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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.12.026 Byline: Yizeng Chen (a), Richard Y.K. Fung (a), Jiafu Tang (b) Keywords: Quality function deployment; House of quality; Rating technical attributes; Fuzzy weighted average; Fuzzy expected value operator Abstract: Quality function deployment (QFD) is a planning and problem-solving tool that is gaining acceptance for translating customer requirements into the technical attributes of a product. Deriving the rating order of technical attributes from input variables is a crucial step in applying QFD. When the relative weights of customer requirements and the relationship measures between customer requirements and technical attributes are expressed as fuzzy numbers, calculating the importance of each technical attribute falls into the category of fuzzy weighted average, in which the derived membership function of the fuzzy importance of each technical attribute is not explicitly known. Thus, most ranking methods are not suitable under these circumstances. A method is proposed in this paper using fuzzy weighted average method in the fuzzy expected value operator in order to rank technical attributes in fuzzy QFD. An example of a flexible manufacturing system design is cited to demonstrate the application of the proposed approach. Author Affiliation: (a) Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong (b) Key Laboratory of Process Industrial Automation of MOE, School of Information Science and Engineering, Northeastern University (NEU), Shenyang, Liaoning, PR China Article History: Received 11 February 2004; Accepted 16 December 2004

With increasing concerns on customer needs in today’s competitive market, the issue of incorporating customer requirements into product design arises the interest of both researchers and practitioners. Quality Function Deployment (QFD) is a well-known methodology for customer-driven product design. However, conventionally, QFD analysis has a major challenge in understanding customer needs accurately. Kano’s model, which studies the nature of customer needs, provides a way for a better classification of customer needs. However, seldom research contributions are found in terms of integrating Kano’s model with QFD quantitatively. In this research, a novel integration approach is proposed. At first, Kano’s model is quantified by identifying relationship between customer needs and customer satisfaction (CS). Next, both qualitative and quantitative results from Kano’s model are integrated into QFD. Finally, a mixed non-linear integer programming model is formulated to maximise CS under cost and technical constraints. In this research, an illustrative example associated with the design of notebook computers is also presented to demonstrate the availability of the proposed approach. [ABSTRACT FROM PUBLISHER]

The parameter h in a fuzzy linear regression model is vital since it influences the degree of the fitting of the estimated fuzzy linear relationship to the given data directly. However, it is usually subjectively pre-selected by a decision-maker as an input to the model in practice. In Liu and Chen (2013) , a new concept of system credibility was introduced by combining the system fuzziness with the system membership degree, and a systematic approach was proposed to optimize the h value for fuzzy linear regression analysis using the minimum fuzziness criterion with symmetric triangular fuzzy coefficients. As an extension, in this paper, their approach is extended to asymmetric cases, and the procedure to find the optimal h value to maximize the system credibility of the fuzzy linear regression model with asymmetric triangular fuzzy coefficients is described. Some illustrative examples are given to show the detailed procedure of this approach, and comparative studies are also conducted via the testing data sets. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) can provide a means of translating customer requirements (CRs) into engineering characteristics (ECs) for each stage of product development and production. The main objective of QFD-based product planning is to determine the target levels of ECs for a new product or service. QFD is a breakthrough tool which can effectively reduce the gap between CRs and a new product/service. Even though there are conflicts among some ECs, the objective of developing new product is to maximize the overall customer satisfaction. Therefore, there may be room for cooperation among ECs. A cooperative game framework combined with fuzzy set theory is developed to determine the target levels of the ECs in QFD. The key to develop the model is the formulation of the bargaining function. In the proposed methodology, the players are viewed as the membership functions of ECs to formulate the bargaining function. The solution for the proposed model is Pareto-optimal. An illustrated example is cited to demonstrate the application and performance of the proposed approach.