Evaluation and selection in product design for mass customization: A knowledge decision support approach

  • Authors:

  • Xuan F. Zha;Ram D. Sriram;Wen F. Lu

  • Affiliations:

  • Manufacturing System Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA;Manufacturing System Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA;Product Design and Development Group, Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075

  • Venue:
  • Artificial Intelligence for Engineering Design, Analysis and Manufacturing - SPECIAL ISSUE: Platform product development for mass customization
  • Year:
  • 2004

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Abstract

Mass customization has been identified as a competitive strategy by an increasing number of companies. Family-based product design is an efficient and effective means to realize sufficient product variety, while satisfying a range of customer demands in support for mass customization. This paper presents a knowledge decision support approach to product family design evaluation and selection for mass customization process. Here, product family design is viewed as a selection problem with the following stages: product family (design alternatives) generation, product family design evaluation, and selection for customization. The fundamental issues underlying product family design for mass customization are 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 is proposed and discussed in detail. This model supports the imprecision inherent in decision making with fuzzy customers' preference relations and uses fuzzy analysis techniques for evaluation and selection. A neural network technique is also adopted to adjust the membership function to enhance the model. The focus of this paper is on the development of a knowledge-intensive support scheme and a comprehensive systematic fuzzy clustering and ranking methodology for product family design evaluation and selection. A case study and the scenario of knowledge support for power supply family evaluation, selection, and customization are provided for illustration.