Laminating Analysis of Digital Twin Drivers and Barriers for Systematic Decision-making, Critical Understanding and Implementation in Manufacturing Industries

Abstract

Today, manufacturing products, processes and assembly lines can be optimized by mirroring the allied physical assets and counterparts available over system boundaries, which can be done by Digital Twin (DT). Manufacturing teams can analyze different data sources and reduce failures by using DT, which in turn can increase production efficiency and decrease industrial breaks. But, it is difficult to mount DT structures and fundamental frameworks due to evident barriers and challenges. Accordingly, the present study is conducted with the purpose to report crucial barriers and drivers that can uplift the ease of implementation of DT in the manufacturing industries. The identification of DT barriers and drivers is presented to help policymakers and decision-makers establish effective strategies to address DT implementation. The synergies between barriers, sub-barriers, drivers and sub-drivers are presented in the study, where a seven-stage research methodology based on entropy weightage method and simple additive weighting technique is presented for evaluation. This study discusses the theoretical concept of DT, its design components from manufacturing insights and presents an application framework of DT for smart manufacturing system design. In the study, five major DT drivers with nineteen sub-drivers are categorized. Additionally, six major DT barriers with thirty-five sub-barriers are also categorized and presented. The study evaluated DT drivers and barriers for persuading the implementation of DT in manufacturing industries and reported critical insights related to the theoretical foundation of DT modelling to develop a smart manufacturing system. The chief novelty of the study lies in inducing the synergies between barriers, sub-barriers, drivers and sub-drivers for the due implementation of DT under the prospects of manufacturing industries.