Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

A methodology for knowledge discovery to support product family design

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Agard, B., & Kusiak, A. (2004). Data-mining-based methodology for the design of product family. International Journal of Production Research, 42(15), 2955–2969.

    Article  Google Scholar 

  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In 20th international conference on very large data bases (pp. 487–499). Santiago de Chile, Chile.

  • Bezdek, J. C. (1974). Numerical taxonomy with fuzzy sets. Journal of Mathematics Biology, 1(1), 57–71.

    Article  Google Scholar 

  • Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. New York, NY: Plenum.

    Google Scholar 

  • Braha, D. (2001). Data mining for design and manufacturing: methods and applications. Dordrecht: Kluwer Academic.

    Google Scholar 

  • Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining. Cambridge, MA: MIT Press.

    Google Scholar 

  • Gonzalez-Zugasti, J. P., Otto, K. N., & Baker, J. D. (2000). A method for architecting product platforms. Research in Engineering Design, 12(2), 61–72.

    Article  Google Scholar 

  • Hipp, J., Guntzer, U., & Nakhaeizadeh, G. (2000). Algorithms for association rule mining—a general survey and comparison. ACM SIGKDD Explorations, 2(1), 58–64.

    Article  Google Scholar 

  • Hirtz, J., Stone, R. B., McAdams, D. A., Szykman, S., & Wood, K. L. (2002). A functional basis for engineering design: reconciling and evolving previous efforts. Research in Engineering Design, 13(2), 65–82.

    Google Scholar 

  • Jiao, J., & Zhang, Y. (2005). Product portfolio identification based on association rule mining. Computer-Aided Design, 27(2), 149–172.

    Article  Google Scholar 

  • Jiao, J., Zhang, L., Pokharel, S., & He, Z. (2007). Identifying generic routings for product families based on text mining and tree matching. Decision Support Systems, 43(3), 866–883.

    Article  Google Scholar 

  • Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  • Kamrani, A. K., & Salhieh, S. M. (2000). Product design for modularity. Boston, MA: Kluwer Academic.

    Google Scholar 

  • Liao, T. W. (2001). Classification and coding approaches to part family formation under a fuzzy environment. Fuzzy sets and systems, 122(3), 425–441.

    Article  Google Scholar 

  • Liao, T. W., & Triantaphyllou, E. (2008). Recent advances in data mining of enterprise data. Singapore: World Scientific.

    Google Scholar 

  • Miyamoto, S. (1990). Fuzzy sets in information retrieval and cluster analysis. Dordrecht: Kluwer Academic.

    Google Scholar 

  • Moon, S. K., Kumara, S. R. T., & Simpson, T. W. (2005). Knowledge representation for product design using techspecs concept ontology. In The IEEE international conference on information reuse and integration (pp. 241–246). Las Vegas, NV.

  • Moon, S. K., Kumara, S. R. T., & Simpson, T. W. (2006). A multi-agent system for modular platform design in a dynamic electronic market environment. In ASME design engineering technical conferences & computers and information in engineering conference, Philadelphia, PA, 10–13 September. ASME, Paper No. DETC2006/CIE-99286.

  • Moore, W. L., Louvier, J. J., & Verma, R. (1999). Using conjoint analysis to help design product platforms. Journal of Product Innovation Management, 16(1), 27–39.

    Article  Google Scholar 

  • Muffatto, M., & Roveda, M. (2000). Developing product platforms: analysis of the development process. Technovation, 20(11), 617–630.

    Article  Google Scholar 

  • Nanda, J., Thevenot, H. J., Simpson, T. W., Stone, R. B., Bohm, M., & Shooter, S. B. (2007). Product family design knowledge representation, aggregation, reuse, and analysis. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 21(2), 173–192.

    Article  Google Scholar 

  • Rai, R., & Allada, V. (2003). Modular product family design: agent-based Pareto-optimization and quality loss function-based post-optimal analysis. International Journal of Production Research, 41(17), 4075–4098.

    Article  Google Scholar 

  • Romanowski, C. J., & Nagi, R. (2004). A data mining approach to forming generic bills of materials in support of variant design activities. ASME Journal of Computing and Information Science in Engineering, 4(1), 316–328.

    Article  Google Scholar 

  • Shooter, S. B., Simpson, T. W., Kumara, S. R. T., Stone, R. B., & Terpenny, J. P. (2005). Toward an information management infrastructure for product family planning and platform customization. International Journal of Mass Customization, 1(1), 134–155.

    Article  Google Scholar 

  • Siddique, Z., & Rosen, D. W. (2000). Product family configuration reasoning using discrete design spaces. In ASME design engineering technical conferences proceedings, Baltimore, MD. ASME, Paper No. DETC00/DTM-14666.

  • Simpson, T. W. (2004). Product platform design and customization: status and promise. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 18(1), 3–20.

    Article  Google Scholar 

  • Simpson, T. W., Maier, J. R. A., & Mistree, F. (2001). Product platform design: method and application. Research in Engineering Design, 13(1), 2–22.

    Article  Google Scholar 

  • Simpson, T. W., Siddique, Z., & Jiao, J. (2005). Product platform and product family design: methods and applications. New York, NY: Springer.

    Google Scholar 

  • Stone, R. B., Wood, K. L., & Crawford, R. H. (2000). A heuristic method for identifying modules for product architectures. Design Studies, 21(1), 5–31.

    Article  Google Scholar 

  • Swartout, W., & Tate, A. (1999). Ontologies. IEEE Transactions on Intelligent Systems, 14(1), 18–19.

    Article  Google Scholar 

  • Tarasewich, P., & Nair, S. K. (2001). Designer-moderated product design. IEEE Transactions on Engineering Management, 48(2), 175–188.

    Article  Google Scholar 

  • Torra, V. (2005). Fuzzy c-means for fuzzy hierarchical clustering. In The IEEE international conference on fuzzy systems (pp. 646–651). Reno, NV.

  • Ulrich, K. T., & Eppinger, S. D. (2000). Product design and development (2nd ed.). New York, NY: McGraw-Hill.

    Google Scholar 

  • Xue, D., & Dong, Z. (1997). Coding and clustering of design and manufacturing features for concurrent design. Computer in Industry, 34(1), 139–153.

    Article  Google Scholar 

  • Yilmaz, E., Triantaphyllou, E., Chen, J., & LIao, T. W. (2003). A heuristic for mining association rules in polynomial time. Mathematical and Computer Modelling, 37(1–2), 219–233.

    Article  Google Scholar 

  • Zha, X. F., & Sriram, R. D. (2006). Platform-based product design and development: a knowledge-intensive support approach. Knowledge-Based Systems, 19(7), 524–543.

    Article  Google Scholar 

  • Zhang, Y., Jiao, J., & Ma, Y. (2007). Market segmentation for product family positioning based on fuzzy clustering. Journal of Engineering Design, 18(3), 227–241.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial & Manufacturing Engineering, The Pennsylvania State University, University Park, PA, 16802, USA

    Seung Ki Moon & Soundar R. T. Kumara

  2. Departments of Mechanical & Nuclear Engineering and Industrial & Manufacturing Engineering, The Pennsylvania State University, University Park, PA, 16802, USA

    Timothy W. Simpson

  3. The Pennsylvania State University, 314D Leonhard Building, University Park, PA, 16802, USA

    Timothy W. Simpson

Authors
  1. Seung Ki Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Timothy W. Simpson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Soundar R. T. Kumara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Timothy W. Simpson.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Moon, S.K., Simpson, T.W. & Kumara, S.R.T. A methodology for knowledge discovery to support product family design. Ann Oper Res 174, 201–218 (2010). https://doi.org/10.1007/s10479-008-0349-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-008-0349-7

Keywords

Search

Navigation