Digital Twin and Computer Vision Combination for Manufacturing and Operations : A Systematic Literature Review

Abstract

This paper examines the transformative role of the Digital Twin-Computer Vision combination (DT-CV combo) in industrial operations, focusing on its applications, challenges, and future directions. It aims to synthesize the existing literature and explore the practical use cases in operations management (OM). A comprehensive systematic literature review is conducted using PRISMA guidelines to analyze the DT-CV combo across the classification of industrial OM. However, given the breadth and importance of manufacturing and the OM field, the study excludes the literature on the DT-CV combo applied to other domains such as healthcare, smart buildings and cities, and transportation. We found that the DT-CV combo in OM is a relatively young but growing field of research. To date, only 29 articles have examined DT-CV combo solutions from various OM perspectives. Case studies are rare, with most studies relying on experimentation and laboratory testing to investigate DT-CV applications in the OM context. According to the cases and methods reviewed in the literature, the DT-CV combo has applications in different OM areas such as design, prototyping, simulation, real-time production monitoring, defect detection, process optimization, hazard detection and mitigation, safety training, emergency response simulation, optimal resource allocation, condition monitoring, inventory management, and scheduling maintenance. We also identified several benefits of DT-CV combo solutions in OM, including reducing human error, ensuring compliance with quality standards, lowering maintenance costs, mitigating production downtime, eliminating operational bottlenecks, and decreasing workplace accidents, while simultaneously improving the effectiveness of training. In this paper, we classify current applications of the DT-CV combo in OM, highlight gaps in the existing literature, and propose research questions to guide future studies in this domain. By considering the rapid phase of AI technology development and combining it with the current state of the art applications of the DT-CV combo in OM, we suggest novel concepts and future directions. The digital twin-vision language model (DT-VLM) combo as a future direction, emphasizing its potential to bridge physical–digital interfaces in industrial workflows, is one of the future development directions.