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Integrating AI and Digital Twin Frameworks in Modern Entrepreneurial Supply Chains

In: Entrepreneurship and Innovation

Author

Listed:
  • Cosmina-Mihaela Rosca

    (Petroleum-Gas University of Ploiesti, Faculty of Mechanical and Electrical Engineering, Department of Automatic Control, Computers, and Electronics)

Abstract

This chapter investigates artificial intelligence (AI) and digital twin (DT) technologies in the context of entrepreneurship, with applicability in supply chain management. The chapter includes a bibliometric analysis of publications indexed in Web of Science between 2020 and 2025. A review of recent literature shows the applied trends of these technologies. The results indicate researchers’ interest in the concept of AI in entrepreneurial processes. However, the field of DT is underexplored. The practical context of small and medium-sized business ecosystems needs a new approach to these paradigms. Based on these findings, the chapter proposes a digital augmented entrepreneurship model. The practical demonstration is achieved by implementing a digital supply chain that utilizes Azure Digital Twins and an ML.NET model. These technologies are integrated into the practical demonstration to anticipate stockouts and automate replenishment processes by optimizing real-time logistics flow. The chapter’s conclusions show that the integration of AI and DT will generate a new paradigm of entrepreneurial decision-making based on data, simulation, automation, and cost reduction. The chapter also highlights a series of challenges associated with these technologies and synthesizes the information presented by emphasizing the importance of integrating these technologies into a unified software product.

Suggested Citation

  • Cosmina-Mihaela Rosca, 2026. "Integrating AI and Digital Twin Frameworks in Modern Entrepreneurial Supply Chains," Springer Books, in: Carolina Feliciana Machado & João Paulo Davim (ed.), Entrepreneurship and Innovation, pages 53-72, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-20997-9_3
    DOI: 10.1007/978-3-032-20997-9_3
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