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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]

Although many conceptual design methods have been proposed, there is still ample room for improvement. First, most conceptual design methods do not relate concepts generation by a combination of basic features to the feasibility of the concepts. Second, those methods may have ignored customer preferences in modeling concept selection. To overcome these shortcomings, a quality-engineering-based conceptual design approach is proposed in this paper by integrating the use of quality function deployment (QFD), morphological matrix analysis (MMA), multi-attribute decision-making techniques (MADM), and possibilistic optimization models (POMs). A possibility distribution function (PDF) is applied to account for the uncertainties of technology development cost in the early design stages, and a mathematical framework is established by incorporating the customer-perceived relative importance into a possibilistic optimization model for design concept selection, which plays a central role in the proposed methodology. Contrary to deterministic optimization models (DOMs), the proposed possibilistic optimization can produce results that are more informative and more reliable to the designers. [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]

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]

In quality function deployment (QFD), establishing the relationship matrix is quite an important step to transform ambiguous and qualitative customer requirements into concrete and quantitative technical characteristics. Owing to the inherent imprecision and fuzziness of the matrix, the fuzzy linear regression (FLR) is gradually applied into QFD to establish it. However, with regard to an FLR model, the h value is a critical parameter whose setting is always an aporia and it is commonly determined by decision makers. To a certain extent, this subjective assignment fades the effectiveness of FLR in the application of QFD. Aiming to this problem, FLR models with optimized parameters h obtained by maximizing system credibility are introduced into QFD in this paper, in which relationship coefficients are assumed as asymmetric triangular fuzzy numbers. Moreover, a systematic approach is developed to identify the relationship matrix in QFD, whose application is demonstrated through a packing machine example. The final results show that FLR models with optimized h values can always achieve a more reliable relationship matrix. Besides, a comparative study on symmetric and asymmetric cases is elaborated detailedly. [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]