Optimizing inventory management in the industrial sector using Artificial Intelligence tools (#1752)

Authors

Chavez Milla, Humberto Angel

Caro Sanchez Sanchez, Kelly Xiomara

Durand Asencios, Malco Andres

Vigo Cancino, Jhonny Manfredy

Abstract

This systematic literature review (SLR) aims to determine how the application of Artificial Intelligence (AI) tools significantly contributes to inventory management in the industrial sector. Although the systematic review is limited by its reliance on secondary data and regional variations, it highlights the substantial innovative capacity of AI in inventory management. Future empirical studies and the development of strategies to facilitate the implementation of AI in developing regions, such as Peru, are recommended, along with investments in technology and training. This approach not only promises to refine inventory management practices, but also foster broader economic and technological advancements in these regions. The SLR confirms that Artificial Intelligence, especially through machine learning and neural networks, can significantly optimize inventory resource management in industry. Key findings highlight notable reductions in operating costs and improvements in efficiency and effectiveness, particularly in regions with advanced infrastructure. In Peru, a study on a company in the agri-food sector showed an improvement in the management of perishable products through the use of AI techniques.