Agentic AI Framework for Smart Inventory Replenishment

• URL: http://arxiv.org/abs/2511.23366v1
• Date: Fri, 28 Nov 2025 17:14:13 GMT
• Title: Agentic AI Framework for Smart Inventory Replenishment
• Authors: Toqeer Ali Syed, Salman Jan, Gohar Ali, Ali Akarma, Ahmad Ali, Qurat-ul-Ain Mastoi,
• Abstract summary: In contemporary retail, the variety of products makes it difficult to predict the demand, prevent stockouts, and find high-potential products.<n>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.
• Score: 0.6670498055582529
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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