Integrating AI and Digital Twin Frameworks in Modern Entrepreneurial Supply Chains

Cosmina-Mihaela Rosca ()
Additional contact information
Cosmina-Mihaela Rosca: Petroleum-Gas University of Ploiesti, Faculty of Mechanical and Electrical Engineering, Department of Automatic Control, Computers, and Electronics

A chapter in Entrepreneurship and Innovation, 2026, pp 53-72 from Springer

Abstract: 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.

Keywords: Artificial intelligence; Digital twin; Supply chain management; Entrepreneurship; Azure infrastructure; Cloud platform (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-20997-9_3

Ordering information: This item can be ordered from
http://www.springer.com/9783032209979

DOI: 10.1007/978-3-032-20997-9_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().