Enhancing Supply Chain Management Using Machine Learning Techniques: A Comprehensive Review, Gap Analysis, and Strategic Framework

Attia Hussien Gomaa

doi:10.65773/tscr.1.1.6

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Section

Review Article

Issue

Vol. 1 No. 1 (2025)

How to Cite

Gomaa, A. H. (2025). Enhancing Supply Chain Management Using Machine Learning Techniques: A Comprehensive Review, Gap Analysis, and Strategic Framework. Transnational Supply Chain Research, 1(1), 1-20. https://doi.org/10.65773/tscr.1.1.6

Authors

Attia Hussien Gomaa

Faculty of Engineering. Shubra, Benha University, Cairo, Egypt

Abstract

In the Industry 4.0 era, Supply Chain Management (SCM) is being transformed by digitalization and advanced analytics, with Machine Learning (ML) emerging as a pivotal driver of efficiency, resilience, and intelligent decision-making. This study reviews ML applications across major SCM domains, demonstrating their potential to enhance predictive accuracy, operational agility, and competitiveness. However, adoption remains constrained by persistent challenges, including data quality, model interpretability, scalability, and integration with legacy systems. A structured gap analysis highlights priority areas for advancement, such as explainable ML, real-time analytics, sustainability-oriented applications, and human–machine collaboration. To address these gaps, a strategic framework is proposed, comprising four interdependent pillars: Data and Systems Foundations, Algorithmic Intelligence, Organizational and Human Integration, and Strategic and Sustainability Alignment. This framework provides a roadmap for embedding ML into supply chains by reinforcing data ecosystems, advancing adaptive and interpretable algorithms, fostering human-centered adoption, and aligning digital transformation with ethical and sustainability imperatives. The study concludes that ML adoption represents a socio-technical transformation rather than a technical upgrade, offering both theoretical insights and practical guidance for building intelligent, resilient, and sustainable SCM.

Keywords:

machine learning, predictive analytics, Industry 4.0, smart supply chains

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