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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]

In recent years, the fuzzy linear regression (FLR) approach is widely applied in the quality function deployment (QFD) to identify the vague and inexact functional relationships between the customer requirements and the engineering characteristics on account of its advantages of objectiveness and reality. However, the h value, which is a vital parameter in the proceeding of the FLR model, is usually set by the design team subjectively. In this paper, we propose a systematic approach using the FLR models attached with optimized h values to identify the functional relationships in QFD, where the coefficients are assumed as symmetric triangular fuzzy numbers. The h values in the FLR models are determined according to the criterion of maximizing the system credibilities of the FLR models. Furthermore, an illustrative example is provided to demonstrate the performance of the proposed approach. Results of the numerical example show that the fuzzy coefficients obtained through the FLR models with optimized h values are more effective than those obtained through the FLR models with arbitrary h values selected by the design team. [ABSTRACT FROM AUTHOR]

Market segmentation plays a crucial role in product design and development. However, conventional segmentation approaches based on one-way cluster analysis techniques have met two special challenges in practice. First, conventional approaches that derive a global result rather than a local one fail to cluster customers into such groups who have similar characteristics on a fraction of variables. Second, since there is no formal mechanism to select appropriate segmentation variables, different combination of variables will obtain different segmentation results, which makes the approaches not quite convincing. To overcome the two limitations, a novel biclustering-based market segmentation method by using customer pain points is proposed in this paper. Different from one-way algorithms clustering only rows or only columns, biclustering algorithms cluster both rows associated with customers and columns associated with customer pain points simultaneously to identify homogenous subgroups of customers with common characteristics towards a subset of segmentation variables. In addition, customer pain points are used to replace traditional segmentation variables in the presented method, which makes the results more reasonable. Subsequently, an illustrated example is studied to demonstrate the effectiveness of the presented method. [ABSTRACT FROM AUTHOR]