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A Bibliometric Analysis of Digital Twin in the Supply Chain

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  • Weng Siew Lam

    (Department of Physical and Mathematical Science, Faculty of Science, Universiti Tunku Abdul Rahman, Kampar Campus, Jalan Universiti, Bandar Barat, Kampar 31900, Perak, Malaysia)

  • Weng Hoe Lam

    (Department of Physical and Mathematical Science, Faculty of Science, Universiti Tunku Abdul Rahman, Kampar Campus, Jalan Universiti, Bandar Barat, Kampar 31900, Perak, Malaysia)

  • Pei Fun Lee

    (Department of Physical and Mathematical Science, Faculty of Science, Universiti Tunku Abdul Rahman, Kampar Campus, Jalan Universiti, Bandar Barat, Kampar 31900, Perak, Malaysia)

Abstract

Digital twin is the digital representation of an entity, and it drives Industry 4.0. This paper presents a bibliometric analysis of digital twin in the supply chain to help researchers, industry practitioners, and academics to understand the trend, development, and focus of the areas of digital twin in the supply chain. This paper found several key clusters of research, including the designing of a digital twin model, integration of a digital twin model, application of digital twin in quality control, and digital twin in digitalization. In the embryonic stage of research, digital twin was tested in the production line with limited optimization. In the development stage, the importance of digital twin in Industry 4.0 was observed, as big data, machine learning, Industrial Internet of Things, blockchain, edge computing, and cloud-based systems complemented digital twin models. Digital twin was applied to improve sustainability in manufacturing and production logistics. In the current prosperity stage with high annual publications, the recent trends of this topic focus on the integration of deep learning, data models, and artificial intelligence for digitalization. This bibliometric analysis also found that the COVID-19 pandemic drove the start of the prosperity stage of digital twin research in the supply chain. Researchers in this field are slowly moving towards applying digital twin for human-centric systems and mass personalization to prepare to transit to Industry 5.0.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3350-:d:1207196
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    1. Georgiana-Alina Crisan & Anda Belciu & Madalina Ecaterina Popescu, 2025. "Digital Transformation—One Step Further to a Sustainable Economy: The Bibliometric Analysis," Sustainability, MDPI, vol. 17(4), pages 1-46, February.
    2. Sung, Kiseo & Park, Kyu-Tae & Lee, Hakyeon, 2024. "Landscaping the digital twin technology: Patent-based networks and technology reference model," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    3. Alexandra Nicoleta Ciucu-Durnoi & Camelia Delcea & Aurelia Stănescu & Cosmin Alexandru Teodorescu & Vanesa Mădălina Vargas, 2024. "Beyond Industry 4.0: Tracing the Path to Industry 5.0 through Bibliometric Analysis," Sustainability, MDPI, vol. 16(12), pages 1-26, June.
    4. Igor Fellype Loureiro Valenca Filgueiras & Fagner José Coutinho de Melo & Eryka Fernanda Miranda Sobral & Aline Amaral Leal Barbosa & Denise Dumke de Medeiros & Pablo Aurélio Lacerda de Almeida Pinto , 2024. "Analyzing the Benefits of Industry 4.0 Technologies That Impact Sustainability 4.0 in Banking Services," Sustainability, MDPI, vol. 16(14), pages 1-25, July.
    5. Pallawi Baldeo Sangode, 2025. "Fusion of Blockchain and Industry 4.0: A Bibliometric Analysis and Detailed Review," SN Operations Research Forum, Springer, vol. 6(1), pages 1-20, March.
    6. Ilaria Bucci & Virginia Fani & Romeo Bandinelli, 2024. "Towards Human-Centric Manufacturing: Exploring the Role of Human Digital Twins in Industry 5.0," Sustainability, MDPI, vol. 17(1), pages 1-28, December.

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