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Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

Author

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  • Vivek Warke

    (Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, India)

  • Satish Kumar

    (Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, India
    Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, India)

  • Arunkumar Bongale

    (Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, India)

  • Ketan Kotecha

    (Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, India)

Abstract

The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, journal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.

Suggested Citation

  • Vivek Warke & Satish Kumar & Arunkumar Bongale & Ketan Kotecha, 2021. "Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis," Sustainability, MDPI, vol. 13(18), pages 1-49, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10139-:d:632790
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    1. João Vieira & João Poças Martins & Nuno Marques de Almeida & Hugo Patrício & João Gomes Morgado, 2022. "Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins," Sustainability, MDPI, vol. 14(12), pages 1-23, June.
    2. Issam A. R. Moghrabi & Sameer Ahmad Bhat & Piotr Szczuko & Rawan A. AlKhaled & Muneer Ahmad Dar, 2023. "Digital Transformation and Its Influence on Sustainable Manufacturing and Business Practices," Sustainability, MDPI, vol. 15(4), pages 1-35, February.
    3. Salem Ahmed Alabdali & Salvatore Flavio Pileggi & Dilek Cetindamar, 2023. "Influential Factors, Enablers, and Barriers to Adopting Smart Technology in Rural Regions: A Literature Review," Sustainability, MDPI, vol. 15(10), pages 1-38, May.
    4. Fangyi Xu & Jihong Wang, 2025. "Harnessing Hybridized Machine Learning Algorithms for Sustainable Smart Production: A Case Study of Solar PV Energy in China," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 3214-3264, March.
    5. Leandra Bezerra dos Santos & Fagner José Coutinho de Melo & Djalma Silva Guimaraes Junior & Eryka Fernanda Miranda Sobral & Denise Dumke de Medeiros, 2023. "Application of ISM to Identify the Contextual Relationships between the Sustainable Solutions Based on the Principles and Pillars of Industry 4.0: A Sustainability 4.0 Model for Law Offices," Sustainability, MDPI, vol. 15(19), pages 1-20, October.
    6. Goran Savić & Milan Segedinac & Zora Konjović & Milan Vidaković & Radoslav Dutina, 2023. "Towards a Domain-Neutral Platform for Sustainable Digital Twin Development," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    7. Eun-Young Ahn & Seong-Yong Kim, 2023. "Digital Twin Application and Bibliometric Analysis for Digitization and Intelligence Studies in Geology and Deep Underground Research Areas," Data, MDPI, vol. 8(4), pages 1-20, April.
    8. Jiayao Liu & Linfeng Wang & Yunsheng Wang & Shipu Xu & Yong Liu, 2023. "Research on the Interface of Sustainable Plant Factory Based on Digital Twin," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    9. Kuzma Kukushkin & Yury Ryabov & Alexey Borovkov, 2022. "Digital Twins: A Systematic Literature Review Based on Data Analysis and Topic Modeling," Data, MDPI, vol. 7(12), pages 1-21, November.
    10. Rafał Trzaska & Adam Sulich & Michał Organa & Jerzy Niemczyk & Bartosz Jasiński, 2021. "Digitalization Business Strategies in Energy Sector: Solving Problems with Uncertainty under Industry 4.0 Conditions," Energies, MDPI, vol. 14(23), pages 1-21, November.
    11. Weng Siew Lam & Weng Hoe Lam & Pei Fun Lee, 2023. "A Bibliometric Analysis of Digital Twin in the Supply Chain," Mathematics, MDPI, vol. 11(15), pages 1-24, July.

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