Artificial Intelligence-Driven Inventory Management: Optimizing Stock Levels and Reducing Costs Through Advanced Machine Learning Techniques
Year: 2024
Type: article
Abstract: This study investigates the implementation of artificial intelligence (AI) algorithms to enhance inventory management processes in small and medium-sized enterprises (SMEs) within the retail sector. Accurate inventory level determination is a critical factor in improving organizational performance. Inventory levels are subject to a wide range of influences, including seasonal fluctuations, promotional campaigns, and macroeconomic conditions, which introduce significant complexity and variability. Such complexities often render manual management approaches inefficient. This research focuses on addressing these challenges through AI-based methodologies, particularly by employing machine learning and data analytics techniques to optimize inventory control. The findings of the study contribute to the literature by highlighting the potential of AI-driven approaches in reducing inventory costs and improving supply chain efficiency.
Authors: Osman Çaylı, Zeki Oralhan
Institution: Nuh Naci Yazgan University
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4406640344