Open Access Research Article

Artificial Intelligence and Supply Chain Management of Small and Medium-Sized Enterprises

Authors

Mojdeh Kheirati Saadi * Department of Entrepreneurship (New Business Orientation), University of Sistan and Baluchestan, Sistan and Baluchestan, Iran.
Ahmad Kazemi Department of Computer Science, University of Sistan and Baluchestan, Sistan and Baluchestan, Iran.

https://doi.org/10.48313/scodm.v2i1.25

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Abstract

Artificial Intelligence (AI) has emerged as a pivotal instrument in the supply chain management of Small and Medium-Sized Enterprises (SMEs). Through the enhancement of operational processes, cost reduction, and the improvement of decision-making accuracy, particularly via demand forecasting, inventory optimization, risk management, process automation, and increased transparency, AI enables SMEs to achieve superior performance within today’s highly competitive markets. This study focuses on investigating the interaction between AI and supply chain management in SMEs. A comprehensive review of the existing literature on the impact of AI on supply chain management, alongside prior research examining the benefits and barriers to its application in this domain, was conducted. Additionally, all referenced studies were analyzed using a descriptive approach within the empirical background section. The findings reveal that AI is being increasingly adopted in the supply chain management of SMEs, offering substantial opportunities for enhancing supply chain performance and exerting positive influences on operational efficiency, cost structures, and decision-making capabilities.

Keywords

Artificial intelligence Intelligent supply chain management Supply chain of small and medium-sized enterprises

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Published 2025-03-15
Issue Vol. 2 No. 1 (2025): Supply Chain and Operations Decision Making
Section Articles
Document Type Research Article
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Kheirati Saadi, M., & Kazemi, A. . (2025). Artificial Intelligence and Supply Chain Management of Small and Medium-Sized Enterprises. Supply Chain and Operations Decision Making, 2(1), 12-20. https://doi.org/10.48313/scodm.v2i1.25

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