Disruptive AI and Machine Learning Technologies for Secure Cloud-Based Supply Chain and Manufacturing Systems

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Published: 2026-02-05
DOI: https://doi.org/10.15662/IJRPETM.2026.0901007
Keywords:
Disruptive Technologies, Artificial Intelligence, Machine Learning, Cloud Computing, Supply Chain Management, Manufacturing Systems, Cybersecurity

Main Article Content

Maria Eleni Karagianni
Senior IT Project Manager, Greece

Abstract

The rapid evolution of global supply chains and manufacturing ecosystems has intensified the demand for intelligent, scalable, and secure digital infrastructures. Disruptive technologies driven by Artificial Intelligence (AI) and Machine Learning (ML), when integrated with cloud computing, are reshaping traditional supply chain management and manufacturing operations. These technologies enable predictive decision-making, real-time visibility, autonomous optimization, and enhanced operational resilience. However, the increased reliance on cloud-based platforms introduces significant security challenges, including data breaches, cyberattacks, and compliance risks. This paper presents a comprehensive analysis of disruptive AI and ML technologies deployed in secure cloud-based supply chain and manufacturing systems. It examines key architectural components, security-aware AI frameworks, and cloud-enabled intelligence models that support operational efficiency while ensuring data integrity and confidentiality. The study further explores real-world applications, benefits, limitations, and emerging research challenges, highlighting the role of secure cloud infrastructures in enabling next-generation intelligent supply chain and manufacturing ecosystems.

Article Details

Issue

Vol. 9 No. 1 (2026): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Disruptive AI and Machine Learning Technologies for Secure Cloud-Based Supply Chain and Manufacturing Systems. (2026). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(1), 54-65. https://doi.org/10.15662/IJRPETM.2026.0901007

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