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A methodology for knowledge discovery to support product family design

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  • Seung Moon
  • Timothy Simpson
  • Soundar Kumara

Abstract

This paper introduces a methodology for knowledge discovery related to product family design that integrates an ontology with data mining techniques. In the proposed methodology, the ontology represents attributes for the components of products in functional hierarchies. Fuzzy clustering is employed for data mining to first partition product functions into subsets for identifying modules in a given product family and then identify the similarity level of components in a module. Module categorization is introduced to support association rule mining for knowledge discovery related to platform design. We apply the proposed methodology to first develop and then utilize design knowledge for a family of power tools. Based on the developed design knowledge, a new platform is suggested to improve commonality in the power tool family. Copyright Springer Science+Business Media, LLC 2010

Suggested Citation

  • Seung Moon & Timothy Simpson & Soundar Kumara, 2010. "A methodology for knowledge discovery to support product family design," Annals of Operations Research, Springer, vol. 174(1), pages 201-218, February.
    • Handle: RePEc:spr:annopr:v:174:y:2010:i:1:p:201-218:10.1007/s10479-008-0349-7
    DOI: 10.1007/s10479-008-0349-7
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    Citations

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    Cited by:

    1. Stelios Tsafarakis, 2016. "Redesigning product lines in a period of economic crisis: a hybrid simulated annealing algorithm with crossover," Annals of Operations Research, Springer, vol. 247(2), pages 617-633, December.
    2. Rong Liu & Jujun Huang & Zhongju Zhang, 2023. "Tracking disclosure change trajectories for financial fraud detection," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 584-602, February.
    3. Samyeon Kim & Seung Ki Moon, 2019. "Eco-modular product architecture identification and assessment for product recovery," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 383-403, January.
    4. Shuang Ma & Gang Du & Jianxin (Roger) Jiao & Ruchuan Zhang, 2016. "Hierarchical game joint optimization for product family-driven modular design," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(12), pages 1496-1509, December.

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