Abstract: Effective inventory management is fundamental to supply chain resilience and efficiency. Artificial intelligence (AI) has emerged as a transformative solution that enables more dynamic and data-driven inventory strategies. To map the latest advancements in this rapidly evolving field, this study presents a systematic literature review (SLR) of AI techniques in inventory management. The review was conducted following the PRISMA 2020 guidelines, through which 87 high-quality articles published between 2021 and 2025 were systematically analyzed. Our review identifies machine learning (ML), deep learning (DL), reinforcement learning (RL), and hybrid methods as the predominant AI technologies. These techniques primarily address three foundational tasks. In demand forecasting, they improve prediction accuracy and mitigate stockout and overstock risks. For inventory control, they balance costs with service levels and optimize replenishment strategies. In inventory classification, they facilitate targeted resource allocation. Despite these advancements, AI research confronts significant challenges, particularly in data dependency, model interpretability, and implementation overhead. To address these gaps, we suggest future research focused on data-efficient learning, explainable AI, and lightweight, integrated frameworks to lower adoption barriers. This review provides a timely and holistic overview of the current research landscape, which serves as a reference for academics to identify research directions.

Jinjin Li, Huijun Huang, Yuping Gong, Lei Wang, Xiangui Yin and Yichang Liu. “A Review of Artificial Intelligence in Inventory Management: Methods, Applications and Directions”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.10 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161090

@article{Li2025,
title = {A Review of Artificial Intelligence in Inventory Management: Methods, Applications and Directions},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161090},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161090},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {10},
author = {Jinjin Li and Huijun Huang and Yuping Gong and Lei Wang and Xiangui Yin and Yichang Liu}
}