Bhaskar B. Gardas, Angappa Gunasekaran, Vaibhav S. Narwane
{"title":"Unlocking factors of digital twins for smart manufacturing: a case of emerging economy","authors":"Bhaskar B. Gardas, Angappa Gunasekaran, Vaibhav S. Narwane","doi":"10.1080/0951192x.2023.2257655","DOIUrl":null,"url":null,"abstract":"ABSTRACTThe Industry 4.0/smart manufacturing paradigm has significantly changed the activities and processes of organizations. Emergent smart manufacturing technology called a ‘Digital Twin’ (DT) aids organizations in enhancing overall performance by creating a virtual prototype of a real system. However, DT technology adoption in emerging economies is in the nascent stage. This research aims to identify the determinants affecting the adoption of DT technology in Indian manufacturing firms. Based on an extensive literature survey and experts’ opinions, 14 determinants were identified, and these determinants were analyzed using a hybrid multi-attribute decision-making approach to understand the contextual relationship and to identify the cause–effect relationship amongst them. Based on these results, the most critical determinants were explored, namely ‘Real-time system operations and tracking’, ‘Integration, the convergence of systems, processes & resources and enterprise collaboration’, ‘Information and Data management within or between the systems’. The manufacturing organizations of emerging economies need to consider these determinants for the effective adoption of DT technology, and policymakers can use the findings of this study to develop appropriate strategies.KEYWORDS: Information managementdigital twinsemerging economiesmanufacturing firmstechnology adoptiondecision-making Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Integrated Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/0951192x.2023.2257655","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 1
Abstract
ABSTRACTThe Industry 4.0/smart manufacturing paradigm has significantly changed the activities and processes of organizations. Emergent smart manufacturing technology called a ‘Digital Twin’ (DT) aids organizations in enhancing overall performance by creating a virtual prototype of a real system. However, DT technology adoption in emerging economies is in the nascent stage. This research aims to identify the determinants affecting the adoption of DT technology in Indian manufacturing firms. Based on an extensive literature survey and experts’ opinions, 14 determinants were identified, and these determinants were analyzed using a hybrid multi-attribute decision-making approach to understand the contextual relationship and to identify the cause–effect relationship amongst them. Based on these results, the most critical determinants were explored, namely ‘Real-time system operations and tracking’, ‘Integration, the convergence of systems, processes & resources and enterprise collaboration’, ‘Information and Data management within or between the systems’. The manufacturing organizations of emerging economies need to consider these determinants for the effective adoption of DT technology, and policymakers can use the findings of this study to develop appropriate strategies.KEYWORDS: Information managementdigital twinsemerging economiesmanufacturing firmstechnology adoptiondecision-making Disclosure statementNo potential conflict of interest was reported by the author(s).
期刊介绍:
International Journal of Computer Integrated Manufacturing (IJCIM) reports new research in theory and applications of computer integrated manufacturing. The scope spans mechanical and manufacturing engineering, software and computer engineering as well as automation and control engineering with a particular focus on today’s data driven manufacturing. Terms such as industry 4.0, intelligent manufacturing, digital manufacturing and cyber-physical manufacturing systems are now used to identify the area of knowledge that IJCIM has supported and shaped in its history of more than 30 years.
IJCIM continues to grow and has become a key forum for academics and industrial researchers to exchange information and ideas. In response to this interest, IJCIM is now published monthly, enabling the editors to target topical special issues; topics as diverse as digital twins, transdisciplinary engineering, cloud manufacturing, deep learning for manufacturing, service-oriented architectures, dematerialized manufacturing systems, wireless manufacturing and digital enterprise technologies to name a few.