Transforming Supply Chains with Artificial Intelligence: A Short Review

The emergence of Industry 4.0, combined with the rapid advancement of Artificial Intelligence (AI), is significantly reshaping supply chain management (SCM). These technologies are enhancing operational efficiency, strengthening resilience, and supporting more informed strategic decision-making. This study presents a detailed review of AI applications across major supply chain functions, including customer relationship management, inventory control, transportation systems, procurement, demand prediction, and risk assessment. AI techniques such as machine learning, natural language processing, and generative models are enabling organizations to optimize logistics operations, minimize uncertainties, and improve forecasting accuracy. Alongside these benefits, the study also examines key challenges associated with AI adoption, including technical complexity, organizational preparedness, and ethical considerations. Furthermore, it highlights the importance of AI in improving transparency and adaptability within supply chains, particularly during periods of disruption and global uncertainty. Looking ahead, emerging trends point toward the development of human-centered AI systems, greater levels of digital integration, and increased attention to ethical and data security issues. The study concludes that AI will play a crucial role in developing more sustainable, adaptive, and resilient supply chains, while also outlining potential directions for future research and practical implementation.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-27
DOI
https://doi.org/10.5281/zenodo.19255216
Primary Topic
Supply Chain Resilience and Risk Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Transforming Supply Chains with Artificial Intelligence: A Short Review

Mr. Vinod A. Gunjal Dr. Niraj C. Chaudhari
Zenodo (CERN European Organization for Nuclear Research)
Supply Chain Resilience and Risk Management
article

Transforming Supply Chains with Artificial Intelligence: A Short Review

Mr. Vinod A. Gunjal Dr. Niraj C. Chaudhari
article en

Abstract

The emergence of Industry 4.0, combined with the rapid advancement of Artificial Intelligence (AI), is significantly reshaping supply chain management (SCM). These technologies are enhancing operational efficiency, strengthening resilience, and supporting more informed strategic decision-making. This study presents a detailed review of AI applications across major supply chain functions, including customer relationship management, inventory control, transportation systems, procurement, demand prediction, and risk assessment. AI techniques such as machine learning, natural language processing, and generative models are enabling organizations to optimize logistics operations, minimize uncertainties, and improve forecasting accuracy. Alongside these benefits, the study also examines key challenges associated with AI adoption, including technical complexity, organizational preparedness, and ethical considerations. Furthermore, it highlights the importance of AI in improving transparency and adaptability within supply chains, particularly during periods of disruption and global uncertainty. Looking ahead, emerging trends point toward the development of human-centered AI systems, greater levels of digital integration, and increased attention to ethical and data security issues. The study concludes that AI will play a crucial role in developing more sustainable, adaptive, and resilient supply chains, while also outlining potential directions for future research and practical implementation.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 4%
Supply Chain Resilience and Risk Management
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Transforming Supply Chains with Artificial Intelligence: A Short Review — Mr. Vinod A. Gunjal Dr. Niraj C. Chaudhari · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS