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International Journal of Science, Strategic Management and Technology

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AI -DRIVEN INVENTORYMANAGEMENT- INNOVATIONS FOR THE FUTURE

AUTHORS:
Abhishek Singh
Mentor
Prof. Sandeep Anand
Affiliation
BBA Logistics And Supply Chain Management
DOI: 10.55041/ijsmt.v2i3.169
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

In the modern era of digital transformation, Artificial Intelligence (AI) is revolutionizing every aspect of business operations, and inventory management stands out as one of the most promising areas of innovation. Traditional inventory systems were largely dependent on manual data entry, periodic reviews, and reactive decision-making, which often led to inefficiencies such as overstocking, stockouts, high carrying costs, and poor demand forecasting. However, with the integration of AI, organizations are now shifting toward intelligent, predictive, and automated inventory management systems that enhance both operational efficiency and strategic decision-making.

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 03
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Mar 21 2026
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Singh, A. (2026). AI -Driven Inventorymanagement- Innovations for the Future. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.169

Singh, Abhishek. "AI -Driven Inventorymanagement- Innovations for the Future." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.169.

Singh, Abhishek. "AI -Driven Inventorymanagement- Innovations for the Future." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.169.

References
1.Aamer, A. M. (2022). Artificial Intelligence Applications in Supply Chain and Inventory Management: A Review of the Current State and Future Directions. Journal of Supply Chain Management Research, 15(3), 45–59.

2.Ahuett-Garza, H., & Kurfess, T. (2018). A Brief Discussion on the Trends of Automation and Industrial Artificial Intelligence. Manufacturing Letters, 15, 60–63.

3.Arora, R., & Agarwal, S. (2021). Role of Artificial Intelligence in Modernizing Inventory and Logistics Management Systems. International Journal of Innovative Research in Technology & Management, 10(4), 32–41.

4.Baryannis, G., Dani, S., & Antoniou, G. (2019). Predictive Analytics and Artificial Intelligence in Supply Chain Management: Review and Implications for the Future. Computers & Industrial Engineering, 137, 106024.

5.Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big Data Analytics in Operations Management. Production and Operations Management, 27(10), 1868–1888.

6.Dubey, R., Gunasekaran, A., & Childe, S. J. (2020). Artificial Intelligence for Supply Chain Resilience: Conceptual Framework and Future Research Directions. International Journal of Production Research, 58(23), 7291–7310.

7.Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The Impact of Digital Technologies and AI on Supply Chain Resilience and Efficiency. International Journal of Production Research, 57(15–16), 4705–4725.Kaur, P., & Singh, R. (2020). AI-Enabled Inventory Optimization and Demand Forecasting: A Comparative Study of Machine Learning Models. Journal of Business Analytics, 5(2), 120–133.

8.Khan, M. A., & Qianli, D. (2022). Artificial Intelligence and Data Analytics for Real-Time Inventory Optimization. Journal of Industrial Information Integration, 28, 100335.

9.Lee, I., & Lee, K. (2021). Internet of Things (IoT) and Artificial Intelligence in Supply Chain Management: Emerging Trends and Future Prospects. Business Horizons, 64(6), 725–736.

10.Li, Z., & Wang, Y. (2020). AI-Based Forecasting Systems for Smart Warehousing and Inventory Control. Journal of Intelligent Manufacturing, 31(8), 1971–1985.
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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