Scalability challenges in implementing artificial intelligence in supply chain networks
Department of Industrial Engineering, Faculty of Engineering, Texas A&M University, Kingsville, Texas, United States of America.
Review Article
World Journal of Advanced Research and Reviews, 2022, 15(01), 858-861
Publication history:
Received on 12 June 2022; revised on 16 July 2022; accepted on 18 July 2022
Abstract:
The integration of Artificial Intelligence (AI) in supply chain networks promises transformative improvements in operational efficiency, predictive accuracy, and risk mitigation. From demand forecasting to autonomous logistics, AI applications hold significant potential to redefine traditional supply chain paradigms. However, despite successful pilot implementations, the journey from local adoption to enterprise-wide and global deployment remains fraught with obstacles. This paper examines the multifaceted scalability challenges associated with AI integration in supply chains, analyzing technical, infrastructural, human, and organizational dimensions. Drawing from empirical studies, case analyses, and contemporary literature, we propose a multidimensional framework that elucidates root causes, identifies capability gaps, and offers pragmatic solutions to improve scalability. We argue that a holistic approach—grounded in data standardization, architectural modularity, cultural readiness, and ethical compliance—is essential for sustainable AI scaling.
Keywords:
Artificial Intelligence; Scalability; Supply Chain; Machine Learning; Data Quality; Digital Transformation
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0