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Trends in Adopting Industry 4.0 for Asset Life Cycle Management for Sustainability: A Keyword Co-Occurrence Network Review and Analysis

Author

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  • Sachini Weerasekara

    (Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA)

  • Zhenyuan Lu

    (Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA)

  • Burcu Ozek

    (Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA)

  • Jacqueline Isaacs

    (Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA)

  • Sagar Kamarthi

    (Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA)

Abstract

With the potential of Industry 4.0 technologies to enable sustainable manufacturing, asset life cycle management (ALCM) has been gaining increasing attention in recent years. This study explores the evolution of Industry 4.0 technology applications to sustainable ALCM from 2002 to 2021. This study is based on keywords collected from 3896 ALCM-related scientific articles published in the Web of Science, IEEE Xplore and Engineering Village between 2002 and 2021. We conducted a review analysis of these keywords using a network science-based methodology, which unlike the tedious traditional literature review methods, gives the capability to analyze a huge number of scientific articles efficiently. We built keyword co-occurrence networks (KCNs) from the keywords and explored the network characteristics to uncover meaningful knowledge patterns, knowledge components, knowledge structure, and research trends in the body of literature at the intersection of ALCM and Industry 4.0. The network modeling and data analysis results identify the emerging Industry 4.0-related keywords in ALCM literature and indicate the recent explosion of connectivity among keywords. We found IoT, predictive maintenance and big data to be the top three most popular Industry 4.0-related keywords in ALCM literature. Furthermore, this study maps relevant ALCM keywords in contemporary literature to the nine pillars of Industry 4.0 to help the responsible manufacturing community identify research trends and emerging technologies for sustainability.

Suggested Citation

  • Sachini Weerasekara & Zhenyuan Lu & Burcu Ozek & Jacqueline Isaacs & Sagar Kamarthi, 2022. "Trends in Adopting Industry 4.0 for Asset Life Cycle Management for Sustainability: A Keyword Co-Occurrence Network Review and Analysis," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12233-:d:926422
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    References listed on IDEAS

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    1. Chenxi Yuan & Guoyan Li & Sagar Kamarthi & Xiaoning Jin & Mohsen Moghaddam, 2022. "Trends in intelligent manufacturing research: a keyword co-occurrence network based review," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 425-439, February.
    2. van Staden, Heletjé E. & Deprez, Laurens & Boute, Robert N., 2022. "A dynamic “predict, then optimize” preventive maintenance approach using operational intervention data," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1079-1096.
    3. El-Akruti, Khaled & Dwight, Richard & Zhang, Tieling, 2013. "The strategic role of Engineering Asset Management," International Journal of Production Economics, Elsevier, vol. 146(1), pages 227-239.
    4. Selen Onel & Abe Zeid & Sagar Kamarthi, 2011. "The structure and analysis of nanotechnology co-author and citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 119-138, October.
    5. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    6. Hsin-Ning Su & Pei-Chun Lee, 2010. "Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 65-79, October.
    7. Kamble, Sachin S & Gunasekaran, Angappa & Parekh, Harsh & Mani, Venkatesh & Belhadi, Amine & Sharma, Rohit, 2022. "Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    8. Aleksandra Kuzior & Mariya Sira, 2022. "A Bibliometric Analysis of Blockchain Technology Research Using VOSviewer," Sustainability, MDPI, vol. 14(13), pages 1-15, July.
    9. Li, Huajiao & An, Haizhong & Wang, Yue & Huang, Jiachen & Gao, Xiangyun, 2016. "Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 657-669.
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    1. Małgorzata Jasiulewicz-Kaczmarek & Katarzyna Antosz & Chao Zhang & Vitalii Ivanov, 2023. "Industry 4.0 Technologies for Sustainable Asset Life Cycle Management," Sustainability, MDPI, vol. 15(7), pages 1-7, March.

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