AI-Powered Inventory Management with Predictive Analytics and Peer-to-Peer Exchange

Author(s)	Sandhosh R, Mohan K, Periyasamy M, Sanjaikanthan J, Thilagavathi T
Country	India
Abstract	Conventional inventory systems deployed across multi-hub retail and distribution networks operate reactively, lacking the foresight needed to detect dead stock early, enforce dynamic pricing strategies, or redistribute surplus goods between locations—ultimately leading to considerable financial losses from product expiry. To overcome these shortcomings, this work proposes HubStock, a full-stack AI-driven inventory management platform that unifies ensemble machine learning with a structured peer-to-peer exchange framework. The platform was developed using Spring Boot 3.1.5, incorporating a purpose-built Random Forest classifier comprising 100 decision trees, a profit-aware tiered discount engine, and an Atomicity, Consistency, Isolation, and Durability (ACID)-compliant six-state exchange mechanism. Experimental evaluation yielded a classification accuracy of 87.5%, a 75% decrease in expiry-related losses, and a monthly revenue recovery exceeding Rs. 45,000 per hub.
Keywords	Inventory Management, Random Forest, Predictive Analytics, Peer-to-Peer Exchange, Explainable AI
Field	Computer Applications
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-16

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