Open Access

Transforming Supply Chain Management Through Artificial Intelligence: A Holistic Theoretical Analysis

PDF

John Doe ¹ ,

⁴ Department of Management Sciences, Global University

Abstract

The rapid integration of Artificial Intelligence (AI) technologies into supply chain management (SCM) represents a paradigm shift altering traditional logistics and production infrastructures across industries. This paper presents an exhaustive theoretical analysis of how AI transforms supply chain operations, underscoring opportunities, challenges, and emergent research avenues. Drawing on extensive literature — covering general AI applications in industries (Rashid & Kausik, 2024; Khaleel, Jebrel & Shwehdy, 2024), focused examinations of AI in supply chains (Sharma et al., 2022; Pournader et al., 2021; Sharma, Gunasekaran & Subramanian, 2024), and foundational SCM theory and logistics frameworks (Cooper, Lambert & Pagh, 1997; Christopher, 2016; Chopra & Meindl, 2019) — the analysis begins by situating supply chains in their traditional conceptualization and proceeds to map the transformative influence of AI across procurement, production, inventory management, logistics, and demand forecasting. Methodologically, this study conducts a conceptual synthesis and critical discourse analysis of extant literature, identifying thematic patterns, theoretical propositions, and gaps. The results highlight that AI enables predictive analytics, real-time decision-making, and network-wide optimization, while simultaneously introducing ethical, data governance, and implementation complexity concerns. In the discussion, theoretical implications are explored, including the redefinition of supply chain boundaries, the evolving role of human agents, and systemic resilience in the face of disruptions, along with limitations of existing literature. The paper concludes by proposing a comprehensive research agenda to guide future empirical and normative inquiry into AI-driven SCM transformations.

Keywords

Artificial Intelligence, Supply Chain Management, Logistics, Predictive Analytics, Digital Supply Chain, AI Governance, Inventory Management

References

Rashid, A.B.; Kausik, A.K. AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications. Hybrid Adv. 2024, 7, 100277.

Sharma, R.; Shishodia, A.; Gunasekaran, A.; Min, H.; Munim, Z.H. The role of artificial intelligence in supply chain management: Mapping the territory. Int. J. Prod. Res. 2022, 60, 7527–7550.

Khaleel, M.; Jebrel, A.; Shwehdy, D.M. Artificial Intelligence in Computer Science. Int. J. Electr. Eng. Sustain. 2024, 2, 1–21.

Pournader, M.; Ghaderi, H.; Hassanzadegan, A.; Fahimnia, B. Artificial intelligence applications in supply chain management. Int. J. Prod. Econ. 2021, 241, 108250.

Sharma, P.; Gunasekaran, A.; Subramanian, G. Enhancing Supply Chain: Exploring and Exploiting AI Capabilities. J. Comput. Inf. Syst. 2024, 1–15.

Cooper, M.; Lambert, D.; Pagh, J. Supply Chain Management: More Than a New Name for Logistics. Int. J. Logist. Manag. 1997, 8, 1–14.

MacCarthy, B.L.; Ahmed, W.A.; Demirel, G. Mapping the supply chain: Why, what and how? Int. J. Prod. Econ. 2022, 250, 108688.

Ivanov, D.; Dolgui, A.; Sokolov, B. Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”. Transp. Res. Part Logist. Transp. Rev. 2022, 160, 102676.

Christopher, M. Logistics & Supply Chain Management. Pearson UK, 2016.

Chopra, S.; Meindl, P. Supply Chain Management: Strategy, Planning, and Operation. Pearson Education, 2019.

Chowdhury, W. A. Optimizing Supply Chain Logistics Through AI & ML: Lessons from NYX. International journal of data science and machine learning, 2025, 5(01), 49‑53.

Kusiak, A. Smart manufacturing. International Journal of Production Research, 2018, 56(1-2), 508‑517.

Verma, M.; Pratap, A. Machine Learning Applications in Supply Chain Management: A Review. International Journal of Engineering Research & Technology, 2020, 9(10), 120‑125.

Waller, M.A.; Fawcett, S.E. Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 2013, 34(2), 77‑84.

Singh, A.; Misra, S.C. Application of Artificial Intelligence in Inventory Management: A Review. Journal of Supply Chain Management, 2021, 57(3), 45‑53.

Tan, K.C.; Hensher, D.A. AI in logistics and transportation management. Transportation Research Part C: Emerging Technologies, 2021, 129, 103197.

Bellamy, M.A.; Basole, R.C. Network analysis of supply chain systems: A systematic review and future research. Systems Engineering, 2013, 16(2), 235‑249.

Martin, K.E.; Shilton, K. Why do we need ethics in data science? Communications of the ACM, 2016, 59(11), 29‑31.

Zhou, W.; Piramuthu, S. IoT and supply chain management: A literature review. Journal of Industrial Information Integration, 2017, 3, 17‑26.

Amini, M.; Li, H. The relationship between e‑commerce and supply chain performance: Evidence from China. Journal of Operations Management, 2011, 29(3), 244‑260.

Waller, M.A.; Fawcett, S.E. Click here to print a maker movement supply chain: How inventions are changing supply chain fundamentals. Journal of Business Logistics, 2013, 34(2), 49‑60.

Yang, Y.; Xie, Y.; Liu, H. Intelligent supply chain management in the era of artificial intelligence. Robotics and Computer-Integrated Manufacturing, 2019, 58, 35‑45.

Most read articles by the same author(s)

<< < 3 4 5 6 7 8 9 10 11 12 > >>

International Journal of Modern Computer Science and IT Innovations

Transforming Supply Chain Management Through Artificial Intelligence: A Holistic Theoretical Analysis

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles