Benefits, challenges, and limitations of inventory control using machine learning algorithms: literature review

Juan Camilo Gutierrez (), Sonia Isabel Polo Triana () and Juan Sebastian León Becerra ()
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Juan Camilo Gutierrez: Universidad de Investigación y Desarrollo UDI
Sonia Isabel Polo Triana: Universidad de Investigación y Desarrollo UDI
Juan Sebastian León Becerra: Universidad de Investigación y Desarrollo UDI

OPSEARCH, 2025, vol. 62, issue 3, No 3, 1140-1172

Abstract: Abstract This article presents a comprehensive review of the literature on the benefits, challenges, and limitations of using machine learning (ML) algorithms in inventory control, focusing on how these algorithms can transform inventory management and improve operational efficiency in supply chains. The originality of the study lies in its integrative approach, combining a detailed review with a critical analysis of current and future applications of ML in inventory control. The main aspects covered in the review include the types of ML algorithms most utilised in inventory control, key benefits such as replenishment optimisation and improved prediction accuracy, and the technical, ethical, and practical limitations in their implementation. The review also addresses challenges in managing high-dimensional data and adapting these algorithms to different operational contexts. The research method adopts a systematic approach to identify and analyse relevant sources, with a thorough bibliographic search resulting in a final corpus of 81 articles. The principal contribution of this research is a compendium of strategies for the implementation of ML in inventory control that leverages potential benefits while mitigating the technical and practical challenges that may arise, contributing to both theory and practice and providing valuable insights for academics and professionals in the industry, underscoring the potential and challenges of using ML in modern inventory control.

Keywords: Inventory control; Machine learning; Deep learning; Strategies (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12597-024-00839-0

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