TY - JOUR
T1 - Methodologies in digital twin for manufacturing industry
T2 - A systematic literature review
AU - Aznar Lapuente, Gabriel
AU - Morella Avinzano, Paula
AU - Lamban Castillo, Maria Pilar
AU - Seneviratne, Dammika
N1 - Publisher Copyright:
© 2025 The Author(s)
PY - 2026/1
Y1 - 2026/1
N2 - In recent years, the investigation of methodologies pertaining to the application of digital twins within the industrial sector has garnered significant interest. This increased development is attributed to the challenges associated with the sustainable design, development, and implementation of such solutions. This study undertakes a systematic review of the literature, encompassing entries from three prominent databases over the past five years. Consequently, the study analyzes step-by-step methodologies, both generalist and specific to particular domains and applications. The paper commences with a brief introduction and presentation of foundational concepts, followed by an exploration of related works and the methodology employed in this research. The study proceeds with detailed sections dedicated to each analyzed methodology, culminating in a discussion and conclusion section that underscore the increasing interest in the development of novel methodologies for the design and implementation of digital twins. Despite the growing volume of research in this domain, a significant proportion of the literature lacks comparative analyses of the benefits, advantages, and limitations associated with different methodological approaches. Furthermore, many of these studies remain largely theoretical or are confined to narrowly defined use cases, with limited evidence of practical, real-world application. The review concludes that digital twins constitute complex systems comprising real, digital, virtual, and conceptual components. It also highlights those numerous methodologies, originally developed in other disciplines, possess the potential to be adapted to support the development and deployment of digital twin systems.
AB - In recent years, the investigation of methodologies pertaining to the application of digital twins within the industrial sector has garnered significant interest. This increased development is attributed to the challenges associated with the sustainable design, development, and implementation of such solutions. This study undertakes a systematic review of the literature, encompassing entries from three prominent databases over the past five years. Consequently, the study analyzes step-by-step methodologies, both generalist and specific to particular domains and applications. The paper commences with a brief introduction and presentation of foundational concepts, followed by an exploration of related works and the methodology employed in this research. The study proceeds with detailed sections dedicated to each analyzed methodology, culminating in a discussion and conclusion section that underscore the increasing interest in the development of novel methodologies for the design and implementation of digital twins. Despite the growing volume of research in this domain, a significant proportion of the literature lacks comparative analyses of the benefits, advantages, and limitations associated with different methodological approaches. Furthermore, many of these studies remain largely theoretical or are confined to narrowly defined use cases, with limited evidence of practical, real-world application. The review concludes that digital twins constitute complex systems comprising real, digital, virtual, and conceptual components. It also highlights those numerous methodologies, originally developed in other disciplines, possess the potential to be adapted to support the development and deployment of digital twin systems.
KW - Ciber physical systems
KW - Digital twin
KW - Frameworks
KW - Manufacturing industry
KW - Methodology
KW - Modeling
UR - https://www.scopus.com/pages/publications/105009985616
U2 - 10.1016/j.future.2025.107997
DO - 10.1016/j.future.2025.107997
M3 - Article
AN - SCOPUS:105009985616
SN - 0167-739X
VL - 174
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
M1 - 107997
ER -