IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v16y2025i1d10.1007_s13132-024-02120-7.html
   My bibliography  Save this article

The Confluence of AI and Big Data Analytics in Industry 4.0: Fostering Sustainable Strategic Development

Author

Listed:
  • Mengze Zheng

    (Ningbo University of Finance & Economics
    Dongbei University of Finance & Economics)

  • Te Li

    (Dongbei University of Finance & Economics)

  • Jing Ye

    (Ningbo University of Finance & Economics)

Abstract

In the era of Industry 4.0, the convergence of digitalization, artificial intelligence (AI), and big data analytics (BDA) has revolutionized the industrial landscape. This research paper explores the intricate interplay between AI-assisted BDA and strategic sustainable development (SSD) within the context of Industry 4.0, focusing on Italian manufacturing. The Fourth Industrial Revolution, characterized by the fusion of physical and digital worlds, has ushered in unprecedented advancements in technology and data analytics. AI-assisted BDA emerges as a powerful tool for harnessing vast datasets to drive innovation, efficiency, and sustainability strategies. It facilitates predictive maintenance, waste reduction, and optimal resource utilization. Furthermore, it aligns with circular economy principles by optimizing resource flows and promoting responsible sourcing. However, this digital transformation is not without challenges. Data privacy, security concerns, and the digital divide need addressing. Multidisciplinary approaches involving policymakers, academics, and industry leaders are essential to establish ethical norms and ensure inclusivity. The study’s theoretical implications call for a re-evaluation of the existing frameworks surrounding BDA’s role in sustainable development. It highlights the symbiotic relationship between digitization, BDA, and SSD, emphasizing the mediating role of environmental and social governance (ESG). This research underscores the transformative potential of AI-assisted BDA in Industry 4.0, bridging the gap between sustainability and innovation. It paves the way for a future where technology and environmental stewardship coalesce to foster a greener, more sustainable industrial landscape, redefining industry and sustainability in the digital age.

Suggested Citation

  • Mengze Zheng & Te Li & Jing Ye, 2025. "The Confluence of AI and Big Data Analytics in Industry 4.0: Fostering Sustainable Strategic Development," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 5479-5515, March.
  • Handle: RePEc:spr:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-02120-7
    DOI: 10.1007/s13132-024-02120-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-024-02120-7
    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/s13132-024-02120-7?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. Abdulaziz Aldoseri & Khalifa N. Al-Khalifa & Abdel Magid Hamouda, 2024. "AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact," Sustainability, MDPI, vol. 16(5), pages 1-25, February.
    2. Chandan K. Sahu & Crystal Young & Rahul Rai, 2021. "Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review," International Journal of Production Research, Taylor & Francis Journals, vol. 59(16), pages 4903-4959, August.
    3. Morteza Ghobakhloo & Mohammad Iranmanesh & Manuel E. Morales & Mehrbakhsh Nilashi & Azlan Amran, 2023. "Actions and approaches for enabling Industry 5.0‐driven sustainable industrial transformation: A strategy roadmap," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 30(3), pages 1473-1494, May.
    4. Ricci, Riccardo & Battaglia, Daniele & Neirotti, Paolo, 2021. "External knowledge search, opportunity recognition and industry 4.0 adoption in SMEs," International Journal of Production Economics, Elsevier, vol. 240(C).
    5. Yu, Wantao & Wong, Chee Yew & Chavez, Roberto & Jacobs, Mark A., 2021. "Integrating big data analytics into supply chain finance: The roles of information processing and data-driven culture," International Journal of Production Economics, Elsevier, vol. 236(C).
    6. Muhieddine Ramadan & Najib Bou Zakhem & Hala Baydoun & Amira Daouk & Samia Youssef & Abir El Fawal & Jean Elia & Ahmad Ashaal, 2023. "Toward Digital Transformation and Business Model Innovation: The Nexus between Leadership, Organizational Agility, and Knowledge Transfer," Administrative Sciences, MDPI, vol. 13(8), pages 1-21, August.
    7. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    8. Bag, Surajit & Dhamija, Pavitra & Singh, Rajesh Kumar & Rahman, Muhammad Sabbir & Sreedharan, V. Raja, 2023. "Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: An empirical study," Journal of Business Research, Elsevier, vol. 154(C).
    9. Som Sekhar Bhattacharyya & Debojit Maitra & Subhamay Deb, 2021. "Study of Adoption and Absorption of Emerging Technologies for Smart Supply Chain Management: A Dynamic Capabilities Perspective," International Journal of Applied Logistics (IJAL), IGI Global, vol. 11(2), pages 14-54, July.
    10. Ida Merete Enholm & Emmanouil Papagiannidis & Patrick Mikalef & John Krogstie, 2022. "Artificial Intelligence and Business Value: a Literature Review," Information Systems Frontiers, Springer, vol. 24(5), pages 1709-1734, October.
    11. Treviño, Linda Klebe & Butterfield, Kenneth D. & McCabe, Donald L., 1998. "The Ethical Context in Organizations: Influences on Employee Attitudes and Behaviors," Business Ethics Quarterly, Cambridge University Press, vol. 8(3), pages 447-476, July.
    12. Georgios Georgiadis & Geert Poels, 2021. "Enterprise architecture management as a solution for addressing general data protection regulation requirements in a big data context: a systematic mapping study," Information Systems and e-Business Management, Springer, vol. 19(1), pages 313-362, March.
    13. Ting Zheng & Marco Ardolino & Andrea Bacchetti & Marco Perona, 2021. "The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 59(6), pages 1922-1954, March.
    14. Lutfi, Abdalwali & Alrawad, Mahmaod & Alsyouf, Adi & Almaiah, Mohammed Amin & Al-Khasawneh, Ahmad & Al-Khasawneh, Akif Lutfi & Alshira'h, Ahmad Farhan & Alshirah, Malek Hamed & Saad, Mohamed & Ibrahim, 2023. "Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    15. 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.
    16. Aleš Popovič & Ray Hackney & Rana Tassabehji & Mauro Castelli, 2018. "The impact of big data analytics on firms’ high value business performance," Information Systems Frontiers, Springer, vol. 20(2), pages 209-222, April.
    17. Abdullah Jihad Rabaya & Norman Mohd Saleh, 2022. "The moderating effect of IR framework adoption on the relationship between environmental, social, and governance (ESG) disclosure and a firm's competitive advantage," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2037-2055, February.
    18. Dmitry Ivanov & Christopher S. Tang & Alexandre Dolgui & Daria Battini & Ajay Das, 2021. "Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 2055-2078, April.
    19. Akter, Shahriar & Hossain, Md Afnan & Sajib, Shahriar & Sultana, Saida & Rahman, Mahfuzur & Vrontis, Demetris & McCarthy, Grace, 2023. "A framework for AI-powered service innovation capability: Review and agenda for future research," Technovation, Elsevier, vol. 125(C).
    20. Samira Ranaei & Arho Suominen & Alan Porter & Stephen Carley, 2020. "Evaluating technological emergence using text analytics: two case technologies and three approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 215-247, January.
    21. Patrucco, Andrea S. & Marzi, Giacomo & Trabucchi, Daniel, 2023. "The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions," Technovation, Elsevier, vol. 126(C).
    22. Luca Marrucci & Fabio Iannone & Tiberio Daddi & Fabio Iraldo, 2022. "Antecedents of absorptive capacity in the development of circular economy business models of small and medium enterprises," Business Strategy and the Environment, Wiley Blackwell, vol. 31(1), pages 532-544, January.
    23. Saumyaranjan Sahoo & Anil Kumar & Arvind Upadhyay, 2023. "How do green knowledge management and green technology innovation impact corporate environmental performance? Understanding the role of green knowledge acquisition," Business Strategy and the Environment, Wiley Blackwell, vol. 32(1), pages 551-569, January.
    24. Zheng, Ting & Ardolino, Marco & Bacchetti, Andrea & Perona, Marco, 2021. "The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 129469, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    25. Helen Onyeaka & Phemelo Tamasiga & Uju Mary Nwauzoma & Taghi Miri & Uche Chioma Juliet & Ogueri Nwaiwu & Adenike A. Akinsemolu, 2023. "Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    26. Sachin S. Kamble & Angappa Gunasekaran, 2020. "Big data-driven supply chain performance measurement system: a review and framework for implementation," International Journal of Production Research, Taylor & Francis Journals, vol. 58(1), pages 65-86, January.
    27. Pandey, Dharen Kumar & Hunjra, Ahmed Imran & Bhaskar, Ratikant & Al-Faryan, Mamdouh Abdulaziz Saleh, 2023. "Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022," Resources Policy, Elsevier, vol. 86(PA).
    28. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    29. Tino T. Herden, 2020. "Explaining the competitive advantage generated from Analytics with the knowledge-based view: the example of Logistics and Supply Chain Management," Business Research, Springer;German Academic Association for Business Research, vol. 13(1), pages 163-214, April.
    30. Aggarwal, Rajesh K. & Evans, Mark E. & Nanda, Dhananjay, 2012. "Nonprofit boards: Size, performance and managerial incentives," Journal of Accounting and Economics, Elsevier, vol. 53(1), pages 466-487.
    31. Pereira, Carla Roberta & Lago da Silva, Andrea & Tate, Wendy Lea & Christopher, Martin, 2020. "Purchasing and supply management (PSM) contribution to supply-side resilience," International Journal of Production Economics, Elsevier, vol. 228(C).
    32. Ranjan, Jayanthi & Foropon, Cyril, 2021. "Big Data Analytics in Building the Competitive Intelligence of Organizations," International Journal of Information Management, Elsevier, vol. 56(C).
    33. Zhuming Bi & Yan Jin & Paul Maropoulos & Wen-Jun Zhang & Lihui Wang, 2023. "Internet of things (IoT) and big data analytics (BDA) for digital manufacturing (DM)," International Journal of Production Research, Taylor & Francis Journals, vol. 61(12), pages 4004-4021, June.
    34. Zhu, Suning & Dong, Tianxi & Luo, Xin (Robert), 2021. "A longitudinal study of the actual value of big data and analytics: The role of industry environment," International Journal of Information Management, Elsevier, vol. 60(C).
    35. Bilal Abu-Salih & Pornpit Wongthongtham & Dengya Zhu & Kit Yan Chan & Amit Rudra, 2021. "Predictive Analytics Using Social Big Data and Machine Learning," Springer Books, in: Social Big Data Analytics, chapter 0, pages 113-143, Springer.
    36. Gorddard, Russell & Colloff, Matthew J. & Wise, Russell M. & Ware, Dan & Dunlop, Michael, 2016. "Values, rules and knowledge: Adaptation as change in the decision context," Environmental Science & Policy, Elsevier, vol. 57(C), pages 60-69.
    Full references (including those not matched with items on IDEAS)

    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. Govindan, Kannan & Kannan, Devika & Jørgensen, Thomas Ballegård & Nielsen, Tim Straarup, 2022. "Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    2. Yang, Li & Zou, Haobo & Shang, Chao & Ye, Xiaoming & Rani, Pratibha, 2023. "Adoption of information and digital technologies for sustainable smart manufacturing systems for industry 4.0 in small, medium, and micro enterprises (SMMEs)," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    3. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    4. Patrucco, Andrea S. & Marzi, Giacomo & Trabucchi, Daniel, 2023. "The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions," Technovation, Elsevier, vol. 126(C).
    5. Zhao, Guoqing & Xie, Xiaotian & Wang, Yi & Liu, Shaofeng & Jones, Paul & Lopez, Carmen, 2024. "Barrier analysis to improve big data analytics capability of the maritime industry: A mixed-method approach," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    6. Battaglia, Daniele & Galati, Francesco & Molinaro, Margherita & Pessot, Elena, 2023. "Full, hybrid and platform complementarity: Exploring the industry 4.0 technology-performance link," International Journal of Production Economics, Elsevier, vol. 263(C).
    7. Ranaboldo, M. & Aragüés-Peñalba, M. & Arica, E. & Bade, A. & Bullich-Massagué, E. & Burgio, A. & Caccamo, C. & Caprara, A. & Cimmino, D. & Domenech, B. & Donoso, I. & Fragapane, G. & González-Font-de-, 2024. "A comprehensive overview of industrial demand response status in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    8. Saraswat, Jeetendra Kumar & Choudhari, Sanjay, 2025. "Integrating big data and cloud computing into the existing system and performance impact: A case study in manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    9. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2025. "Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 61-93, January.
    10. Juhás Martin & Juhásová Bohuslava & Nemlaha Eduard & Charvát Dominik, 2021. "Increasing the Efficiency of a Robotic Cell Using Simulation," Research Papers Faculty of Materials Science and Technology Slovak University of Technology, Sciendo, vol. 29(49), pages 24-35, September.
    11. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    12. Jože M. Rožanec & Luka Bizjak & Elena Trajkova & Patrik Zajec & Jelle Keizer & Blaž Fortuna & Dunja Mladenić, 2024. "Active learning and novel model calibration measurements for automated visual inspection in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(5), pages 1963-1984, June.
    13. Mahdi Mokhtarzadeh & Jorge Rodríguez-Echeverría & Ivana Semanjski & Sidharta Gautama, 2025. "Hybrid intelligence failure analysis for industry 4.0: a literature review and future prospective," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2309-2334, April.
    14. Moazzeni, Sahar & Sgarbossa, Fabio, 2025. "Collaborative logistics and digital technologies in rural contexts: a systematic review and a decision aid model for logistics decision-makers," Discussion Papers 2025/12, Norwegian School of Economics, Department of Business and Management Science.
    15. Md Mehedi Hasan Emon & Golam Mustafa MD. Nurullah Rabbani & Avishek Nath, 2023. "Challenges And Opportunities In The Implementation Of Big Data Analytics In Management Information Systems In Bangladesh," Acta Informatica Malaysia (AIM), Zibeline International Publishing, vol. 7(2), pages 122-130, September.
    16. Luo, Shiyue & Yu, Mengyao & Dong, Yilan & Hao, Yu & Li, Changping & Wu, Haitao, 2024. "Toward urban high-quality development: Evidence from more intelligent Chinese cities," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    17. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    18. repec:osf:osfxxx:9auec_v1 is not listed on IDEAS
    19. Aljumah, Ahmad Ibrahim & Nuseir, Mohammed T. & Alam, Md. Mahmudul, 2021. "Traditional Marketing Analytics, Big Data Analytics, Big Data System Quality and the Success of New Product Development," OSF Preprints 9auec, Center for Open Science.
    20. Haodong Chen & Niloofar Zendehdel & Ming C. Leu & Zhaozheng Yin, 2024. "Fine-grained activity classification in assembly based on multi-visual modalities," Journal of Intelligent Manufacturing, Springer, vol. 35(5), pages 2215-2233, June.
    21. Showimy Aldossari & Umi Asma’ Mokhtar & Ahmad Tarmizi Abdul Ghani, 2023. "Factor Influencing the Adoption of Big Data Analytics: A Systematic Literature and Experts Review," SAGE Open, , vol. 13(4), pages 21582440231, December.

    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_s13132-024-02120-7. 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.