IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v16y2025i1d10.1007_s13132-024-02001-z.html
   My bibliography  Save this article

AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency

Author

Listed:
  • Zhijuan Zong

    (Fuyang Normal University)

  • Yu Guan

    (Fuyang Normal University)

Abstract

In the era of Industry 4.0, integrating digital technologies into industrial processes has become imperative for sustaining growth and fostering innovation. This research paper explores the profound impact of AI-driven intelligent data analytics and predictive analysis on economic efficiency and managerial practices within Industry 4.0. With a focus on knowledge, innovation, technology, and society, this study delves into the transformative potential of these advanced technologies. Intelligent data analytics, powered by artificial intelligence (AI), has revolutionized the way industries harness vast datasets. Uncovering real-time patterns, correlations, and opportunities empowers decision-makers with accurate and timely insights. Predictive analysis, rooted in statistics and machine learning, aids in forecasting trends and managing risks, offering economic stability across sectors. Using a mixed-methods approach, the study combines qualitative interviews with 19 Chinese operations managers and quantitative data from an online survey of 286 managers. The study ranks various Industry 4.0 technologies through ordinal regression based on their impact on environmental sustainability and economic management. Results show that smart sensors, radio-frequency identification, AI, and analytics are the most influential technologies for enhancing economic and environmental outcomes. Conversely, technologies like additive manufacturing and automated robots yield less favorable results. The study also identifies a noticeable gap in professionals’ understanding and adoption of AI and augmented reality. Environmental concerns around the disposal of electronic waste generated by these technologies are also highlighted. The research thus offers significant insights for companies seeking to adopt intelligent data analytics to enhance economic performance and environmental sustainability. On the managerial front, the fusion of these technologies enables agile and responsive frameworks, promoting dynamic strategies in response to changing market dynamics. This culture of continual improvement fosters excellence and foresight in managerial processes. However, challenges exist, including the underutilization of data, data complexity, historical biases, and the need for tailored AI solutions across industries. Ethical considerations, data privacy, and security also pose concerns. Collaborative innovation among stakeholders is crucial to addressing these challenges and seizing opportunities. Governments, academia, and industry players must collaborate to develop technologically advanced, economically viable, and socially responsible solutions. As industries transition to Industry 4.0, this paper advocates a critical approach that embraces technology’s potential while mitigating risks. The future lies in a technologically advanced, economically resilient, and socially inclusive industrial landscape driven by AI-powered knowledge and innovation.

Suggested Citation

  • Zhijuan Zong & Yu Guan, 2025. "AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 864-903, March.
    • Handle: RePEc:spr:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-02001-z
    DOI: 10.1007/s13132-024-02001-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-024-02001-z
    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/s13132-024-02001-z?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Nishant, Rohit & Kennedy, Mike & Corbett, Jacqueline, 2020. "Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda," International Journal of Information Management, Elsevier, vol. 53(C).
    2. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    3. Dwivedi, Yogesh K. & Hughes, Laurie & Ismagilova, Elvira & Aarts, Gert & Coombs, Crispin & Crick, Tom & Duan, Yanqing & Dwivedi, Rohita & Edwards, John & Eirug, Aled & Galanos, Vassilis & Ilavarasan, , 2021. "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International Journal of Information Management, Elsevier, vol. 57(C).
    4. Davenport, Thomas H., 2018. "The AI Advantage: How to Put the Artificial Intelligence Revolution to Work," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262039176, December.
    5. Lynn Wu & Lorin Hitt & Bowen Lou, 2020. "Data Analytics, Innovation, and Firm Productivity," Management Science, INFORMS, vol. 66(5), pages 2017-2039, May.
    6. Ashok, Mona & Madan, Rohit & Joha, Anton & Sivarajah, Uthayasankar, 2022. "Ethical framework for Artificial Intelligence and Digital technologies," International Journal of Information Management, Elsevier, vol. 62(C).
    7. Zhencheng Fan & Zheng Yan & Shiping Wen, 2023. "Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    8. Liu, Hui & Zhu, Qirong & Muhammad Khoso, Wali & Khalique Khoso, Abdul, 2023. "Spatial pattern and the development of green finance trends in China," Renewable Energy, Elsevier, vol. 211(C), pages 370-378.
    9. Motoki, Kosuke & Pathak, Abhishek, 2022. "Articulatory global branding: Generalizability, modulators, and mechanisms of the in-out effect in non-WEIRD consumers," Journal of Business Research, Elsevier, vol. 149(C), pages 231-239.
    10. Muñoz, Fernando, 2021. "Carbon-intensive industries in Socially Responsible mutual funds' portfolios," International Review of Financial Analysis, Elsevier, vol. 75(C).
    11. Dalenogare, Lucas Santos & Benitez, Guilherme Brittes & Ayala, Néstor Fabián & Frank, Alejandro Germán, 2018. "The expected contribution of Industry 4.0 technologies for industrial performance," International Journal of Production Economics, Elsevier, vol. 204(C), pages 383-394.
    12. Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.
    13. Arpita Chari & Denis Niedenzu & Mélanie Despeisse & Carla Gonçalves Machado & João Domingues Azevedo & Rui Boavida‐Dias & Björn Johansson, 2022. "Dynamic capabilities for circular manufacturing supply chains—Exploring the role of Industry 4.0 and resilience," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2500-2517, July.
    14. Areej Althabatah & Mohammed Yaqot & Brenno Menezes & Laoucine Kerbache, 2023. "Transformative Procurement Trends: Integrating Industry 4.0 Technologies for Enhanced Procurement Processes," Logistics, MDPI, vol. 7(3), pages 1-40, September.
    15. Kristoffersen, Eivind & Mikalef, Patrick & Blomsma, Fenna & Li, Jingyue, 2021. "The effects of business analytics capability on circular economy implementation, resource orchestration capability, and firm performance," International Journal of Production Economics, Elsevier, vol. 239(C).
    16. Rafael Martínez-Peláez & Alberto Ochoa-Brust & Solange Rivera & Vanessa G. Félix & Rodolfo Ostos & Héctor Brito & Ramón A. Félix & Luis J. Mena, 2023. "Role of Digital Transformation for Achieving Sustainability: Mediated Role of Stakeholders, Key Capabilities, and Technology," Sustainability, MDPI, vol. 15(14), pages 1-27, July.
    17. Zahra, Shaker A. & Bogner, William C., 2000. "Technology strategy and software new ventures' performance: Exploring the moderating effect of the competitive environment," Journal of Business Venturing, Elsevier, vol. 15(2), pages 135-173, March.
    18. Ranjan, Jayanthi & Foropon, Cyril, 2021. "Big Data Analytics in Building the Competitive Intelligence of Organizations," International Journal of Information Management, Elsevier, vol. 56(C).
    19. Christian Voegtlin & Andreas Georg Scherer & Günter K Stahl & Olga Hawn, 2022. "Grand Societal Challenges and Responsible Innovation," Post-Print hal-03466563, HAL.
    20. Christian Voegtlin & Andreas Georg Scherer & Günter K. Stahl & Olga Hawn, 2022. "Grand Societal Challenges and Responsible Innovation," Journal of Management Studies, Wiley Blackwell, vol. 59(1), pages 1-28, January.
    21. Zaighum, Isma & Aman, Ameenullah & Sharif, Arshian & Suleman, Muhammad Tahir, 2021. "Do energy prices interact with global Islamic stocks? Fresh insights from quantile ARDL approach," Resources Policy, Elsevier, vol. 72(C).
    22. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    23. Naoki Makimoto & Ryuta Takashima, 2023. "Capacity Market and Investments in Power Generations: Risk-Averse Decision-Making of Power Producer," Energies, MDPI, vol. 16(10), pages 1-19, May.
    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. Dalia Štreimikienė & Ahmad Bathaei & Justas Streimikis, 2025. "Enhancing Sustainable Global Supply Chain Performance: A Multi-Criteria Decision-Making-Based Approach to Industry 4.0 and AI Integration," Sustainability, MDPI, vol. 17(10), pages 1-19, May.

    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. Wong, David T.W. & Ngai, Eric W.T., 2023. "The impact of advanced manufacturing technology, sensing and analytics capabilities, and planning comprehensiveness on sustained competitive advantage: The moderating role of environmental uncertainty," International Journal of Production Economics, Elsevier, vol. 265(C).
    2. Moinak Maiti & Parthajit Kayal & Aleksandra Vujko, 2025. "A study on ethical implications of artificial intelligence adoption in business: challenges and best practices," Future Business Journal, Springer, vol. 11(1), pages 1-12, December.
    3. Akter, Shahriar & Dwivedi, Yogesh K. & Sajib, Shahriar & Biswas, Kumar & Bandara, Ruwan J. & Michael, Katina, 2022. "Algorithmic bias in machine learning-based marketing models," Journal of Business Research, Elsevier, vol. 144(C), pages 201-216.
    4. Mohammadreza Akbari & John L. Hopkins, 2022. "Digital technologies as enablers of supply chain sustainability in an emerging economy," Operations Management Research, Springer, vol. 15(3), pages 689-710, December.
    5. Moustafa Elnadi & Yasser Omar Abdallah, 2024. "Industry 4.0: critical investigations and synthesis of key findings," Management Review Quarterly, Springer, vol. 74(2), pages 711-744, June.
    6. Kyoko Sasaki & Wendy Stubbs & Megan Farrelly, 2023. "The relationship between corporate purpose and the sustainable development goals in large Japanese companies," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 30(5), pages 2475-2489, September.
    7. Mohsen Farhadloo & Saman Asvadi & Kash Khorasani, 2025. "Potential vulnerabilities associated with emerging technologies: insights from a systematic literature review," Management Review Quarterly, Springer, vol. 75(2), pages 1717-1760, June.
    8. Qiuyue Shao & Xiaoping Zhao & Shouming Chen & Jing Zhao, 2025. "CEO Pay Caps, Political Promotion Incentives, and Green Innovation: Evidence from Chinese Publicly Listed Firms," Sustainability, MDPI, vol. 17(12), pages 1-21, June.
    9. Mao, Qian & Li, Yilong, 2024. "Blockchain evolution, artificial intelligence and ferrous metal trade," Resources Policy, Elsevier, vol. 98(C).
    10. Jacobsen, Lina Fogt & Frank, Darius-Aurel & Søndergaard, Helle Alsted & Peschel, Anne Odile, 2025. "From diversity to distinction: Exploring the role of diversity and inclusion related to consumer perceptions of company innovativeness and attractiveness," Journal of Business Research, Elsevier, vol. 189(C).
    11. Alexandra RADU & Mihaela HERCIU, 2025. "Data Analytics, Decision-Making Process And Business Performance: A Bibliometric Analysis," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 20(2), pages 292-313, August.
    12. Ika, Lavagnon A. & Locatelli, Giorgio & Drouin, Nathalie, 2024. "Policy-driven projects: Empowering the world to confront grand challenges," European Management Journal, Elsevier, vol. 42(6), pages 835-842.
    13. Dinh, Jeannette Mai & Isaak, Andrew Jay & Yahyaoui, Yasmine, 2024. "Investing for good – Uncovering crowd investors' motivations to participate in sustainability-oriented crowdlending," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 207, pages 1-15.
    14. Zhang, Cong & Yang, Jianhua, 2024. "Artificial intelligence and corporate ESG performance," International Review of Economics & Finance, Elsevier, vol. 96(PC).
    15. Issa Helmi & Lakkis Hussein & Dakroub Roy & Jaber Jad, 2023. "Examining User Engagement and Experience in Agritech," International Journal of Contemporary Management, Sciendo, vol. 59(2), pages 17-32, June.
    16. Joan Torrent‐Sellens & Pilar Ficapal‐Cusí & Mihaela Enache‐Zegheru, 2023. "Boosting environmental management: The mediating role of Industry 4.0 between environmental assets and economic and social firm performance," Business Strategy and the Environment, Wiley Blackwell, vol. 32(1), pages 753-768, January.
    17. Panizzon, Mateus & Janissek-Muniz, Raquel, 2025. "Theoretical dimensions for integrating research on anticipatory governance, scientific foresight and sustainable S&T public policy design," Technology in Society, Elsevier, vol. 80(C).
    18. Abou-Foul, Mohamad & Ruiz-Alba, Jose L. & López-Tenorio, Pablo J., 2023. "The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective," Journal of Business Research, Elsevier, vol. 157(C).
    19. Haoyang Wu & Jing Liu & Biming Liang, 2025. "AI-Driven Supply Chain Transformation in Industry 5.0: Enhancing Resilience and Sustainability," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 3826-3868, March.
    20. Tian, Meng & Chen, Yang & Tian, Guanghao & Huang, Wei & Hu, Chuan, 2023. "The role of digital transformation practices in the operations improvement in manufacturing firms: A practice-based view," International Journal of Production Economics, Elsevier, vol. 262(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-02001-z. 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.