IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v24y2022i1d10.1007_s10668-021-01499-6.html
   My bibliography  Save this article

In-service machine tool remanufacturing: a sustainable resource-saving and high-valued recovery approach

Author

Listed:
  • Yanbin Du

    (Chongqing Technology and Business University
    Chongqing Technology and Business University)

  • Guohua He

    (Chongqing Technology and Business University)

  • Bo Li

    (Chongqing Technology and Business University)

  • Zhijie Zhou

    (Chongqing Technology and Business University)

  • Guoao Wu

    (Chongqing Technology and Business University)

Abstract

With great changes in production requirements, in-service machine tools may be unable to meet new production requirements. Aiming for this problem, a sustainable resource-saving and high-valued recovery approach for in-service machine tools is proposed, which integrates remanufacturing and product-service systems (PSS). In-service machine tool remanufacturing is defined as a new remanufacturing model based on condition monitoring and diagnosis, which is different from traditional used machine tool remanufacturing and new machine tool manufacturing mentioned in the current literature. Procedure framework of in-service machine tool remanufacturing is proposed, including condition monitoring and diagnosis, matching analysis, remanufacturability evaluation and decision-making, identification of potential problems, individualized redesign, disassembly, cleaning, inspection and classification, performance improvement as well as reassembly and inspection. Combining the remanufacturing practice of an in-service heavy-duty horizontal lathe, the comprehensive resource-efficient benefits of in-service machine tool remanufacturing are illustrated. The results show that the proposed remanufacturing model can restore in-service machine tools to like-new or better performance and upgrade their functionality, with great economic and social benefits. For the implementation of this remanufacturing model, an in-depth analysis of the supporting technologies such as condition monitoring and diagnosis, decision-making analysis, etc., should be done in future research to guarantee the production capacities of in-service machine tools.

Suggested Citation

  • Yanbin Du & Guohua He & Bo Li & Zhijie Zhou & Guoao Wu, 2022. "In-service machine tool remanufacturing: a sustainable resource-saving and high-valued recovery approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 1335-1358, January.
  • Handle: RePEc:spr:endesu:v:24:y:2022:i:1:d:10.1007_s10668-021-01499-6
    DOI: 10.1007/s10668-021-01499-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-021-01499-6
    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/s10668-021-01499-6?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. 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.
    2. Xianpei Hong & Lan Wang & Yeming Gong & Wanying (Amanda) Chen, 2020. "What is the role of value-added service in a remanufacturing closed-loop supply chain?," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3342-3361, June.
    3. Zhigang Jiang & Ya Jiang & Yan Wang & Hua Zhang & Huajun Cao & Guangdong Tian, 2019. "A hybrid approach of rough set and case-based reasoning to remanufacturing process planning," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 19-32, January.
    4. Östlin, Johan & Sundin, Erik & Björkman, Mats, 2008. "Importance of closed-loop supply chain relationships for product remanufacturing," International Journal of Production Economics, Elsevier, vol. 115(2), pages 336-348, October.
    5. Chang Fang & Zhuangzhuang You & Yudou Yang & Duomei Chen & Samar Mukhopadhyay, 2020. "Is third-party remanufacturing necessarily harmful to the original equipment manufacturer?," Annals of Operations Research, Springer, vol. 291(1), pages 317-338, August.
    6. Xiaochen Sun & Yancong Zhou & Yongjian Li & Kannan Govindan & Xiaonan Han, 2020. "Differentiation competition between new and remanufactured products considering third-party remanufacturing," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(1), pages 161-180, January.
    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. Alegoz, Mehmet & Kaya, Onur & Bayindir, Z. Pelin, 2021. "A comparison of pure manufacturing and hybrid manufacturing–remanufacturing systems under carbon tax policy," European Journal of Operational Research, Elsevier, vol. 294(1), pages 161-173.
    2. Zhang, Guangxia & Gong, Yeming & Hong, Xianpei, 2022. "Free rider effect of quality information disclosure in remanufacturing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    3. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    4. Ahmad Ibrahim Aljumah & Mohammed T. Nuseir & Md. Mahmudul Alam, 2021. "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Post-Print hal-03538161, HAL.
    5. Aaltonen, Aleksi Ville & Alaimo, Cristina & Kallinikos, Jannis, 2021. "The making of data commodities: data analytics as an embedded process," LSE Research Online Documents on Economics 110296, London School of Economics and Political Science, LSE Library.
    6. Anke Joubert & Matthias Murawski & Markus Bick, 2023. "Measuring the Big Data Readiness of Developing Countries – Index Development and its Application to Africa," Information Systems Frontiers, Springer, vol. 25(1), pages 327-350, February.
    7. Rampersad, Giselle, 2020. "Robot will take your job: Innovation for an era of artificial intelligence," Journal of Business Research, Elsevier, vol. 116(C), pages 68-74.
    8. Yue Tan & Chunxiang Guo, 2019. "Research on Two-Way Logistics Operation with Uncertain Recycling Quality in Government Multi-Policy Environment," Sustainability, MDPI, vol. 11(3), pages 1-18, February.
    9. Sidney Anderson, 2024. "Expanding data literacy to include data preparation: building a sound marketing analytics foundation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 227-234, June.
    10. Chiara Mio & Silvia Panfilo & Benedetta Blundo, 2020. "Sustainable development goals and the strategic role of business: A systematic literature review," Business Strategy and the Environment, Wiley Blackwell, vol. 29(8), pages 3220-3245, December.
    11. Magdalena Rusch & Josef‐Peter Schöggl & Rupert J. Baumgartner, 2023. "Application of digital technologies for sustainable product management in a circular economy: A review," Business Strategy and the Environment, Wiley Blackwell, vol. 32(3), pages 1159-1174, March.
    12. Michela Arnaboldi, 2018. "The Missing Variable in Big Data for Social Sciences: The Decision-Maker," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    13. Anike Sult & Janice Wobst & Rainer Lueg, 2024. "The role of training in implementing corporate sustainability: A systematic literature review," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(1), pages 1-30, January.
    14. Brewis, Claire & Dibb, Sally & Meadows, Maureen, 2023. "Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    15. Rui Wang & Xiangyu Guo & Shisheng Zhong & Gaolei Peng & Lin Wang, 2022. "Decision rule mining for machining method chains based on rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 799-807, March.
    16. Mohamed Gaber & Edward J. Lusk, 2019. "A Vetting Protocol for the Analytical Procedures Platform for the AP-Phase of PCAOB Audits," Accounting and Finance Research, Sciedu Press, vol. 8(4), pages 1-43, November.
    17. Leogrande, Angelo, 2021. "The Destruction of Price-Representativeness," MPRA Paper 111239, University Library of Munich, Germany.
    18. Damminda Alahakoon & Rashmika Nawaratne & Yan Xu & Daswin Silva & Uthayasankar Sivarajah & Bhumika Gupta, 2023. "Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities," Information Systems Frontiers, Springer, vol. 25(1), pages 221-240, February.
    19. Taiwen Feng & Hongyan Sheng, 2023. "Identifying the equifinal configurations of prompting green supply chain integration and subsequent performance outcome," Business Strategy and the Environment, Wiley Blackwell, vol. 32(8), pages 5234-5251, December.
    20. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.

    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:endesu:v:24:y:2022:i:1:d:10.1007_s10668-021-01499-6. 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.