IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i3d10.1007_s10845-017-1305-7.html
   My bibliography  Save this article

A method for product platform planning based on pruning analysis and attribute matching

Author

Listed:
  • Qiuhua Zhang

    (Wuhan University)

  • Weiping Peng

    (Wuhan University)

  • Jin Lei

    (Wuhan University)

  • Junhao Dou

    (Wuhan University)

  • Xiangyang Hu

    (Wuhan University)

  • Rui Jiang

    (Wuhan University)

Abstract

Product platform planning can greatly support product variant design, which is of great help to the implementation of mass customization (MC). In most of product platform planning methods, product modules and product families have been usually preplanned before products are designed, which would not make full use of the existing product resources. In this paper, we propose a method for product platform planning using the existing product data in product lifecycle management (PLM) database. The proposed method introduces two key technologies, i.e., pruning analysis and attribute matching. The pruning analysis is used to find out the sharing parts of different product families, which constitutes the basic framework of product platform; the attribute matching is used to classify product modules into different categories according to their sharing degrees, which reveals the relationships of different product modules and forms the association rules of product platform. The effectiveness of the proposed method is verified by the product data in the PLM database of a valve company. The proposed method greatly improves the reuse rate of existing product resources, providing an effective and fast way for enterprises to implement the MC strategy.

Suggested Citation

  • Qiuhua Zhang & Weiping Peng & Jin Lei & Junhao Dou & Xiangyang Hu & Rui Jiang, 2019. "A method for product platform planning based on pruning analysis and attribute matching," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1069-1083, March.
  • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1305-7
    DOI: 10.1007/s10845-017-1305-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-017-1305-7
    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/s10845-017-1305-7?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. Anzanello, Michel J. & Fogliatto, Flavio S., 2011. "Selecting the best clustering variables for grouping mass-customized products involving workers' learning," International Journal of Production Economics, Elsevier, vol. 130(2), pages 268-276, April.
    2. Zhang, Linda L., 2015. "A literature review on multitype platforming and framework for future research," International Journal of Production Economics, Elsevier, vol. 168(C), pages 1-12.
    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. Siri Jagstedt & Magnus Persson, 2019. "Using Platform Strategies In The Development Of Integrated Product-Service Solutions," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 23(04), pages 1-36, May.
    2. López-Camacho, Eunice & Terashima-Marín, Hugo & Ochoa, Gabriela & Conant-Pablos, Santiago Enrique, 2013. "Understanding the structure of bin packing problems through principal component analysis," International Journal of Production Economics, Elsevier, vol. 145(2), pages 488-499.
    3. Soliman, Marlon & Saurin, Tarcisio Abreu & Anzanello, Michel Jose, 2018. "The impacts of lean production on the complexity of socio-technical systems," International Journal of Production Economics, Elsevier, vol. 197(C), pages 342-357.
    4. Cenamor, J. & Rönnberg Sjödin, D. & Parida, V., 2017. "Adopting a platform approach in servitization: Leveraging the value of digitalization," International Journal of Production Economics, Elsevier, vol. 192(C), pages 54-65.
    5. Akhtar, Pervaiz & Ghouri, Arsalan Mujahid & Ashraf, Aniqa & Lim, Jia Jia & Khan, Naveed R & Ma, Shuang, 2024. "Smart product platforming powered by AI and generative AI: Personalization for the circular economy," International Journal of Production Economics, Elsevier, vol. 273(C).
    6. Kerstens, Kristiaan & Azadi, Majid & Kazemi Matin, Reza & Farzipoor Saen, Reza, 2024. "Double hedonic price-characteristics frontier estimation for IoT service providers in the industry 5.0 era: A nonconvex perspective accommodating ratios," European Journal of Operational Research, Elsevier, vol. 319(1), pages 222-233.
    7. Bersch, Christopher V. & Akkerman, Renzo & Kolisch, Rainer, 2021. "Strategic planning of new product introductions: Integrated planning of products and modules in the automotive industry," Omega, Elsevier, vol. 105(C).
    8. Du, Jiaoman & Zhou, Jiandong & Li, Xiang & Li, Lei & Guo, Ao, 2021. "Integrated self-driving travel scheme planning," International Journal of Production Economics, Elsevier, vol. 232(C).
    9. Almohri, Haidar & Chinnam, Ratna Babu & Colosimo, Mark, 2019. "Data-driven analytics for benchmarking and optimizing the performance of automotive dealerships," International Journal of Production Economics, Elsevier, vol. 213(C), pages 69-80.
    10. Dao, Cuong D. & Zuo, Ming J. & Pandey, Mayank, 2014. "Selective maintenance for multi-state series–parallel systems under economic dependence," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 240-249.
    11. Jalali, Hamed & Van den Broeke, Maud & Van Nieuwenhuyse, Inneke, 2022. "Platform and product design for markets with quality and feature sensitive customers," International Journal of Production Economics, Elsevier, vol. 244(C).
    12. Van den Broeke, Maud M. & Boute, Robert N. & Van Mieghem, Jan A., 2018. "Platform flexibility strategies: R&D investment versus production customization tradeoff," European Journal of Operational Research, Elsevier, vol. 270(2), pages 475-486.
    13. Casado, Silvia & Laguna, Manuel & Pacheco, Joaquín & Puche, Julio C., 2020. "Grouping products for the optimization of production processes: A case in the steel manufacturing industry," European Journal of Operational Research, Elsevier, vol. 286(1), pages 190-202.
    14. Fang, Edward Aihua & Li, Xiaoyi & Lu, Jiajun, 2016. "Effects of organizational learning on process technology and operations performance in mass customizers," International Journal of Production Economics, Elsevier, vol. 174(C), pages 68-75.
    15. Miriam Rocha & Cristina Albuquerque Moreira Silva & Reinaldo Germano Santos Junior & Michel Anzanello & Gabrielli Harumi Yamashita & Luis Antonio Lindau, 2020. "Selecting the most relevant variables towards clustering bus priority corridors," Public Transport, Springer, vol. 12(3), pages 587-609, October.
    16. Yang, Zaoli & Li, Qin & Charles, Vincent & Xu, Bing & Gupta, Shivam, 2023. "Supporting personalized new energy vehicle purchase decision-making: Customer reviews and product recommendation platform," International Journal of Production Economics, Elsevier, vol. 265(C).
    17. Anzanello, Michel J. & Fogliatto, Flavio S. & Santos, Luana, 2014. "Learning dependent job scheduling in mass customized scenarios considering ergonomic factors," International Journal of Production Economics, Elsevier, vol. 154(C), pages 136-145.

    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:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1305-7. 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.