Agentic AI Framework for Smart Inventory Replenishment
This addresses inventory optimization for retail businesses, but it appears incremental as it builds on existing methods like demand forecasting and multi-agent negotiation.
The paper tackles inventory management challenges in retail by proposing an agentic AI framework that monitors inventory, initiates purchases, and identifies high-potential products, resulting in decreased stockouts, reduced holding costs, and improved product turnover.
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.