International Journal of Research and Scientific Innovation (IJRSI)
The Role of Artificial Intelligence in Optimising Retail Inventory Management
Published October 14, 2025 • Vol. 12, Issue 9, pp. 3991–3994Open Access
DOI: 10.51244/IJRSI.2025.120800357
Abstract
This review paper examines how Artificial Intelligence (AI) is transforming retail inventory management with predictive analytics, machine learning algorithms, and automation. Drawing on the results of four articles, the review examines AI-facilitated enhancements in demand forecasting, inventory optimization, supply chain integration, and reordering automation. The study also examines challenges and strategic implications of leveraging AI in the retail industry. The study establishes the premise that not only does AI enhance forecasting accuracy and response time, but it also supports lean inventory practices, scalability, and real-time responsiveness. With the help of AI, retail firms try to realign their inventory to achieve its full potential.
Keywords: Artificial Intelligence, Inventory Management, Retail, Demand Forecasting, Machine Learning, Automation, Supply Chain
| Journal | International Journal of Research and Scientific Innovation (IJRSI) |
|---|---|
| ISSN | 2321-2705 |
| Volume / Issue | Volume 12, Issue 9 |
| Pages | 3991–3994 |
| Publication date | October 14, 2025 |
| DOI | 10.51244/IJRSI.2025.120800357 |
| Publisher | RSIS International |
| License | Open Access |
How to cite this article
Sangeeta Jha, & Satyadev Singh (2025). The Role of Artificial Intelligence in Optimising Retail Inventory Management. International Journal of Research and Scientific Innovation (IJRSI), 12(9), 3991-3994. https://doi.org/10.51244/IJRSI.2025.120800357
BibTeX
@article{Sangeeta2025,
title = {The Role of Artificial Intelligence in Optimising Retail Inventory Management},
author = {Sangeeta Jha and Satyadev Singh},
journal = {International Journal of Research and Scientific Innovation (IJRSI)},
volume = {12},
number = {9},
pages = {3991--3994},
year = {2025},
doi = {10.51244/IJRSI.2025.120800357},
publisher = {RSIS International}
}