IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v26y2024i6d10.1007_s10796-021-10173-1.html
   My bibliography  Save this article

Functional Requirements and Supply Chain Digitalization in Industry 4.0

Author

Listed:
  • Lu Han

    (Beijing Jiaotong University)

  • Hanping Hou

    (Beijing Jiaotong University)

  • Z. M. Bi

    (Purdue University Fort Wayne)

  • Jianliang Yang

    (Beijing University of Chemical Technology)

  • Xiaoxiao Zheng

    (Beijing Jiaotong University)

Abstract

Industry 4.0 aims to automate traditional manufacturing and industrial practices with the aids of recently developed information technologies such as cyber-physical systems, Internet of things, big data analytics, and cloud computing. Implementation of industry 4.0 in manufacturing leads to the digitization of all manufacturing businesses including computer aided design and manufacturing, enterprise resource planning, and supply chain management (SCM). This paper focuses on the challenges and solutions in digitizing supply chains in dynamic, distributed, and decentralized business environments. The complexity and dynamics of supply chains in industry 4.0 are discussed, the performance of a supply chain is evaluated from the perspectives of costs and quality of services, and supply chain management is formulated as an optimization problem for higher requirements of quality of services, efficiency, and timeliness. The challenges of developing digitization solutions to data acquisition, data fusion, and data-driven decision-making supports are discussed in detail. The potential solutions to these challenges are proposed and the impacts on supply chain management are assessed using the data from in a list of automotive manufacturers in China. It has been found the proposed solutions will make positive and significant impact on the digitation of supply chains.

Suggested Citation

  • Lu Han & Hanping Hou & Z. M. Bi & Jianliang Yang & Xiaoxiao Zheng, 2024. "Functional Requirements and Supply Chain Digitalization in Industry 4.0," Information Systems Frontiers, Springer, vol. 26(6), pages 2273-2285, December.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:6:d:10.1007_s10796-021-10173-1
    DOI: 10.1007/s10796-021-10173-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-021-10173-1
    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/s10796-021-10173-1?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. Maryam Abdirad & Krishna Krishnan & Deepak Gupta, 2021. "A two-stage metaheuristic algorithm for the dynamic vehicle routing problem in Industry 4.0 approach," Journal of Management Analytics, Taylor & Francis Journals, vol. 8(1), pages 69-83, January.
    2. Tashkova M., 2016. "Big data - the new challenge facing business," Економічний вісник Донбасу Экономический вестник Донбасса, CyberLeninka;Институт экономики промышленности НАН Украины, issue 4 (46), pages 164-167.
    3. Ivanov, Dmitry & Pavlov, Alexander & Pavlov, Dmitry & Sokolov, Boris, 2017. "Minimization of disruption-related return flows in the supply chain," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 503-513.
    4. Li, Ling, 2011. "Assessing the relational benefits of logistics services perceived by manufacturers in supply chain," International Journal of Production Economics, Elsevier, vol. 132(1), pages 58-67, July.
    5. Beate Franke & Jean-FRANçois Plante & Ribana Roscher & En-shiun Annie Lee & Cathal Smyth & Armin Hatefi & Fuqi Chen & Einat Gil & Alexander Schwing & Alessandro Selvitella & Michael M. Hoffman & Roger, 2016. "Statistical Inference, Learning and Models in Big Data," International Statistical Review, International Statistical Institute, vol. 84(3), pages 371-389, December.
    6. Kwon, Ohbyung & Lee, Namyeon & Shin, Bongsik, 2014. "Data quality management, data usage experience and acquisition intention of big data analytics," International Journal of Information Management, Elsevier, vol. 34(3), pages 387-394.
    7. Oecd, 2016. "Big Data: Bringing Competition Policy to the Digital Era," OECD Roundtables on Competition Policy Papers 193, OECD Publishing.
    8. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    9. David S. Cochran & Joseph T. Foley & Zhuming Bi, 2017. "Use of the manufacturing system design decomposition for comparative analysis and effective design of production systems," International Journal of Production Research, Taylor & Francis Journals, vol. 55(3), pages 870-890, February.
    10. Zhan Ye & Ahmad P Tafti & Karen Y He & Kai Wang & Max M He, 2016. "SparkText: Biomedical Text Mining on Big Data Framework," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-15, September.
    11. Li, Ling, 2013. "The path to Made-in-China: How this was done and future prospects," International Journal of Production Economics, Elsevier, vol. 146(1), pages 4-13.
    12. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    13. Gaynor, Joseph, 2016. "Incorporating Big Data into Market News Reporting," Agricultural Outlook Forum 2016 236860, United States Department of Agriculture, Agricultural Outlook Forum.
    14. Li Da Xu, 2013. "Introduction: Systems Science in Industrial Sectors," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 211-213, May.
    15. Alexandre Dolgui & Dmitry Ivanov & Boris Sokolov, 2020. "Reconfigurable supply chain: the X-network," International Journal of Production Research, Taylor & Francis Journals, vol. 58(13), pages 4138-4163, July.
    16. Jaqueline Iaksch & Ederson Fernandes & Milton Borsato, 2021. "Digitalization and Big data in smart farming – a review," Journal of Management Analytics, Taylor & Francis Journals, vol. 8(2), pages 333-349, April.
    17. Ling Li & Li Xu & Hueiwang Anna Jeng & Dayanand Naik & Thomas Allen & Maria Frontini, 2008. "Creation of environmental health information system for public health service: A pilot study," Information Systems Frontiers, Springer, vol. 10(5), pages 531-542, November.
    18. Ni Li & Minghui Sun & Zhuming Bi & Zeya Su & Chao Wang, 2014. "A new methodology to support group decision-making for IoT-based emergency response systems," Information Systems Frontiers, Springer, vol. 16(5), pages 953-977, November.
    19. Li Da Xu, 2020. "The contribution of systems science to Industry 4.0," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 618-631, July.
    20. Zhuming Bi, 2011. "Revisiting System Paradigms from the Viewpoint of Manufacturing Sustainability," Sustainability, MDPI, vol. 3(9), pages 1-18, August.
    21. Prajogo, Daniel & Olhager, Jan, 2012. "Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration," International Journal of Production Economics, Elsevier, vol. 135(1), pages 514-522.
    22. Lida Xu & WenAn Tan & Hongyuan Zhen & Weiming Shen, 2008. "An approach to enterprise process dynamic modeling supporting enterprise process evolution," Information Systems Frontiers, Springer, vol. 10(5), pages 611-624, November.
    23. Emma Brandon-Jones & Brian Squire & Chad W. Autry & Kenneth J. Petersen, 2014. "A Contingent Resource-Based Perspective of Supply Chain Resilience and Robustness," Journal of Supply Chain Management, Institute for Supply Management, vol. 50(3), pages 55-73, July.
    24. Siqing Shan & Zhongjun Hu & Zhilian Liu & Jihong Shi & Li Wang & Zhuming Bi, 2017. "An adaptive genetic algorithm for demand-driven and resource-constrained project scheduling in aircraft assembly," Information Technology and Management, Springer, vol. 18(1), pages 41-53, March.
    25. Yu, Wantao & Chavez, Roberto & Jacobs, Mark A. & Feng, Mengying, 2018. "Data-driven supply chain capabilities and performance: A resource-based view," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 371-385.
    26. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    27. Friso Zimmermann & Kai Foerstl, 2014. "A Meta-Analysis of the “Purchasing and Supply Management Practice–Performance Link”," Journal of Supply Chain Management, Institute for Supply Management, vol. 50(3), pages 37-54, July.
    28. Haiqing DENG & Xi CHEN, 2016. "The Central Bank in Big Data Era," World Scientific Book Chapters, in: Reforging the Central Bank The Top-Level Design of the Chinese Financial System in the New Normal, chapter 7, pages 193-205, World Scientific Publishing Co. Pte. Ltd..
    29. 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.
    30. Xiong Xiong & Zhang Jin & Jin Xi & Feng Xu, 2016. "Review on Financial Innovations in Big Data Era," Journal of Systems Science and Information, De Gruyter, vol. 4(6), pages 489-504, December.
    31. Yan Shen & Minggao Shen & Qin Chen, 2016. "Measurement of the new economy in China: big data approach," China Economic Journal, Taylor & Francis Journals, vol. 9(3), pages 304-316, September.
    32. Sarac, Aysegul & Absi, Nabil & Dauzère-Pérès, Stéphane, 2010. "A literature review on the impact of RFID technologies on supply chain management," International Journal of Production Economics, Elsevier, vol. 128(1), pages 77-95, November.
    33. Yong Sun & Wenan Tan & Lingxia Li & Weiming Shen & Zhuming Bi & Xiaoming Hu, 2016. "A new method to identify collaborative partners in social service provider networks," Information Systems Frontiers, Springer, vol. 18(3), pages 565-578, June.
    34. Manthou, Vicky & Vlachopoulou, Maro & Folinas, Dimitris, 2004. "Virtual e-Chain (VeC) model for supply chain collaboration," International Journal of Production Economics, Elsevier, vol. 87(3), pages 241-250, February.
    35. Li, Ling & Wang, Bin & Cook, David P., 2014. "Enhancing green supply chain initiatives via empty container reuse," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 190-204.
    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. Yang Lu, 2025. "The Current Status and Developing Trends of Industry 4.0: a Review," Information Systems Frontiers, Springer, vol. 27(1), pages 215-234, February.
    2. Liu, Hua & Xu, Xiaoping & Cheng, T.C.E. & Yu, Yugang, 2024. "Building resilience or maintaining robustness: Insights from relational view and information processing perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).
    3. Li, Xiang, 2020. "Reducing channel costs by investing in smart supply chain technologies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    4. Yang, Miying & Fu, Mingtao & Zhang, Zihan, 2021. "The adoption of digital technologies in supply chains: Drivers, process and impact," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    5. Yu Sun & Ling Li & Hui Shi & Dazhi Chong, 2020. "The transformation and upgrade of China's manufacturing industry in Industry 4.0 era," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 734-740, July.
    6. Siqing Shan & Cangyan Li & Wei Yao & Jihong Shi & Jie Ren, 2014. "An Empirical Study on Critical Factors Affecting Employee Satisfaction," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 447-460, May.
    7. Lin, Jiabao & Fan, Yuchen, 2024. "Seeking sustainable performance through organizational resilience: Examining the role of supply chain integration and digital technology usage," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    8. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry & Zennaro, Ilenia, 2021. "Costs of resilience and disruptions in supply chain network design models: A review and future research directions," International Journal of Production Economics, Elsevier, vol. 235(C).
    9. Emilia Vann Yaroson & Soumyadeb Chowdhury & Sachin Kumar Mangla & Prasanta Kumar Dey, 2024. "Unearthing the interplay between organisational resources, knowledge and industry 4.0 analytical decision support tools to achieve sustainability and supply chain wellbeing," Annals of Operations Research, Springer, vol. 342(2), pages 1321-1368, November.
    10. Ni Li & Minghui Sun & Zhuming Bi & Zeya Su & Chao Wang, 2014. "A new methodology to support group decision-making for IoT-based emergency response systems," Information Systems Frontiers, Springer, vol. 16(5), pages 953-977, November.
    11. Mona Haji & Laoucine Kerbache & Mahaboob Muhammad & Tareq Al-Ansari, 2020. "Roles of Technology in Improving Perishable Food Supply Chains," Logistics, MDPI, vol. 4(4), pages 1-24, December.
    12. Muhammad Irfan & Mingzheng Wang & Naeem Akhtar, 2019. "Impact of IT capabilities on supply chain capabilities and organizational agility: a dynamic capability view," Operations Management Research, Springer, vol. 12(3), pages 113-128, December.
    13. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    14. Rameshwar Dubey & David J. Bryde & Cyril Foropon & Gary Graham & Mihalis Giannakis & Deepa Bhatt Mishra, 2022. "Agility in humanitarian supply chain: an organizational information processing perspective and relational view," Annals of Operations Research, Springer, vol. 319(1), pages 559-579, December.
    15. Zhou, Honggeng & Li, Ling, 2020. "The impact of supply chain practices and quality management on firm performance: Evidence from China's small and medium manufacturing enterprises," International Journal of Production Economics, Elsevier, vol. 230(C).
    16. Wen, Xiao-Wei & Marlin, Janita & Wen, Zhi-Jian & Yang, Zhao-Hui, 2020. "Reviewing studies of radio frequency identification applications in supply chain for food safety," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 23(5), February.
    17. 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).
    18. Xin Wang & Li Wang & Xiaobo Xu & Ping Ji, 2014. "Identifying Employee Turnover Risks Using Modified Quality Function Deployment," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 398-404, May.
    19. Haiqing Yu & Shukuan Zhao & Xiaobo Xu & Yilin Wang, 2014. "An Empirical Study on the Dynamic Relationship between Higher Educational Investment and Economic Growth using VAR Model," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 461-470, May.
    20. Siqing Shan & Cangyan Li & Jihong Shi & Li Wang & Huali Cai, 2014. "Impact of Effective Communication, Achievement Sharing and Positive Classroom Environments on Learning Performance," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 471-482, May.

    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:6:d:10.1007_s10796-021-10173-1. 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.