lynx   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v281y2025ics0925527325000027.html
   My bibliography  Save this article

Cost-effective industrial internet of things network planning for sustainable manufacturing systems

Author

Listed:
  • Yun, Lingxiang
  • Li, Lin
  • Zhang, Jiapei
  • Guan, Jingze
Abstract
The industrial Internet of Things (IoT) has been gaining significant momentum and is considered a promising technology for establishing a foundation for energy and carbon management in the manufacturing sector. Existing management studies commonly assume the availability of IoT data for all manufacturing equipment, yet connecting every single piece of equipment requires enormous investments without necessarily yielding significant improvements in management effectiveness. In practice, the value derived from IoT data collected from diverse manufacturing equipment varies based on the unique features of the equipment and its role within the manufacturing system. Therefore, this study proposes a planning method for cost-effective industrial IoT networks in the context of sustainable manufacturing. The proposed approach jointly considers the quality-of-service requirements of the IoT network and the characteristics of the manufacturing system. It aims to balance the trade-offs between industrial IoT investments and the effectiveness of IoT data-enabled carbon management in manufacturing systems. In addition, an efficient algorithm is developed to solve the network planning problem and identify cost-effective solutions. Case studies on automobile assembly lines indicate the effectiveness of the proposed method in generating economically favorable industrial IoT networks, leading to a 38.9% reduction in industrial IoT implementation costs while maintaining satisfactory carbon emission reduction effects for manufacturing systems.

Suggested Citation

  • Yun, Lingxiang & Li, Lin & Zhang, Jiapei & Guan, Jingze, 2025. "Cost-effective industrial internet of things network planning for sustainable manufacturing systems," International Journal of Production Economics, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:proeco:v:281:y:2025:i:c:s0925527325000027
    DOI: 10.1016/j.ijpe.2025.109517
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527325000027
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2025.109517?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. Raj, Alok & Dwivedi, Gourav & Sharma, Ankit & Lopes de Sousa Jabbour, Ana Beatriz & Rajak, Sonu, 2020. "Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective," International Journal of Production Economics, Elsevier, vol. 224(C).
    2. Yun, Lingxiang & Xiao, Minkun & Li, Lin, 2022. "Vehicle-to-manufacturing (V2M) system: A novel approach to improve energy demand flexibility for demand response towards sustainable manufacturing," Applied Energy, Elsevier, vol. 323(C).
    3. Yun, Lingxiang & Li, Lin & Ma, Shuaiyin, 2022. "Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment," Applied Energy, Elsevier, vol. 310(C).
    4. 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).
    5. Lu, Renzhi & Bai, Ruichang & Huang, Yuan & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2021. "Data-driven real-time price-based demand response for industrial facilities energy management," Applied Energy, Elsevier, vol. 283(C).
    6. Cai, Wei & Wang, Lianguo & Li, Li & Xie, Jun & Jia, Shun & Zhang, Xugang & Jiang, Zhigang & Lai, Kee-hung, 2022. "A review on methods of energy performance improvement towards sustainable manufacturing from perspectives of energy monitoring, evaluation, optimization and benchmarking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    7. Zhao Peng & Huan Zhang & Hongtao Tang & Yue Feng & Weiming Yin, 2022. "Research on flexible job-shop scheduling problem in green sustainable manufacturing based on learning effect," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1725-1746, August.
    8. Frank, Alejandro Germán & Dalenogare, Lucas Santos & Ayala, Néstor Fabián, 2019. "Industry 4.0 technologies: Implementation patterns in manufacturing companies," International Journal of Production Economics, Elsevier, vol. 210(C), pages 15-26.
    9. Bag, Surajit & Gupta, Shivam & Kumar, Sameer, 2021. "Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development," International Journal of Production Economics, Elsevier, vol. 231(C).
    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. Cugno, Monica & Castagnoli, Rebecca & Büchi, Giacomo, 2021. "Openness to Industry 4.0 and performance: The impact of barriers and incentives," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    2. 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).
    3. Rodríguez-Espíndola, Oscar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel & Emrouznejad, Ali, 2022. "Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    4. Colombari, Ruggero & Geuna, Aldo & Helper, Susan & Martins, Raphael & Paolucci, Emilio & Ricci, Riccardo & Seamans, Robert, 2023. "The interplay between data-driven decision-making and digitalization: A firm-level survey of the Italian and U.S. automotive industries," International Journal of Production Economics, Elsevier, vol. 255(C).
    5. Leherbauer, Dominik & Schulz, Julia & Egyed, Alexander & Hehenberger, Peter, 2025. "Demand-side management in less energy-intensive industries: A systematic mapping study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 212(C).
    6. Cifone, Fabiana Dafne & Hoberg, Kai & Holweg, Matthias & Staudacher, Alberto Portioli, 2021. "‘Lean 4.0’: How can digital technologies support lean practices?," International Journal of Production Economics, Elsevier, vol. 241(C).
    7. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    8. Bianco, Débora & Bueno, Adauto & Godinho Filho, Moacir & Latan, Hengky & Miller Devós Ganga, Gilberto & Frank, Alejandro G. & Chiappetta Jabbour, Charbel Jose, 2023. "The role of Industry 4.0 in developing resilience for manufacturing companies during COVID-19," International Journal of Production Economics, Elsevier, vol. 256(C).
    9. Tao, Zhibin & Chao, Jiaxiao, 2024. "Unlocking new opportunities in the industry 4.0 era, exploring the critical impact of digital technology on sustainable performance and the mediating role of GSCM practices," Innovation and Green Development, Elsevier, vol. 3(3).
    10. Calış Duman, Meral & Akdemir, Bunyamin, 2021. "A study to determine the effects of industry 4.0 technology components on organizational performance," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    11. Shet, Sateesh V. & Pereira, Vijay, 2021. "Proposed managerial competencies for Industry 4.0 – Implications for social sustainability," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    12. Alok Raj & Anand Jeyaraj, 2023. "Antecedents and consequents of industry 4.0 adoption using technology, organization and environment (TOE) framework: A meta-analysis," Annals of Operations Research, Springer, vol. 322(1), pages 101-124, March.
    13. Somohano-Rodríguez, Francisco M. & Madrid-Guijarro, Antonia, 2022. "Do industry 4.0 technologies improve Cantabrian manufacturing smes performance? The role played by industry competition," Technology in Society, Elsevier, vol. 70(C).
    14. Valentina De Simone & Valentina Di Pasquale & Maria Elena Nenni & Salvatore Miranda, 2023. "Sustainable Production Planning and Control in Manufacturing Contexts: A Bibliometric Review," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    15. Culot, Giovanna & Orzes, Guido & Sartor, Marco & Nassimbeni, Guido, 2020. "The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    16. El Bhilat, El Mehdi & El Jaouhari, Asmae & Hamidi, L. Saadia, 2024. "Assessing the influence of artificial intelligence on agri-food supply chain performance: the mediating effect of distribution network efficiency," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    17. Colombari, Ruggero & Neirotti, Paolo & Berbegal-Mirabent, Jasmina, 2024. "Disentangling the socio-technical impacts of digitalization: What changes for shop-floor decision-makers?," International Journal of Production Economics, Elsevier, vol. 276(C).
    18. 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).
    19. 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.
    20. 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).

    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:eee:proeco:v:281:y:2025:i:c:s0925527325000027. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.
    Лучший частный хостинг