Integrating large language models to design smarter inventory policies
Automated Design of Inventory Policy with Large Language Models: An Exploratory Study
Artificial Intelligence
Summary
Inventory management is tricky because companies must decide how much to order without wasting money or running out of stock. The authors created a method that uses large language models (LLMs) to suggest new inventory strategies, then uses optimization tools to fine-tune these strategies. Their approach found better-performing and easy-to-understand ordering rules that saved costs in many test cases. This method also generated some new types of inventory rules that hadn’t been explored before.
What this means in practice
- •For inventory planners: Automate creation of improved ordering strategies that reduce costs and adapt across diverse demand scenarios.
- •For supply chain software developers: Incorporate LLM-guided policy search combined with parameter optimization to enhance inventory decision-making algorithms.
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.