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An optimum design selection approach for product customization development

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Abstract

Consumer preferences and information on product choice behavior can be of significant value in the development processes of innovative products. In this paper, product customization evaluation and selection model is introduced to support imprecision inherent of qualitative inputs from customers and designers in the decision making process. Focusing on customer utility generation, an optimum design selection approach based on fuzzy set decision-making is proposed, where design attributes priority is identified from customer preferences using an analytical hierarchy process. A multi-attribute analysis diagram is developed to visualize the preference of each attribute from the expert’s group decision. Conjoint analysis is used in the product customization to focus on customer utility generation in terms of multiple criteria. The use of the decision-making method is illustrated with a case example that highlights the utility of the proposed method.

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References

  • Albayrak E., Erensal Y. C. (2004) Using analytic hierarchy process (AHP) to improve human performance: An application of multiple criteria decision making problem. Journal of Intelligent Manufacturing 15: 491–503

    Article  Google Scholar 

  • Ayag Z. (2005) A fuzzy AHP-based simulation approach to concept evaluation in a NPD environment. IIE Transactions 37(9): 827–842

    Article  Google Scholar 

  • Besharati B., Azarm S., Kannan P. (2006) A decision support system for product design selection: A generalized purchase modeling approach. Decision Support Systems 42(1): 333–350

    Article  Google Scholar 

  • Büyüközkan G., Feyziǒlu O., Ruan D. (2007) Fuzzy group decision-making to multiple preference formats in quality function deployment. Computers in Industry 58(5): 392–402

    Article  Google Scholar 

  • Chen C., Khoo L., Yan W. (2005) PDCS-a product definition and customisation system for product concept development. Expert Systems with Applications 28(3): 591–602

    Article  Google Scholar 

  • Chen S. (1985) Ranking fuzzy numbers with maximizing set and minimizing set. Fuzzy Sets and Systems 17(2): 113–129

    Article  Google Scholar 

  • Chiang D., Guo R., Pai F. (2008) Improved customer satisfaction with a hybrid dispatching rule in semiconductor back-end factories. International Journal of Production Research 46(17): 4903–4923

    Article  Google Scholar 

  • Farquhar P. (1977) A survey of multiattribute utility theory and applications. TIMS Studies in the Management Sciences 6: 59–89

    Google Scholar 

  • Green P., Srinivasan V. (1978) Conjoint analysis in consumer research: issues and outlook. Journal of consumer research 5(2): 103–123

    Article  Google Scholar 

  • Jiao J., Tseng M. (1998) Fuzzy ranking for concept evaluation in configuration design for mass customization. Concurrent Engineering 6(3): 189–206

    Article  Google Scholar 

  • Kwong C. K., Bai H. (2002) A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. Journal of Intelligent Manufacturing 13: 367–377

    Article  Google Scholar 

  • Louviere J., Hensher D., Swait J. (2000) Stated choice methods: analysis and applications. Cambridge University Press

  • Liu, C., Ramirez-Serrano, A., & Yin, G. F. (2009). Customer-driven product design and evaluation method for collaborative design environments. Journal of Intelligent Manufacturing. doi:10.1007/s10845-009-0334-2.

  • Saaty T. L. (1980) The analytic hierarchy process. McGraw-Hill, New York

    Google Scholar 

  • Shao X. Y., Wang Z. H., Li P. G., Feng X. J. (2006) Integrating data mining and rough set for customer group-based discovery of product configuration rules. International Journal of Production Research 44(14): 2789–2811

    Article  Google Scholar 

  • Thurston D., Crawford C. (1994) A method for integrating end-user preferences for design evaluation in rule-based systems. Journal of Mechanical Design 116(2): 522–530

    Article  Google Scholar 

  • Tsai H., Hsiao S. (2004) Evaluation of alternatives for product customization using fuzzy logic. Information Sciences 158: 233–262

    Article  Google Scholar 

  • Vanegas L., Labib A. (2005) Fuzzy approaches to evaluation in engineering design. Journal of mechanical design 127(1): 24–33

    Article  Google Scholar 

  • Xu L., Li Z., Li S., Tang F. (2007) A decision support system for product design in concurrent engineering. Decision Support Systems 42(4): 2029–2042

    Article  Google Scholar 

  • Zadeh L. (1965) Fuzzy sets. Information and Control 8: 338–353

    Article  Google Scholar 

  • Zha X., Sriram R., Lu W. (2005) Evaluation and selection in product design for mass customization: A knowledge decision support approach. AI EDAM 18(01): 87–109

    Google Scholar 

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Correspondence to Guofu Yin.

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Liu, C., Ramirez-Serrano, A. & Yin, G. An optimum design selection approach for product customization development. J Intell Manuf 23, 1433–1443 (2012). https://doi.org/10.1007/s10845-010-0473-5

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