IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v16y2025i1d10.1007_s13132-024-01999-6.html
   My bibliography  Save this article

AI-Driven Supply Chain Transformation in Industry 5.0: Enhancing Resilience and Sustainability

Author

Listed:
  • Haoyang Wu

    (Jilin University of Finance and Economics)

  • Jing Liu

    (Jilin University of Finance and Economics)

  • Biming Liang

    (Jilin University of Finance and Economics)

Abstract

The emergence of Industry 5.0 has ushered in a new era of industrial transformation characterized by the integration of physical and digital systems. In this dynamic environment, the role of artificial intelligence (AI) in supply chain management has become paramount. This research paper explores the impact of AI-driven imperatives on supply chain resilience and sustainability in Industry 5.0. Using a combination of probability analysis (PA), Bayesian-BWM (B-BWM), and Pareto analysis, we identify key AI-driven strategies for enhancing supply chain operations in the ready-made garment (RMG) and footwear sectors in China. The study highlights the importance of continuous monitoring of supply chain operations via the Internet of Things (IoT), adopting a circular production and packaging (CPP) model, and establishing a digital supply chain replica. These strategies improve resilience and contribute to environmental and societal outcomes. Automation and robust cybersecurity for data handling are crucial in the context of Industry 5.0, as they enhance production adaptability and data security. Also, streamlining inventory via RFID technology and harnessing AI to improve workforce safety and operational flow are essential measures. Comprehensive data analysis and forward-looking predictions through AI-driven big data analytics provide insights into consumer demand and energy consumption, ensuring efficient supply chain management. Lastly, while promising, blockchain integration poses challenges in terms of investment and regulatory compliance. However, it is important to consider the ethical implications and regulatory frameworks associated with AI deployment, as well as the need for education and training to bridge the digital gap. Collaboration between governments, industries, and educational institutions is essential to establish a comprehensive Industry 15.0 that benefits all segments of society. This research sheds light on the transformative potential of AI-driven supply chain management in Industry 5.0 and underscores the importance of addressing challenges to maximize its benefits.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-01999-6
    DOI: 10.1007/s13132-024-01999-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-024-01999-6
    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/s13132-024-01999-6?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. Barry Eichengreen, 2023. "Financial regulation in the age of the platform economy," Journal of Banking Regulation, Palgrave Macmillan, vol. 24(1), pages 40-50, March.
    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. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    4. Trevor Cadden & Denis Dennehy & Matti Mantymaki & Raymond Treacy, 2022. "Understanding the influential and mediating role of cultural enablers of AI integration to supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 60(14), pages 4592-4620, July.
    5. 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).
    6. Bonsón, Enrique & Lavorato, Domenica & Lamboglia, Rita & Mancini, Daniela, 2021. "Artificial intelligence activities and ethical approaches in leading listed companies in the European Union," International Journal of Accounting Information Systems, Elsevier, vol. 43(C).
    7. Chun-Liang Chen, 2019. "Value Creation by SMEs Participating in Global Value Chains under Industry 4.0 Trend: Case Study of Textile Industry in Taiwan," Journal of Global Information Technology Management, Taylor & Francis Journals, vol. 22(2), pages 120-145, April.
    8. Shweta Bajaj & Ruchi Garg & Monika Sethi, 2018. "Total quality management: a critical literature review using Pareto analysis," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 67(1), pages 128-154, January.
    9. Alice Villar & Stefania Paladini & Oliver Buckley, 2023. "Towards Supply Chain 5.0: Redesigning Supply Chains as Resilient, Sustainable, and Human-Centric Systems in a Post-pandemic World," SN Operations Research Forum, Springer, vol. 4(3), pages 1-46, September.
    10. Shahriar Akter & Katina Michael & Muhammad Rajib Uddin & Grace McCarthy & Mahfuzur Rahman, 2022. "Transforming business using digital innovations: the application of AI, blockchain, cloud and data analytics," Annals of Operations Research, Springer, vol. 308(1), pages 7-39, January.
    11. Jiang, Peng & Fan, Yee Van & Klemeš, Jiří Jaromír, 2021. "Impacts of COVID-19 on energy demand and consumption: Challenges, lessons and emerging opportunities," Applied Energy, Elsevier, vol. 285(C).
    12. Maria José Sousa & Daniela Wilks, 2018. "Sustainable Skills for the World of Work in the Digital Age," Systems Research and Behavioral Science, Wiley Blackwell, vol. 35(4), pages 399-405, July.
    13. Sumona Mukhuty & Arvind Upadhyay & Holly Rothwell, 2022. "Strategic sustainable development of Industry 4.0 through the lens of social responsibility: The role of human resource practices," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2068-2081, July.
    14. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    15. Delic, Mia & Eyers, Daniel R., 2020. "The effect of additive manufacturing adoption on supply chain flexibility and performance: An empirical analysis from the automotive industry," International Journal of Production Economics, Elsevier, vol. 228(C).
    16. Hong, Jiangtao & Liao, Yi & Zhang, Yibin & Yu, Zhefu, 2019. "The effect of supply chain quality management practices and capabilities on operational and innovation performance: Evidence from Chinese manufacturers," International Journal of Production Economics, Elsevier, vol. 212(C), pages 227-235.
    17. 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.
    18. Vincent, Vinod U., 2021. "Integrating intuition and artificial intelligence in organizational decision-making," Business Horizons, Elsevier, vol. 64(4), pages 425-438.
    19. Mi, Xiaomei & Tang, Ming & Liao, Huchang & Shen, Wenjing & Lev, Benjamin, 2019. "The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next?," Omega, Elsevier, vol. 87(C), pages 205-225.
    20. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    21. Farheen Naz & Rohit Agrawal & Anil Kumar & Angappa Gunasekaran & Abhijit Majumdar & Sunil Luthra, 2022. "Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2400-2423, July.
    22. Guilherme F. Frederico, 2021. "From Supply Chain 4.0 to Supply Chain 5.0: Findings from a Systematic Literature Review and Research Directions," Logistics, MDPI, vol. 5(3), pages 1-21, July.
    23. M. O'Dwyer & R. Filieri & L. O'Malley, 2023. "Establishing successful university–industry collaborations: barriers and enablers deconstructed," Post-Print hal-04779125, HAL.
    24. Mostafiz, Md Imtiaz & Musteen, Martina & Saiyed, Abrarali & Ahsan, Mujtaba, 2022. "COVID-19 and the global value chain: Immediate dynamics and long-term restructuring in the garment industry," Journal of Business Research, Elsevier, vol. 139(C), pages 1588-1603.
    25. Sunil Mithas & Zhi‐Long Chen & Terence J.V. Saldanha & Alysson De Oliveira Silveira, 2022. "How will artificial intelligence and Industry 4.0 emerging technologies transform operations management?," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4475-4487, December.
    26. Dubey, Rameshwar & Bryde, David J. & Dwivedi, Yogesh K. & Graham, Gary & Foropon, Cyril, 2022. "Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view," International Journal of Production Economics, Elsevier, vol. 250(C).
    27. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    28. Miriyala, Srinivas Soumitri & Subramanian, Venkat & Mitra, Kishalay, 2018. "TRANSFORM-ANN for online optimization of complex industrial processes: Casting process as case study," European Journal of Operational Research, Elsevier, vol. 264(1), pages 294-309.
    29. Mohammadi, Majid & Rezaei, Jafar, 2020. "Bayesian best-worst method: A probabilistic group decision making model," Omega, Elsevier, vol. 96(C).
    30. Taimur Ahad & Peter Busch & Yvette Blount & Winnie Picoto, 2021. "Mobile Phone-based Information Systems for Empowerment: Opportunities for Ready-made Garment Industries," Journal of Global Information Technology Management, Taylor & Francis Journals, vol. 24(1), pages 57-85, January.
    31. Michele O’Dwyer & Raffaele Filieri & Lisa O’Malley, 2023. "Establishing successful university–industry collaborations: barriers and enablers deconstructed," The Journal of Technology Transfer, Springer, vol. 48(3), pages 900-931, June.
    32. Janya Chanchaichujit & Sreejith Balasubramanian & Ng Si Min Charmaine, 2020. "A systematic literature review on the benefit-drivers of RFID implementation in supply chains and its impact on organizational competitive advantage," Cogent Business & Management, Taylor & Francis Journals, vol. 7(1), pages 1818408-181, January.
    33. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
    34. Juan Manuel Maqueira & José Moyano-Fuentes & Sebastián Bruque, 2019. "Drivers and consequences of an innovative technology assimilation in the supply chain: cloud computing and supply chain integration," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2083-2103, April.
    35. Li Cui & Hao Wu & Jing Dai, 2023. "Modelling flexible decisions about sustainable supplier selection in multitier sustainable supply chain management," International Journal of Production Research, Taylor & Francis Journals, vol. 61(14), pages 4603-4624, July.
    36. Pervaiz Akhtar & Arsalan Mujahid Ghouri & Haseeb Ur Rehman Khan & Mirza Amin ul Haq & Usama Awan & Nadia Zahoor & Zaheer Khan & Aniqa Ashraf, 2023. "Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions," Annals of Operations Research, Springer, vol. 327(2), pages 633-657, August.
    37. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2022. "A new artificial neural networks algorithm to analyze the nexus among logistics performance, energy demand, and environmental degradation," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 315-328.
    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. Feng Ji & Yonghua Zhou & Hongjian Zhang & Guiqing Cheng & Qubo Luo, 2025. "Navigating the Digital Odyssey: AI-Driven Business Models in Industry 4.0," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 5714-5757, March.
    2. Jorzik, Philip & Klein, Sascha P. & Kanbach, Dominik K. & Kraus, Sascha, 2024. "AI-driven business model innovation: A systematic review and research agenda," Journal of Business Research, Elsevier, vol. 182(C).
    3. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    4. Li, Lixu & Zhu, Wenwen & Chen, Lujie & Liu, Yaoqi, 2024. "Generative AI usage and sustainable supply chain performance: A practice-based view," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
    5. 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.
    6. Samadhiya, Ashutosh & Yadav, Sanjeev & Kumar, Anil & Majumdar, Abhijit & Luthra, Sunil & Garza-Reyes, Jose Arturo & Upadhyay, Arvind, 2023. "The influence of artificial intelligence techniques on disruption management: Does supply chain dynamism matter?," Technology in Society, Elsevier, vol. 75(C).
    7. Fosso Wamba, Samuel & Queiroz, Maciel M. & Trinchera, Laura, 2024. "The role of artificial intelligence-enabled dynamic capability on environmental performance: The mediation effect of a data-driven culture in France and the USA," International Journal of Production Economics, Elsevier, vol. 268(C).
    8. Wei Yang & Fenglei Han & Yin Zhou & Yu Gao, 2025. "More relational or more digital? The synchronous and ambivalent influences of firm capabilities on value co-creation," The Journal of Technology Transfer, Springer, vol. 50(2), pages 446-468, April.
    9. Schneider, K. Florian, 2024. "Intermediaries (not) in action: Impediments to initiating the utilization of public research," Technology in Society, Elsevier, vol. 78(C).
    10. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    11. Irina Ervits, 2024. "Developing indicators for the social benefits of university-industry collaborations," International Journal of Corporate Social Responsibility, Springer, vol. 9(1), pages 1-17, December.
    12. Wu, Qun & Liu, Xinwang & Zhou, Ligang & Qin, Jindong & Rezaei, Jafar, 2024. "An analytical framework for the best–worst method," Omega, Elsevier, vol. 123(C).
    13. Rahman, Muhammad Sabbir & Bag, Surajit & Hossain, Md Afnan & Abdel Fattah, Fadi Abdel Muniem & Gani, Mohammad Osman & Rana, Nripendra P., 2023. "The new wave of AI-powered luxury brands online shopping experience: The role of digital multisensory cues and customers’ engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    14. Mingyang Nan & Longyang Huang, 2025. "Innovation Ecosystems: A Cross-Industry Examination of Knowledge Flows and Collaboration Dynamics," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 26-64, March.
    15. Md Maruf Hossan Chowdhury & Priyabrata Chowdhury & Mohammed Quaddus & Kazi Waziur Rahman & Sakib Shahriar, 2024. "Flexibility in Enhancing Supply Chain Resilience: Developing a Resilience Capability Portfolio in the Event of Severe Disruption," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 25(2), pages 395-417, June.
    16. Min Zhang & Jingjing Zeng, 2025. "Which governance structures are conducive to the performance of TTOs? Evidence from Taiwan," The Journal of Technology Transfer, Springer, vol. 50(1), pages 271-303, February.
    17. Shahzad, Fakhar & Zaied, Younes Ben & Shahzad, Muhammad Asim & Mahmood, Faisal, 2024. "Insights into the performance of green supply chain in the Chinese semiconductor industry," International Journal of Production Economics, Elsevier, vol. 273(C).
    18. Pietronudo, Maria Cristina & Croidieu, Grégoire & Schiavone, Francesco, 2022. "A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    19. Burak Can Altay & Abdullah Erdem Boztas & Abdullah Okumuş & Muhammet Gul & Erkan Çelik, 2023. "How Will Autonomous Vehicles Decide in Case of an Accident? An Interval Type-2 Fuzzy Best–Worst Method for Weighting the Criteria from Moral Values Point of View," Sustainability, MDPI, vol. 15(11), pages 1-20, June.
    20. Assefa Tesfaye Hailu, 2024. "The role of university–industry linkages in promoting technology transfer: implementation of triple helix model relations," Journal of Innovation and Entrepreneurship, Springer, vol. 13(1), pages 1-17, 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_s13132-024-01999-6. 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.