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

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  • 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
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    References listed on IDEAS

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    1. Ranjan, Jayanthi & Foropon, Cyril, 2021. "Big Data Analytics in Building the Competitive Intelligence of Organizations," International Journal of Information Management, Elsevier, vol. 56(C).
    2. 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).
    3. Christian Voegtlin & Andreas Georg Scherer & Günter K Stahl & Olga Hawn, 2022. "Grand Societal Challenges and Responsible Innovation," Post-Print hal-03466563, HAL.
    4. 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).
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. Muñoz, Fernando, 2021. "Carbon-intensive industries in Socially Responsible mutual funds' portfolios," International Review of Financial Analysis, Elsevier, vol. 75(C).
    10. 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).
    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. Lynn Wu & Lorin Hitt & Bowen Lou, 2020. "Data Analytics, Innovation, and Firm Productivity," Management Science, INFORMS, vol. 66(5), pages 2017-2039, May.
    14. 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).
    15. 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).
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
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