Dr. Valeria, Conti and Dr. Hiroshi, Yamamoto and Dr. Felipe, Morales (2022) Leveraging Digital Twins and AI-Driven Analytics to Accelerate Organizational Digital Transformation. International Journal of Trend in Scientific Research and Development, 6 (4). pp. 2396-2404. ISSN 2456-6470

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Abstract

Digital transformation has become a strategic imperative for organizations seeking to remain competitive in an increasingly data-driven and fast-paced global economy. Among the most promising enablers of this transformation are digital twins—virtual replicas of physical assets, processes, or systems—and AI-driven analytics, which together enable predictive, prescriptive, and autonomous decision-making. This article explores how the integration of digital twins with artificial intelligence accelerates organizational digital transformation by providing real-time visibility, operational optimization, and innovation at scale. It examines core applications across industries such as manufacturing (predictive maintenance, process optimization), healthcare (personalized treatment and hospital operations), energy (smart grids and sustainability), and logistics (supply chain resilience and efficiency). Key benefits discussed include enhanced agility, cost reduction, risk mitigation, and improved customer-centricity. At the same time, the article critically evaluates challenges such as data interoperability, model accuracy, cybersecurity, and adoption barriers, offering insights into mitigation strategies. Finally, it highlights emerging trends, including the convergence of digital twins with IoT, 5G, edge computing, and autonomous systems, alongside the role of industry standards and governance frameworks in enabling large-scale adoption. The findings suggest that organizations that strategically leverage digital twins and AI-driven analytics will not only accelerate their digital transformation journey but also unlock new business models, improve resilience, and achieve sustainable competitive advantage.

Item Type:	Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	26 Sep 2025 07:29
Last Modified:	26 Sep 2025 07:29
URI:	http://eprints.umsida.ac.id/id/eprint/16376

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