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Quality function deployment (QFD) is a useful tool for maximising customer satisfaction in the new product planning (NPP) process. When applying QFD, recognising the importance levels of customer requirements (CRs) is a vital issue, since they have great influences on the values of decision variables, i.e. the fulfilment levels of design requirements (DRs), for NPP. Unlike existing researches, this study evaluates the importance scores of CRs from three different perspectives: the customer, competition and value perspectives. To cope with the vague and uncertain nature at the NPP stage, various fuzzy approaches are adopted for measuring the importance scores of CRs from different perspectives. The three measures are aggregated as the evaluated importance score of each CR. In addition, different to prior studies, this paper modifies Chen and Weng’s model to develop a new normalised relationship between CRs and DRs to improve the existing models’ drawbacks. A fuzzy linear programming model is established to determine the optimal fulfilment levels of DRs to maximally satisfy CRs. Based on an illustrative example, the proposed method achieves greater total customer satisfaction than previous methods. Furthermore, this paper also proposed a normalised relationship formulation with conventional way to reasonablely modify Wasserman’s model. [ABSTRACT FROM PUBLISHER]

The prioritisation of design requirements is critical for determining resource allocation in the new product design stage through the quality function deployment (QFD) planning processes. To prioritise design requirements, normalisation models are usually used to perform aggregation and normalisation functions. Recognising the weakness of the Wasserman normalisation model from both theoretical and practical viewpoints, this paper proposes an improved normalisation model for the aggregation and normalisation functions. It is verified that the proposed model satisfies Lyman’s normalisation requirement and avoids the problem of Wasserman’s normalisation model. In addition, a normalisation model that takes into account the correlation among customer requirements is also developed to complete a generalised normalisation model for QFD planning processes. A product design case is presented to demonstrate the advantages of the proposed normalisation models. [ABSTRACT FROM PUBLISHER]

Quality function deployment (QFD) is a useful planning approach for constructing new product designs or modifying existing products. The approach has been widely adopted in various applications. For QFD processes, the house of quality (HOQ) is first constructed to describe customer requirements (CRs) and their importance, design requirements (DRs), the relationships between CRs and DRs, and the correlations among DRs. This study proposes a systematic procedure for constructing the HOQ and describes its application from a group decision-making (GDM) perspective. Considering the imprecise information in the design stage, fuzzy set theory is employed to develop fuzzy approaches for constructing the HOQ. In practical applications, a QFD team is usually organized to collect relevant information; therefore, GDM approaches are incorporated into the construction process of the HOQ. To reflect the differences between decision-makers’ evaluations of elements in the HOQ, various ways of assessing the components of the HOQ are allowed. A modified fuzzy clustering approach is proposed for finding the consensus of the QFD team. Based on the established HOQ, approaches are presented for prioritizing DRs as a reference for allocating internal resources to appropriate DRs in order to optimally satisfy CRs. A numerical example is used to demonstrate the applicability of the proposed approaches. [ABSTRACT FROM PUBLISHER]

Quality function deployment (QFD) frameworks are useful tools for constructing a new product development (NPD) plan that enables the clear itemization of customer needs and the systematic evaluation of each solution to maximize customer satisfaction. A complete QFD process includes four sequential phases in which four important decision outcomes are determined for NPD, namely, the fulfillment levels of design requirements (DRs), part characteristics, process parameters, and production requirements. Unlike prior studies which have focused only on determining DRs, this paper extends Chen and Ko's models to consider the close link between the four phases in NPD using the means-end chain concept to build up a series of fuzzy nonlinear programming models for determining the fulfillment levels of each decision outcome for customer satisfaction. In addition, this paper incorporates risk analysis, which is treated as the constraint in the models, into the QFD process. To deal with the vague nature of product development processes, fuzzy sets are applied for both QFD and risk analysis. A numerical example is used to demonstrate the applicability of the proposed model. [ABSTRACT FROM PUBLISHER]

Abstract: For new product development, quality function deployment (QFD) is a useful approach to maximize customer satisfaction. The determination of the fulfillment levels of design requirements (DRs) and parts characteristics (PCs) in phases 1 and 2 is an important issue during QFD processes for new product design. Unlike the existing literature, which mainly focuses on the DRs, this paper proposes fuzzy nonlinear programming models based on Kano''s concept to determine the fulfillment levels of PCs with the aim of achieving the determined contribution levels of DRs in phase 1 for customer satisfaction. In addition, to deal with the design risk, this study incorporates failure modes and effects analysis (FMEA) into QFD processes, and treats it as the constraint factor in the models. To cope with the vague nature of product development processes, fuzzy approaches are used for both FMEA and QFD. The applicability of the proposed models in practice is demonstrated with a numerical example. [Copyright &y& Elsevier]

Abstract: Quality function deployment (QFD) is a customer-driven approach in processing new product developments in order to maximize customer satisfaction. Determining the fulfillment levels of design requirements (DRs) and parts characteristics (PCs) is an important decision problem during QFD activity processes for new product development. Unlike the existing literature, which mainly focuses on the determination of DRs, this paper proposes fuzzy linear programming models to determine the fulfillment levels of PCs under the requirement to achieve the determined contribution levels of DRs for customer satisfaction. In addition, considering the design risk, this paper incorporates failure modes and effect analysis (FMEA) into QFD processes, which is treated as the constraint in the models. To cope with the vague nature of product development processes, fuzzy approaches are used for both FMEA and QFD. The illustration of the proposed models is performed with a numerical example to demonstrate the applicability in practice. [Copyright &y& Elsevier]

Abstract: Quality function deployment (QFD) is a customer-driven approach for processing new product developments in order to maximize customer satisfaction. Each engineering design characteristic is maximized for product performance according to the level of customer satisfaction. To cope with the vague nature of product development processes, fuzzy approaches are used to represent the importance scores of customer requirements (CRs), the relationship between CRs and design requirements (DRs) and relationship between the DRs themselves. Considering Kano’s category of design requirements, this paper extends Chen and Weng’s model (2003), and presents a fuzzy nonlinear model to determine the performance level of each DR for maximizing customer satisfaction, under the same group of constraints as that in Chen and Weng’s model. The results, from an illustrative example, indicate that the total degree of customer satisfaction achieved by the proposed nonlinear model is greater than that by the original fuzzy model. [Copyright &y& Elsevier]

Quality function deployment (QFD) is the product development process to maximize customer satisfaction. The engineering design characteristics related to product performance are specified for this purpose. For dealing with the fuzzy nature in the product design processes, fuzzy approaches are applied to represent the relationships between customer requirements (CRs) and engineering design requirements (DRs) as well as among the DRs. A new measure for evaluating the fuzzy normalized relationships is derived. A fuzzy model is formulated to determine the fulfillment level of each DR for maximizing the customer satisfaction under the resource limitation and the considerations of technical difficulty and market competition. The producing ranges of fulfillment level of each DR and those of customer satisfaction can provide the QFD team with more information. An example is used to illustrate the model. [Copyright &y& Elsevier]