IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v26y2024i5d10.1007_s10796-021-10213-w.html
   My bibliography  Save this article

Emerging Enabling Technologies for Industry 4.0 and Beyond

Author

Listed:
  • Alexander Sigov

    (Old Dominion University)

  • Leonid Ratkin

    (Russian Technological University)

  • Leonid A. Ivanov

    (Russian Academy of Sciences)

  • Li Da Xu

    (Russian Academy of Engineering)

Abstract

Rapid advances in technology have spurred tremendous progress in developing the next generation of Industry 4.0 that was initially introduced in 2011 as a German strategic initiative for revolutionizing the manufacturing sector. Ten years have passed since 2011. In these ten years, numerous new and promising technologies and applications have been developed. The original concept of Industry 4.0, including the conceptual framework, technology framework, and enabling technologies, has experienced tremendous changes. As such, the new generation of Industry 4.0 emerges, which is also called Industry 5.0. Today, we are on the cusp of the Industry 4.0 evolution supported by a new set of enabling technologies. In such evolution of Industry 4.0, future Industry 4.0 requires a combination of recently emerging new technologies, which is giving rise to the emergence of the next generation of Industry 4.0 or Industry 5.0. Such technologies originate from different disciplines, including Artificial Intelligence (AI), 5G/6G, Quantum Computing, and others. The technologies in the original Industry 4.0 framework, such as Cyber-Physical Systems, IoT, etc., will be affected by Artificial Intelligence (AI), 5G/6G, and Quantum Computing. At this present moment, the emergence of a new era of Industry 4.0 can be seen. In this paper, we briefly survey the main emerging enabling technologies in Industry 4.0 as it relates to industries.

Suggested Citation

  • Alexander Sigov & Leonid Ratkin & Leonid A. Ivanov & Li Da Xu, 2024. "Emerging Enabling Technologies for Industry 4.0 and Beyond," Information Systems Frontiers, Springer, vol. 26(5), pages 1585-1595, October.
  • Handle: RePEc:spr:infosf:v:26:y:2024:i:5:d:10.1007_s10796-021-10213-w
    DOI: 10.1007/s10796-021-10213-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-021-10213-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10796-021-10213-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang Lu, 2019. "Artificial intelligence: a survey on evolution, models, applications and future trends," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(1), pages 1-29, January.
    2. Grzegorz Mazurek & Karolina Małagocka, 2019. "Perception of privacy and data protection in the context of the development of artificial intelligence," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(4), pages 344-364, October.
    3. Ling Li, 2020. "Education supply chain in the era of Industry 4.0," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 579-592, July.
    4. Ruixi Yuan & Zhu Li & Xiaohong Guan & Li Xu, 2010. "An SVM-based machine learning method for accurate internet traffic classification," Information Systems Frontiers, Springer, vol. 12(2), pages 149-156, April.
    5. K. S. Law & Fu-Lai Chung, 2020. "Knowledge-driven decision analytics for commercial banking," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(2), pages 209-230, April.
    6. Kenneth Tung, 2019. "AI, the internet of legal things, and lawyers," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(4), pages 390-403, October.
    7. Kuo Chi-Hsien & Shinya Nagasawa, 2019. "Applying machine learning to market analysis: Knowing your luxury consumer," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(4), pages 404-419, October.
    8. Hong Chen & Ling Li & Yong Chen, 2021. "Explore success factors that impact artificial intelligence adoption on telecom industry in China," Journal of Management Analytics, Taylor & Francis Journals, vol. 8(1), pages 36-68, January.
    9. Binaya Kumar Panigrahi & Tushar Kumar Nath & Manas Ranjan Senapati, 2019. "An application of local linear radial basis function neural network for flood prediction," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(1), pages 67-87, January.
    10. Michael Haenlein & Andreas Kaplan & Chee-Wee Tan & Pengzhu Zhang, 2019. "Artificial intelligence (AI) and management analytics," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(4), pages 341-343, October.
    11. Kanchan Pradhan & Priyanka Chawla, 2020. "Medical Internet of things using machine learning algorithms for lung cancer detection," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(4), pages 591-623, October.
    12. Dheeraj Malhotra & O. P. Rishi, 2019. "A comprehensive review from hyperlink to intelligent technologies based personalized search systems," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(4), pages 365-389, October.
    13. Yue Kang & Zhao Cai & Chee-Wee Tan & Qian Huang & Hefu Liu, 2020. "Natural language processing (NLP) in management research: A literature review," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(2), pages 139-172, April.
    14. Li, Hong-Xing & Da, Xu Li, 2000. "A neural network representation of linear programming," European Journal of Operational Research, Elsevier, vol. 124(2), pages 224-234, July.
    15. A. Kullaya Swamy & B. Sarojamma, 2020. "Bank transaction data modeling by optimized hybrid machine learning merged with ARIMA," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(4), pages 624-648, October.
    16. Weiqiang Zhang & Yidan Xiang & Xiaohui Liu & Pengzhu Zhang, 2019. "Domain ontology development of knowledge base in cardiovascular personalized health management," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(4), pages 420-455, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ling Li, 2024. "Industry 4.0 and Beyond," Information Systems Frontiers, Springer, vol. 26(5), pages 1581-1583, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baoshan Ge & Liyi Zhao, 2022. "The impact of the integration of opportunity and resources of new ventures on entrepreneurial performance: The moderating role of BDAC‐AI," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 440-461, May.
    2. Hong Jiang & Jinlong Gai & Shukuan Zhao & Peggy E. Chaudhry & Sohail S. Chaudhry, 2022. "Applications and development of artificial intelligence system from the perspective of system science: A bibliometric review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 361-378, May.
    3. Xueling Li & Yujie Long & Meixi Fan & Yong Chen, 2022. "Drilling down artificial intelligence in entrepreneurial management: A bibliometric perspective," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 379-396, May.
    4. Baoshan Ge & Qi Wang & Meifang Yao, 2022. "From ideas to entrepreneurial opportunity: A study on AI," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 618-632, May.
    5. Meifang Yao & Dan Ye & Liyi Zhao, 2022. "The relationship between inbound open innovation and the innovative use of information technology by individuals in teams of start‐ups," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 503-515, May.
    6. Fang Wang, 2022. "AI‐enabled IT capability and organizational performance," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 609-617, May.
    7. Weifeng Jia & Shuo Wang & Yongping Xie & Zifeng Chen & Kaixin Gong, 2022. "Disruptive technology identification of intelligent logistics robots in AIoT industry: Based on attributes and functions analysis," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 557-568, May.
    8. Zimei Liu & Kefan Xie & Ling Li & Yong Chen, 2020. "A paradigm of safety management in Industry 4.0," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 632-645, July.
    9. Jing Ge & Feng Wang & Hongxia Sun & Liuliu Fu & Mingwei Sun, 2020. "Research on the maturity of big data management capability of intelligent manufacturing enterprise," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 646-662, July.
    10. Huishuang Su & Xintong Qu & Shuo Tian & Qiang Ma & Ling Li & Yong Chen, 2022. "Artificial intelligence empowerment: The impact of research and development investment on green radical innovation in high‐tech enterprises," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 489-502, May.
    11. Yu Sun & Yuming He & Haiqing Yu & Hecheng Wang, 2022. "An evaluation framework of IT‐enabled service‐oriented manufacturing," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 657-667, May.
    12. Hong Jiang & Shuyu Sun & Hongtao Xu & Shukuan Zhao & Yong Chen, 2020. "Enterprises' network structure and their technology standardization capability in Industry 4.0," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 749-765, July.
    13. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.
    14. Xueling Li & Xiaoyan Zhang & Yuan Liu & Yuanying Mi & Yong Chen, 2022. "The impact of artificial intelligence on users' entrepreneurial activities," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 597-608, May.
    15. Ting Hou & Baihua Cheng & Rongxiao Wang & Wei Xue & Peggy E. Chaudhry, 2020. "Developing Industry 4.0 with systems perspectives," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 741-748, July.
    16. Jianjing Qu & Yanan Zhao & Yongping Xie, 2022. "Artificial intelligence leads the reform of education models," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 581-588, May.
    17. Wei Zhang & Linhui Sun & Xinping Wang & Anbo Wu, 2022. "The influence of AI word‐of‐mouth system on consumers' purchase behaviour: The mediating effect of risk perception," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 516-530, May.
    18. Ling Li, 2024. "Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond," Information Systems Frontiers, Springer, vol. 26(5), pages 1697-1712, October.
    19. Shuo Tian & Hangeng Zhao & Xiaobo Xu & Rongchao Mu & Qiang Ma, 2022. "Knowledge chain integration of design structure matrix‐based project team: An integration model," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 462-473, May.
    20. Yongdang Chen & Zhiyou Han & Kunyu Cao & Xianrong Zheng & Xiaobo Xu, 2020. "Manufacturing upgrading in industry 4.0 era," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 766-771, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:26:y:2024:i:5:d:10.1007_s10796-021-10213-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.