situations.
inventory policylarge language modelsparameter optimizationlost-sales inventorycost reductionoperational datapolicy classdemand datareplenishment logicinterpretable decision rules
Authors
Fenghua Yang, Preet Baxi, Yi Zhang, Stefanus Jasin, Yanzhe Lei, Mo Liu, Parshan Pakiman
Abstract
Firms making inventory decisions have access to operational data, optimization tools, and large language models (LLMs). Typically, data characterize the operating environment, optimization selects parameters within a prespecified inventory policy class, and LLMs support coding and decision analysis. We develop an integrated framework that combines these resources to automate inventory policy design. Given demand data, the framework iteratively uses an LLM to generate parameterized policy classes and an external solver to optimize its parameters within each class. Across 30 lost-sales inventory instances, the mean cost reduction relative to optimized base-stock benchmarks increases from 17.5% after one generation to 30.0% after ten generations. Parameter optimization is central to this performance: an LLM-only variant performs substantially worse, whereas optimization-guided feedback improves policy quality, accelerates search, and directs the LLM toward better policy classes rather than merely better parameter values within a fixed class. The strongest discovered policies are also interpretable: they combine recognizable inventory-control motifs, including capped orders, discounted or weighted pipeline inventory, and threshold-based replenishment logic. The search thereby produces new policy-class functional forms that, to our knowledge, have not previously been studied in the lost-sales inventory literature. These functional forms are not specified ex ante but emerge from the search process. Moreover, after their parameters are re-optimized, three discovered policy classes achieve average cost reductions of 21.75% to 22.60% across 10,064 new inventory instances. Overall, the results show that data-driven parameter optimization can guide LLM-based search over a broad space of inventory policy classes and identify high-performing, interpretable, and transferable decision rules.