Evaluation and Selection in Product Design for Mass Customization
XuanFang Zha, Ram D. Sriram, W F. Lu, Fujun Wang
Mass customization has been identified as a competitive strategy by an increasing number of companies. Family-based product design has been recognized as an efficient and effective means to realize sufficient product variety to satisfy a range of customer demands in support for mass customization. This chapter presents a knowledge-supported approach to concept evaluation and selection in design for the mass customization process. The focus of this chapter is on the development of a knowledge intensive support scheme and a comprehensive systematic fuzzy clustering and ranking methodology for concept design evaluation and selection. In the chapter, product family design is viewed as a selection problem with the following key stages: product family (design alternatives) generation, product family design evaluation, and selection for customization. First, the fundamental issues underlying product family design for mass customization are identified and discussed. Then, a knowledge support framework and its relevant technologies are developed for module-based product family design for mass customization. A systematic fuzzy clustering and ranking model that models imprecision inherent in decision-making with fuzzy customers? preference relations and carrying out fuzzy analysis and evaluation in solving the multi-criteria decision making problem during the early design stage is proposed and discussed in detail. The neural network technique is used to adjust the membership function. The proposed model is illustrated by a case study of knowledge support for power supply product evaluation, selection, and customization.
Theme Volumes on Business and Technology in the New Millennium
and knowledge support, decision-making, design evaluation, fuzzy clustering, fuzzy ranking, mass customization, Product family (line)
, Sriram, R.
, Lu, W.
and Wang, F.
Evaluation and Selection in Product Design for Mass Customization, Kluwer Academic Press, , [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822108
(Accessed June 10, 2023)