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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
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    References listed on IDEAS

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    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.
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