Reviewing the Application of Data Driven Digital Twins in Manufacturing Systems: A Business and Management Perspective

Abstract

Simulation modelling has been a widely used tool for analyzing manufacturing systems and improving their performance. Although, little attention has been paid to the application of data-driven simulation modelling of the manufacturing systems. With the development of new-generation information and digitalization technologies, more data can be collected from the manufacturing shop floor. This has paved the way for employing data-driven simulation of manufacturing systems knowns as a digital twin. This paper reviews the literature and practice on digital twins in manufacturing systems from a business and management perspective to identify the gaps and recommend avenues for future research. The results show that 2018 has been a turning point in the literature with small scale case studies of digital twins emerging independent of commercial practice. Since 2018 the digital twin literature has moved on from descriptions and conceptual frameworks to focus on one product lifecycle phase with any reference to sustainability advance being confined to energy and resource efficiency. Practice has been advanced by manufacturers and IT vendors however the definition of digital twins lacks precision for ease comparison with the literature. Future avenues for research are identified in the areas of lifecycle phases and digital model fidelity.