Abstract

Inventory management plays a crucial role in optimizing supply chain operations, minimizing costs, and meeting customer demand effectively. With the advent of advanced technologies, machine learning (ML) has emerged as a transformative tool in the development and application of inventory models. This paper explores the current trends, challenges, and opportunitiesat the intersection of inventory modeling and machine learning. Recent trends highlight the growing integration of supervised and unsupervised learning techniques for demand forecasting, stock level optimization, and dynamic decision-making. However, several challenges persist, including data quality issues, interpretability of complex ML models, integration with legacy systems, and the need for domain-specific customization. Despite these challenges, significant opportunitiesexist in leveraging real-time analytics, reinforcement learning, and hybrid models to improve inventory accuracy and responsiveness. This study provides a comprehensive overview of the state-of-the-art developments and outlines future research directions that can enhance the effectiveness and sustainability of inventory systems using machine learning.

Author

Jyoti Chaudhary, SR Singh, Dipti Singh  ( Pages 141-166 )
Email:singhdipti113@gmail.com
Affiliation: Department of Mathematics, Chaudhary Charan Singh University Meerut      DOI:

Keyword

Machine Learning, Inventory model

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