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From technological development to social advance: A review of Industry 4.0 through machine learning

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  • Lee, Changhun
  • Lim, Chiehyeon

Abstract

Industry 4.0 has attracted considerable interest from firms, governments, and individuals as the new concept of future computer, industrial, and social systems. However, the concept has yet to be fully explored in the scientific literature. Given the topic's broad scope, this work attempts to understand and clarify Industry 4.0 by analyzing 660 journal papers and 3,901 news articles through text mining with unsupervised machine learning algorithms. Based on the results, this work identifies 31 research and application issues related to Industry 4.0. These issues are categorized and described within a five-level hierarchy: 1) infrastructure development for connection, 2) artificial intelligence development for data-driven decision making, 3) system and process optimization, 4) industrial innovation, and 5) social advance. Further, a framework for convergence in Industry 4.0 is proposed, featuring six dimensions: connection, collection, communication, computation, control, and creation. The research outcomes are consistent with and complementary to existing relevant discussion and debate on Industry 4.0, which validates the utility and efficiency of the data-driven approach of this work to support experts’ insights on Industry 4.0. This work helps establish a common ground for understanding Industry 4.0 across multiple disciplinary perspectives, enabling further research and development for industrial innovation and social advance.

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  • Lee, Changhun & Lim, Chiehyeon, 2021. "From technological development to social advance: A review of Industry 4.0 through machine learning," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:tefoso:v:167:y:2021:i:c:s0040162521000858
    DOI: 10.1016/j.techfore.2021.120653
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    4. Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.
    5. Danilo Ferreira de Souza & Emeli Lalesca Aparecida da Guarda & Welitom Ttatom Pereira da Silva & Ildo Luis Sauer & Hédio Tatizawa, 2022. "Perspectives on the Advancement of Industry 4.0 Technologies Applied to Water Pumping Systems: Trends in Building Pumps," Energies, MDPI, vol. 15(9), pages 1-17, May.
    6. Tariq Al Amri & Katalin Puskas Khetani & Manuel Marey-Perez, 2021. "Towards Sustainable I4.0: Key Skill Areas for Project Managers in GCC Construction Industry," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    7. Grybauskas, Andrius & Stefanini, Alessandro & Ghobakhloo, Morteza, 2022. "Social sustainability in the age of digitalization: A systematic literature Review on the social implications of industry 4.0," Technology in Society, Elsevier, vol. 70(C).
    8. Núñez-Merino, Miguel & Maqueira-Marín, Juan Manuel & Moyano-Fuentes, José & Castaño-Moraga, Carlos Alberto, 2022. "Industry 4.0 and supply chain. A Systematic Science Mapping analysis," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    9. Bootz, Jean-Philippe & Michel, Sophie & Pallud, Jessie & Monti, Régine, 2022. "Possible changes of Industry 4.0 in 2030 in the face of uberization: Results of a participatory and systemic foresight study," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    10. Natalia A. Kosolapova & Ludmila G. Matveeva & Anastasia Y. Nikitaeva & Lesego Molapisi, 2021. "The Rational Use of Water Resources in the Strategy of Industry 4.0," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 3023-3041, July.
    11. Yan, Zheming & Sun, Zao & Shi, Rui & Zhao, Minjuan, 2023. "Smart city and green development: Empirical evidence from the perspective of green technological innovation," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
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    13. Laubengaier, Désirée A. & Cagliano, Raffaella & Canterino, Filomena, 2022. "It Takes Two to Tango: Analyzing the Relationship between Technological and Administrative Process Innovations in Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 180(C).

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