Optimizing product design using the Kano model and QFD

In the current decade, client requirements appear to play an increasingly important role in designing not‐for‐profit organisations, in particular in the domains of services and healthcare. Quality function deployment (QFD) is a well‐known design method. This method has a vested reputation in industrial production as a means of systematically incorporating customer requirements in product design. However, in the domain of the services, and especially the professional services, there is little experience in applying QFD. Application in this domain probably causes problems, for instance with respect to the customer concept, which is more ambiguous in this domain, and with respect to the interrelated nature of the product (service) and the process. In this paper we present some limitations of conventional QFD outside physical industrial production and we present a refinement and an extension of QFD for healthcare applications, based on research methods in the social sciences. Illustrations are given from two cases in Dutch healthcare organisations.

This paper shows a new method for integration of Kano's model in quality function deployment (QFD) through a case study of evaluating the 9th China-Asean Exposition. The QFD could help collect customer's needs and translate them into the relative arrangement and management of exposition. But in order to understand the customers' needs accurately, the paper introduces Kano's model so as to distinguish the excitement needs, the basic needs and the performance needs. This methodology has been used in product design and production plan. This study uses it to evaluate that the exposition satisfies its exhibitors' requirements to what extent, which starts a new application field for QFD and Kano's model. Finally, this paper gives some implication advice for the China-Asean Exposition so as to help improve its quality.

Quality function deployment (QFD) is a systematic approach to transform customer requirements (CRs) into product engineering characteristics (ECs). Traditional QFD relies on market research or customer questionnaires to collect a series of ambiguous and uncertain CRs. As a result, evaluating the weighting of CRs and determining the design matrix between CRs and ECs have become the focus and difficulty of QFD. This paper proposes the grey system theory in artificial intelligence technology combined with QFD to develop grey-QFD to solve the issues mentioned before. First, collect the average evaluation values between the aesthetic images and customer satisfaction of representative products. The grey prediction GM (1, N) model is used to obtain the weight of aesthetic needs relative to customer satisfaction and import it into the left QFD. Second, the domain experts decomposed the product form into a morphological analysis table, and fuzzy Delphi screened key ECs and imported them into the ceiling of QFD. Finally, grey relationship analysis established the aesthetic product design matrix between CRs and ECs, and calculated and ranked the final weights of each ECs by using grey relationship degree. The research uses the security camera in the smart home as an experimental object. After operating the proposed grey-QFD, the aesthetic quality of the target product (lively, intelligent, friendly, personalized, and fashionable) and the optimization of the corresponding product ECs are obtained. The result provides a theoretical reference for designers and significantly improves customer aesthetic satisfaction.

Quality function deployment (QFD) is becoming a widely used customer-driven approach and tool in product design. The inherent fuzziness in QFD modelling makes fuzzy regression more appealing than classical statistical tools. A new fuzzy regression-based mathematical programming approach for QFD product planning is presented. First, fuzzy regression theories with symmetric and non-symmetric triangular fuzzy coefficients are discussed to identify the relational functions between engineering characteristics and customer requirements and among engineering characteristics. By embedding the relational functions obtained by fuzzy regression, a mathematical programming model is developed to determine targets of engineering characteristics, taking into consideration the fuzziness, financial factors and customer expectations among the competitors in product development process. The proposed modelling approach can help design team assess relational functions in QFD effectively and reconcile tradeoffs among the various degree of customer satisfaction and determine a set of the level of attainment of engineering characteristics for the new/improved product towards a higher customer expectation within design budget. The comparison results under symmetric and non-symmetric cases and the simulation analysis are made when the approach is applied to a quality improvement problem for an emulsification dynamite packing machine.

Quality function deployment (QFD) is a planning and problem-solving tool that is gaining acceptance for translating customer requirements into the engineering characteristics of a product. Deriving the rating order of engineering characteristics from input variables is a crucial step in applying QFD. A grey method is proposed to prioritize engineering characteristics. The engineering characteristics are considered to be comparability sequences, while the customer requirements in customer competitive analysis are considered to be reference sequences. So QFD can be regarded as a grey system and the relationships between the customer requirements and engineering characteristic can be determined with the grey relational matrix. Then grey superior analysis is used to prioritize the engineering characteristics. An example of the door design of an automobile is used to demonstrate the application of the proposed approach.

Sustainable supply chain management plays an important role in company’s sustainability success. As the frequently emerging disruptions may harm supply chain’s sustainable performance, managers need to design an effective sustainable supply chain (SSC) resilience solution to mitigate different risks. In addition, it is critical to integrate customer requirements (CRs) in SSC resilience solution design as SSC’s performance is highly determined by customer satisfaction (CS) in the customer-oriented market. This article proposes an integrative approach to achieve an effective design of SSC resilience solution, taking both CRs and risk factors (RFs) into consideration. In this study, the Kano model, decision-making and trial evaluation laboratory (DEMATEL) are integrated with quality function deployment (QFD) through nonlinear programming to guide companies’ design. Kano model is employed to analyse CRs qualitatively and quantitatively to determine each CR’s priority and CS. DEMATEL is adopted to determine the RFs’ interrelationships. The results obtained with the Kano model and DEMATEL are incorporated into QFD to transform CRs into RFs, and then into resilience measures. Furthermore, a nonlinear optimisation model is formulated to achieve the optimal resilience solution with maximised CS and risk mitigation under minimum cost investment. Finally, a case study is conducted to verify the methodology.

As a powerful tool for improving customer satisfaction, quality function deployment (QFD) can convert customer requirements (CRs) into engineering characteristics (ECs) during product development and design. Aiming to address the deficiencies of traditional QFD in expert evaluation, CRs’ weight determination and ECs’ importance ranking, this paper proposes an enhanced QFD model that integrates hesitant fuzzy binary semantic variables, the Best–Worst Method (BWM), and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The objective is to determine the prioritization of product engineering characteristics. Indeed, hesitant fuzzy linguistic term sets (HFLTS) have found extensive application in decision-making problems. Compared to other fuzzy language methods, HFLTS offers greater convenience and flexibility in addressing decision-makers’ hesitations and uncertainties. Initially, the combination of hesitant fuzzy linguistic term sets with interval binary tuple language variables is employed to articulate the uncertainty in the assessment information provided by QFD team members. Subsequently, the improved BWM and TOPSIS methods based on HFLTS are used to improve the accuracy of the importance ranking of engineering characteristics by determining the weights of CRs and prioritizing ECs in two stages. Finally, the feasibility and effectiveness of the proposed method are validated through an illustrative example.

The asymmetry of knowledge with regard to user requirements for a shared research equipment service makes it difficult to develop user-oriented strategies and policies. To resolve this issue, we developed a framework using the Quality Function Deployment (QFD) process and Kano's model for application to a case study on innovation in the shared research equipment service at the Gyeonggi Bio-Center. Kano's model allows us to define which service quality requirements are attractive, one-dimensional or must-be qualities in the shared research equipment service. The QFD process has rarely been applied to the shared research equipment service (which is closely related with technology infrastructure policy). The newly developed framework consists of three actors: ‘user’, ‘service institution’ and ‘provincial government’. The QFD process in the framework is performed by a two-step deployment of service deployment and policy deployment that bridges the knowledge gap among the three actors in the regional innovation system. Alternatives and implications for shared research equipment service with a focus on user-friendliness have been systematically produced through application of the suggested framework.

As a useful product development technique, quality function deployment (QFD) can convert customer requirements into suitable engineering characteristics for maximum customer satisfaction. Nonetheless, the traditional QFD has been extensively criticized as having many defects in practical applications, which include the crisp evaluation of relationships between customer requirements and engineering characteristics, and the non-robust ranking of engineering characteristics. In this paper, we put forward a new method using double hierarchy hesitant linguistic term sets (DHHLTSs) and axiomatic design approach to enhance the performance of the classical QFD. Firs, the DHHLTSs are employed to evaluate the relationships between customer requirements and engineering characteristics. Then, the axiomatic design theory is extended to derive the prioritization of engineering characteristics in QFD. Additionally, a maximizing consensus method is used to determine the weights of experts considering inconsistency among their opinions. Finally, an empirical case of a shared car manufacturer is provided with a comparison analysis to demonstrate the feasibility and effectiveness of our proposed QFD approach.

Rail Transportation is the best option of transport in many countries of the world. One of the factors that increase the share of rail passenger transportation is developing rail transportation of passenger by increasing level of customer satisfaction in this type of service. The main purpose of this paper is service design by identification and prioritization of customer demands in Kano model. The statistical population consisted of travelers who used the Iran South-East Railway services. To determine the sample size, as there was not any certain statistical test as research requirement, we used the saturated sample, so that we selected samples as long as the opinions became converged. Semi-structured interviews, interviews with experts and a questionnaire for a two-week period in January 2014 were used to collect data. The final questionnaire was compiled and its validity was confirmed after obtaining the expert’s points of view and fixing deficiencies and problems. Cronbach’s alpha coefficient was used to assess the reliability of questionnaire. Descriptive and inferential statistics were used to analyze the obtained information from the questionnaires. Kano model, QFD method in the SPSS and Excel were used for the inferential statistics. Data analysis in the Kano model and QFD method was done in Excel software; and Spss16 software was used to measure the reliability. After identifying the characteristics of service with the help of experts, quality Function Development (QFD) was used to prioritize them and finally, required services has been ranked and proper selection of personnel, upgrade facilities of rail and the exact timing of train schedules are 3 first priority, if first six priorities are done, almost half of the costumers will be satisfied.

Quality function deployment (QFD) is a systematic approach that captures customer requirements and translates them, through house of quality (HOQ), into engineering characteristics of product. As the functional relationships between customer requirements and engineering characteristics in QFD are uncertain, unclear and fuzzy, radial basis function (RBF) to determine the functional relationships for QFD is presented, and a QFD functional relationships model based on RBF is proposed. According to RBF neural network can realize the nonlinear mapping space from the input space to the output, and can obtain the optimal relationships pattern of the input and output, the customer requirements and engineering characteristics in QFD constituted the input and output of the RBF neural network respectively, the optimal relationships are constructed through the neural network training. A case study of natural lighting products development is provided to illustrate the application of the presented method.

Understanding human needs and user requirements is very critical to realize appropriate and acceptable product and system design.The primary measure of the success of a product, service, or software system is in its ability to meet the purpose for which it was intended (Jones, I. et al. 2015).To translate customer needs into more quantifiable engineering requirements and utilize the requirements for design, several methodologies have been established and used within the design community.Examples include Quality Function Deployment (QFD) based methods (Pullman, M.

An improved grey quality function deployment approach using the grey TRIZ technique

PurposeThe concept of sustainable product design (SPD) is gaining significant attention in recent research. However, due to inherent uncertainties associated with new product development and incorporation of multiple qualitative and quantitative criteria; SPD is a complex and challenging task. The purpose of this paper is to introduce a novel approach by integrating quality function deployment (QFD), multi-criteria decision making (MCDM) technique and Six Sigma evaluation for facilitating SPD in the context of Industry 4.0.Design/methodology/approachThe customer requirements are evaluated through the neutrosophic-decision-making trial and evaluation laboratory-analytic network process (DEMATEL-ANP)-based approach followed by utilizing QFD matrix to estimate the weights of the engineering characteristics (EC). The Six Sigma method is then employed to evaluate the alternatives’ design based on the ECs’ values.FindingsThe effectiveness of the suggested approach is illustrated through an example. The result indicates that utilization of the neutrosophic MCDM technique with integration of Six Sigma methodology provides a simple, effective and computationally inexpensive method for SPD.Practical implicationsThe proposed approach is helpful in upstream evaluation of the product design with limited experimental/numerical data, maintaining a strong competitive position in the market and enhancing customer satisfaction.Originality/valueThis work provides a novel approach to objectively quantify performance of SPD under the paradigm of Industry 4.0 using the integration of QFD-based hybrid MCDM with Six Sigma method.

Nowadays, many service provider organizations compete to survive and surpass other competitors in the world.They apply new techniques and instruments to identify and to prioritize important criteria for their customers to gain customer satisfaction.Banks, as one of the service provider organizations, are no exception.Quality plays essential role in banking industry and customer' gratification is considered as one of the major and essential goals in this field.Recognition and awareness regarding the customers' needs and requirements would facilitate providing satisfactory services.It could be said that improved understanding, accurate identification and prioritization of bank customers' requirements are the keys to success for bank managers.The present study aims to integrate two approaches of Quality Function Deployment (QFD) and Kano's model through implementation of Analytical Hierarchy Process (AHP).This study proposes a novel approach to identify and to analyze the priorities of bank customers' requirements.The results indicate that the priorities of bank customers are different before and after integration of Kano's Model in the planning matrix of QFD.