Abstract
The concept of digital twin has gained substantial attention in both academic and industrial circles in recent years due to its potential for facilitating smart manufacturing and contributing to Industry 4.0. The digital twin concept revolves around the idea of replicating the characteristics and behaviors of real-world objects to provide accurate representations in virtual environments. It has been widely used in various industries for different purposes, such as virtual experimentation, prediction and performance optimization, monitoring, and control. However, the existing studies have not investigated the potential application of digital twin for data management in a structured and methodological manner. Therefore, this paper aims to review articles that explored the use of digital twin as a data management method at different levels and dimensions. Moreover, this review critically analyses studies that explored relevant methods that can support digital twin applications in a data management context. This study follows PRISMA guidelines and reviewed 51 academic articles from various academic databases. The primary findings indicate that the digital twin can serve as a comprehensive platform for collecting, processing, analyzing, and managing data from various sources in a unified approach. Based on the critical review, future directions in this research context are outlined.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call