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Timely identification of heterogeneous customer requirements serves as a vital step for a company to formulate product strategies to meet the diverse and changing needs of its customers. By relaxing the search for global patterns in classical clustering, we propose a biclustering-based method, BiHCR, to identify heterogeneous customer requirements from the perspective of local patterns detection. Specifically, conforming to customers' attitudes toward products derived from customer participation, we first transform the original data matrix with customers as rows and customer requirements as columns into a binary matrix. Then, by combining the two significant biclustering algorithms, Bimax and RepBimax, we design BiHCR to identify the biclusters embedded in the binary matrix to improve the detection results from the larger biclusters and their overlaps. Furthermore, the empirical case of smartphone development in a Chinese company verifies that BiHCR can identify homogeneous subgroups of customers with similar requirements without redundant noise compared with Bimax. Additionally, in contrast to RepBimax, our proposed BiHCR can also detect the intractable overlapping biclusters in the binary matrix used to describe the heterogeneity of customer requirements. Since the process of customer participation in product development gradually became a dominant approach to collecting customer requirements information for many industries, a conceptual framework of customer requirements identification is constructed and the detailed steps are clarified for manufacturers. [ABSTRACT FROM AUTHOR]

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]

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]

Abstract: Product planning is one of four important processes in new product development (NPD) using quality function deployment (QFD), which is a widely used customer-driven approach. In our opinion, the first problem to be solved is how to incorporate both qualitative and quantitative information regarding relationships between customer requirements (CRs) and engineering characteristics (ECs) as well as those among ECs into the problem formulation. Owing to the typical vagueness or imprecision of functional relationships in a product, product planning is becoming more difficult, particularly in a fuzzy environment. In this paper, an asymmetric fuzzy linear regression approach is proposed to estimate the functional relationships for product planning based on QFD. Firstly, by integrating the least-squares regression into fuzzy linear regression, a pair of hybrid linear programming models with asymmetric triangular fuzzy coefficients are developed to estimate the functional relationships for product planning under uncertainties. Secondly, using the basic concept of fuzzy regression, asymmetric triangular fuzzy coefficients are extended to asymmetric trapezoidal fuzzy coefficients, and another pair of hybrid linear programming models with asymmetric trapezoidal fuzzy coefficients is proposed. The main advantage of these hybrid-programming models is to integrate both the property of central tendency in least squares and the possibilistic property in fuzzy regression. Next, the illustrated example shows that trapezoidal fuzzy number coefficients have more flexibility to handle a wider variety of systematic uncertainties and ambiguities that cannot be modeled efficiently using triangular number fuzzy coefficients. Both asymmetric triangular and trapezoidal fuzzy number coefficients can be applicable to a much wider variety of design problems where uncertain, qualitative, and fuzzy relationships are involved than when symmetric triangular fuzzy numbers are used. Finally, future research direction is also discussed. [Copyright &y& Elsevier]