Agentic AI Framework for Smart Inventory Replenishment

TL;DR

This paper presents an agentic AI framework for retail inventory management that reduces stockouts and costs by integrating demand forecasting, supplier negotiation, and continuous learning, demonstrated through a prototype in a real store setting.

Contribution

The paper introduces a novel agentic AI system combining multiple techniques for smart inventory replenishment, with practical implementation and testing in a retail environment.

Findings

Decreased stockouts in tested store setting

Reduced inventory holding costs

Improved product turnover rate

Abstract

In contemporary retail, the variety of products available (e.g. clothing, groceries, cosmetics, frozen goods) make it difficult to predict the demand, prevent stockouts, and find high-potential products. We suggest an agentic AI model that will be used to monitor the inventory, initiate purchase attempts to the appropriate suppliers, and scan for trending or high-margin products to incorporate. The system applies demand forecasting, supplier selection optimization, multi-agent negotiation and continuous learning. We apply a prototype to a setting in the store of a middle scale mart, test its performance on three conventional and artificial data tables, and compare the results to the base heuristics. Our findings indicate that there is a decrease in stockouts, a reduction of inventory holding costs, and an improvement in product mix turnover. We address constraints, scalability as well as improvement prospect.