Digital Twin Models in Industrial Operations: A Systematic Literature Review

Abstract

A Digital Twin is one of the enabling technologies of Industry 4.0 that couples actual physical systems with corresponding virtual representation. Currently, the application of Digital Twin models has attracted the attention of many researchers with the focus of production, predictive maintenance, and after-sale services. However, its role in industrial operations particularly in production, predictive maintenance, and after-sales services lacks efforts to systematically review the state-of-the-art. Moreover, this review discusses some of the challenges in implementing DT models to extend its role in the aforementioned application domains. In this paper, a systematic literature review was conducted to assess the role of Digital Twin models in industrial operations and to identify challenges for realization. Twenty-five research studies that were published until the end of June 2019 were selected and analyzed in order to show the current state-of-the-art on the role of Digital Twin models in the industrial operations and challenges in the implementation. Review results underline that the majority of the studies have focused on the application of Digital Twins in the production sector followed by predictive maintenance and after-sales services. Many authors have discussed how to apply Digital Twin models without remarking their role in the aforementioned domains of industrial operations. This paper provides insights for different industrial sectors, practitioners, researchers and experts of the field on the specific roles of Digital Twin models and challenges of implementing these models in the areas of production, predictive maintenance, and after-sale services.