Abstract: Small and medium-sized enterprises (SMEs) often face challenges in managing inventory due to fluctuating demand, limited analytical resources, and manual tracking systems. These inefficiencies lead to frequent stockouts, overstocking, and financial losses. This paper presents an AI-driven inventory management system designed to leverage machine learning for demand forecasting, automate replenishment, and optimize stock levels. The proposed system integrates predictive analytics with real-time inventory tracking to support data-driven decision-making. Preliminary results demonstrate improved forecasting accuracy and enhanced operational efficiency, contributing to sustainable and intelligent business management.
Keywords: AI-driven inventory management, demand forecasting, machine learning, real-time tracking
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DOI:
10.17148/IJARCCE.2025.141027
[1] Ms. Sneha Bankar, Amit Shinde, Tejas Yewankar, Aditya Almale, Tejas Patil, "AI-Driven Inventory Predictor for Small Businesses," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.141027
