Science, Education and Innovations in the Context of Modern Problems

Authors and Affiliations  

Brahim Ammari
Dr.
Department of Management Sciences, University of El Oued, El Oued, Algeria
E-mail: ammariguedri@gmail.com

Sara Berrehouma
Dr.
Department of Management Sciences, University of El Oued, El Oued, Algeria
E-mail: Sara-berrehouma@univ-eloued.dz

Samir Bouafia
Dr.
Faculty of Economics, Commerce and Management Sciences, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Bordj Bou Arreridj, Algeria
E-mail: Samir.bouafia@univ-bba.dz

Abdelkader Nemsi
Dr.
Department of Management Sciences, University of El Oued, El Oued, Algeria
E-mail: abdelkadernet3@gmail.com

Abdelkrim Adaika
Dr.
Department of Management Sciences, University of El Oued, El Oued, Algeria
E-mail: adaikaabdelkrim@gmail.com

Younes Zine
Dr.
Department of Management Sciences, University of El Oued, El Oued, Algeria
E-mail: zine-younes@univ-eloued.dz

Keywords

Artificial Intelligence Inventory Management Supply Chain Management Amazon Predictive Analytics Warehouse Automation Digital Transformation

Abstract

The increasing adoption of artificial intelligence (AI) has transformed the way organizations manage inventory, coordinate supply chains, and respond to rapidly changing market conditions. As global businesses face growing pressure to improve operational efficiency while maintaining high levels of customer service, AI-driven technologies have emerged as important tools for enhancing inventory visibility, forecasting accuracy, and logistical performance. Against this background, this study investigates the contribution of artificial intelligence to inventory management through an analytical case study of Amazon, a company widely recognized for its extensive use of digital technologies and automated logistics systems. The study employs a comparative analysis of inventory management indicators for 2017 and 2023, representing two different stages in Amazon’s technological development. Key performance measures include inventory turnover, inventory holding period, inventory-related costs, inventory loss rates, and the level of intelligent systems utilization. The findings reveal notable improvements across all examined indicators. Inventory turnover increased, inventory holding periods declined, storage-related costs were reduced, and inventory losses decreased. At the same time, the utilization of AI-enabled systems expanded substantially, reflecting Amazon’s growing reliance on predictive analytics, machine learning algorithms, robotics, and automated warehouse management technologies. The results suggest that artificial intelligence has contributed to greater inventory efficiency by improving demand forecasting, accelerating inventory movement, optimizing warehouse operations, and enhancing supply chain coordination. Furthermore, the integration of AI with complementary digital technologies such as the Internet of Things (IoT), cloud computing, and data analytics has strengthened operational responsiveness and supported more effective resource utilization. The study concludes that artificial intelligence is no longer merely a supporting technological resource but has become a strategic capability that shapes contemporary inventory management practices.

How to Cite

Ammari, B., Berrehouma, S., Bouafia, S., Nemsi, A., Adaika, A., & Zine, Y. (2026). Artificial Intelligence and Inventory Performance: A Longitudinal Analysis of Amazon’s Supply Chain Operations (2017–2023). Science, Education and Innovations in the Context of Modern Problems, 9(8), 1–13.

Publication History

Open Access and Licensing

© 2026 The Author(s).

Published by Science, Education and Innovations in the Context of Modern Problems (SEI) under the auspices of IMCRA – International Meetings and Conferences Research Association (Azerbaijan).

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).

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