Digital Twins and Their Applications in Modeling Different Levels of Manufacturing Systems: A Review

TL;DR

This review explores the applications, benefits, and challenges of Digital Twins in manufacturing, highlighting their potential to improve efficiency and decision-making while addressing integration and security issues.

Contribution

It provides a comprehensive classification of Digital Twins, analyzes modeling technologies, and discusses real-world applications, opportunities, and barriers for widespread adoption.

Findings

Digital Twins enhance manufacturing efficiency and lifecycle management.

Challenges include data integration complexity and cybersecurity risks.

Case studies demonstrate significant operational improvements.

Abstract

Digital Twin (DT) has gained great interest as an innovative technology in Industry 4.0 that enables advanced modeling, simulation, and optimization of service and manufacturing systems. This article provides an extensive review of the literature on digital twins (DTs) and their utilization at the levels of product/production line, production system, and enterprise, and considers how they have been applied under real industrial conditions. This article classifies the types of DTs as well as modeling technologies of DT and applications in different fields, with particular focus on the research of strengths and limitations of Discrete Event Simulation (DES) for systems modelling. A generic structure for DT is proposed, outlining the essential components and flow of data. Case studies demonstrate the benefits of DTs for increased efficiency, reduced downtime, and improved lifecycle management, as well as challenges caused by the complexity of data integration and cybersecurity risk, and high implementation costs. This paper contributes to the growing body of knowledge by identifying both the opportunities and barriers to widespread DT adoption. This study concludes that while DTs offer transformative capabilities for enhancing efficiency and decision-making, overcoming these challenges is crucial for realizing their widespread adoption and impact across service and manufacturing sectors.