International Journal of Scientific Research and Engineering Development

Supply chain management is a fundamental aspect of modern business operations, ensuring the efficient flow of goods, information, and resources from suppliers to consumers. Traditional supply chain systems often face challenges such as inaccurate demand forecasting, inefficient inventory management, and lack of real-time insights. This paper presents an AI-Based Supply Chain Management System (SupplyAI) designed to address these issues using data-driven techniques. The proposed system utilizes structured datasets and machine learning-based analytical methods to predict demand patterns and optimize inventory levels. The system is implemented using Python and integrates data processing, visualization, and prediction modules. Experimental results demonstrate improved forecasting accuracy, reduced inventory imbalance, and enhanced decision-making capabilities. The system is scalable, cost-effective, and particularly beneficial for small and medium enterprises aiming to modernize their supply chain operations.

Shifa Bilal Tamboli, Simeen Phiroj Mulani, Arman Tajuddin Shiakh,"AI-Based Supply Chain Management System (SupplyAI)" International Journal of Scientific Research and Engineering Development, V9(2): Page(2355-2358) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.