Engineering Characteristics In Quality Function Deployment Research Articles

New approach for quality function deployment based on multi-granular unbalanced linguistic information and consensus reaching process

As a widely used product improvement technique, the quality function deployment (QFD) can satisfy customer requirements and realize higher customer satisfaction by translating them into corresponding engineering characteristics. Nonetheless, two basic challenges hinder the effective application of QFD are the imprecise relationship assessments between customer requirements and engineering characteristics because of the uncertainty inherent in human judgements and the unreasonable importance ranking of engineering characteristics due to the heterogeneity of domain experts. In response, this paper puts forward a new hybrid QFD approach for the priority of engineering characteristics to satisfy customer requirements. First, multi-granular unbalanced linguistic term sets are utilized to describe the vague relational evaluations between customer requirements and engineering characteristics. Then, the opinion evolution social network consensus reaching model is employed to assist QFD experts in deriving consensual relational evaluations. Taking the conflict customer requirements into consideration, the combined compromise solution method is adopted and extended to derive the priority orders of engineering characteristics. To illustrate the practicality and effectiveness of the proposed QFD approach, a product development case about low pressure pulse filter is provided with comparative analysis and simulation experiments. The results show that the proposed approach can represent complex linguistic relationship assessments of experts and determine more accurate priority orders of engineering characteristics in QFD.

Consensus-driven methodology to managing diversity and complex linguistic ratings in quality function deployment: An optimization-based approach

Quality function deployment (QFD) is a quality management tool that can effectively transform customer requirements (CRs) into engineering characteristics (ECs) of services or products. It is a crucial process to determine the prioritization of ECs from the assessments of the relationship between CRs and ECs in QFD. This study designs several optimization models to derive the consensual prioritization of ECs from the diverse and complex assessments, in which the assessments of QFD team members are modelled by comparative linguistic expressions (CLEs). First, a consensus-driven optimization model (CDOM) is presented to manage CLEs by transforming them into linguistic distribution assessments. Then, a minimum adjustment element consensus model (MAECM) is developed to derive the consensual assessments by minimizing the number of adjustment elements between the members’ original and adjusted assessments. Following this, a technique for order preference by the similarity to ideal solution (TOPSIS) based method is used to derive the prioritization of ECs from the consensual assessments. A case study regarding the improvement of window services in the Bank of JT Shanghai Branch is proposed to illustrate the implementation of the presented method. The comparative analysis shows that the presented method outperforms previous methods in managing CLEs and consensus.

New approach for quality function deployment with an extended alternative queuing method under linguistic Pythagorean fuzzy environment

As a customer-driven tool to support the development of new products or services, quality function deployment (QFD) is able to transform customer requirements (CRs) into suitable engineering characteristics (ECs) to maximise the customer satisfaction. However, the conventional QFD method has some weaknesses limiting its effectiveness in the real applications. In this study, a new QFD approach based on linguistic Pythagorean fuzzy sets (LPFSs) and alternative queuing method (AQM) is constructed to enhance the performance of the traditional QFD. First, we express the assessments from experts on the relationships between CRs and ECs using the LPFSs. Second, we apply an extended AQM to determine the importance prioritisation of ECs. Additionally, a consensus reaching algorithm is adopted to dynamically determine the relative weights of experts considering their inconsistent judgements. An illustrative application of 5G smartphone development is conducted to demonstrate the feasibility and efciency of our proposed QFD approach. Through the empirical case with a comparative analysis, the proposed approach is proved to be useful and practical for capturing experts' opinions and prioritising ECs in QFD. [Submitted: 19 March 2020; Accepted: 11 April 2021]

A new method for quality function deployment using double hierarchy hesitant fuzzy linguistic term sets and axiomatic design approach

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.

New Approach for Quality Function Deployment Using Linguistic Z-Numbers and EDAS Method

Quality function deployment (QFD) is an effective product development and management tool, which has been broadly applied in various industries to develop and improve products or services. Nonetheless, when used in real situations, the traditional QFD method shows some important weaknesses, especially in describing experts’ opinions, weighting customer requirements, and ranking engineering characteristics. In this study, a new QFD approach integrating linguistic Z-numbers and evaluation based on distance from average solution (EDAS) method is proposed to determine the prioritization of engineering characteristics. Specially, linguistic Z-numbers are adopted to deal with the vague evaluation information provided by experts on the relationships among customer requirements and engineering characteristics. Then, the EDAS method is extended to estimate the final priority ratings of engineering characteristics. Additionally, stepwise weight assessment ratio analysis (SWARA) method is employed to derive the relative weights of customer requirements. Finally, a practical case of Panda shared car design is introduced and a comparison is conducted to verify the feasibility and effectiveness of the proposed QFD approach. The results show that the proposed linguistic Z-EDAS method can not only represent experts’ interrelation evaluation information flexibly, but also produce a more reasonable and reliable prioritization of engineering characteristics in QFD.

A new integrated approach for engineering characteristic prioritization in quality function deployment

As a customer-driven quality improvement tool, quality function deployment (QFD) can convert customer requirements (CRs) into appropriate engineering characteristics (ECs) in product design and development. However, the conventional QFD method has been criticized for a variety of drawbacks, which limit its efficiency and potential applications. In this study, a new QFD approach integrating picture fuzzy linguistic sets (PFLSs) and the evaluation based on distance from average solution (EDAS) method is proposed for the determination of ranking order of ECs. The PFLSs are utilized to express the judgements of experts on the relationships among CRs and ECs. Then, the EDAS method is extended under picture fuzzy linguistic environment for the prioritization of the ECs identified in QFD. Moreover, a combined weighing method based on technique for order of preference by similarity to ideal solution (TOPSIS) and maximum entropy theory is established to calculate the weights of experts objectively. Finally, a product-service system design is provided to illustrate the effectiveness of the proposed QFD approach. The result shows that the manufacturer should pay more attention to “Meantime before failure”, “Warning feature” and “Quality of product manual”. Feedback from domain experts indicates that the integrated approach being proposed in this paper is more suitable for assessing and prioritizing ECs in QFD.

Hesitant fuzzy integrated MCDM approach for quality function deployment: a case study in electric vehicle

Quality function deployment (QFD) is a product planning management instrument which has been used in a broad range of industries. However, the traditional QFD method has been criticised much for its deficiencies in acquiring experts’ opinions, weighting customer requirements (CRs) and ranking engineering characteristics (ECs). To overcome the limitations, an integrated analytical model is presented in this study for obtaining the importance ratings of ECs in QFD by integrating decision-making trial and evaluation laboratory (DEMATEL) technique and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method under hesitant fuzzy environment. In particular, the hesitant fuzzy DEMATEL is used to analyse the interrelationships among CRs and determine their weights, and the hesitant fuzzy VIKOR is utilised to prioritise ECs. Finally, the feasibility and practicality of the proposed method are verified by an example regarding the product development of electric vehicle.

Determination of Target Values of Engineering Characteristics in QFD Using Uncertain Programming

Quality function deployment (QFD) is a new product development tool remarked with interpreting customer requirements into engineering characteristics of the design process. On account of the inherent imprecise and uncertain elements in the weights of customer requirements, the relationships between customer requirements and engineering characteristics, and the correlations among engineering characteristics, uncertain variables are preferred to be applied in this paper. By taking advantage of expected value modelling to determine the target values of engineering characteristics in handling different practical design scenarios, two uncertain programming models are proposed for optimizing the QFD process in an uncertain environment. Subsequently, the proposed uncertain models are implemented in a motor car design for quality development.

Translating online customer opinions into engineering characteristics in QFD: A probabilistic language analysis approach

Online opinions provide informative customer requirements for product designers. However, the increasing volume of opinions make them hard to be digested entirely. It is expected to translate online opinions for designers automatically when they are launching a new product. In this research, an exploratory study is conducted, in which customer requirements in online reviews are manually translated into engineering characteristics (ECs) for Quality function deployment (QFD). From the exploratory study, a simple mapping from keywords to ECs is observed not able to be built. It is also found that it will be a time-consuming task to translate a large number of reviews. Accordingly, a probabilistic language analysis approach is proposed, which translates reviews into ECs automatically. In particular, the statistic concurrence information between keywords and nearby words is analyzed. Based on the unigram model and the bigram model, an integrated impact learning algorithm is advised to estimate the impacts of keywords and nearby words respectively. The estimated impacts are utilized to infer which ECs are implied in a given context. Using four brands of printer reviews from Amazon.com, comparative experiments are conducted. Finally, an illustrative example is shown to clarify how this approach can be applied by designers in QFD.

Determination of target values of engineering characteristics in QFD using a fuzzy chance-constrained modelling approach

Quality function deployment (QFD) is a method used for the manufacturing process of a product or service that is devoted to transforming customer requirements (CRs) into appropriate engineering characteristics (ECs) by specifying the importance of the ECs and then setting their target values. Confronting the inherent vagueness or impreciseness in the QFD process, we embed the fuzzy set theory into QFD. A fuzzy chance-constrained modelling approach with core philosophies of fuzzy expected value model and fuzzy chance-constrained programming is used in this paper. Thus, a novel fuzzy chance-constrained programming model whose objective is to minimize the fuzzy expected cost is proposed to determine the target values of the ECs with risk control to ensure satisfying CRs. Meanwhile, when considering the importance of the ECs, we adopt a more reasonable dispose which is to aggregate the relationships between the CRs and the ECs, and the correlations among the ECs. In order to solve the presented model, a hybrid intelligent algorithm is designed by integrating fuzzy simulation and genetic algorithm. Finally, an example of a motor car design is given to demonstrate the feasibility and effectiveness of the devised modelling approach and algorithm.

Cooperative fuzzy games approach to setting target levels of ECs in quality function deployment.

Quality function deployment (QFD) can provide a means of translating customer requirements (CRs) into engineering characteristics (ECs) for each stage of product development and production. The main objective of QFD-based product planning is to determine the target levels of ECs for a new product or service. QFD is a breakthrough tool which can effectively reduce the gap between CRs and a new product/service. Even though there are conflicts among some ECs, the objective of developing new product is to maximize the overall customer satisfaction. Therefore, there may be room for cooperation among ECs. A cooperative game framework combined with fuzzy set theory is developed to determine the target levels of the ECs in QFD. The key to develop the model is the formulation of the bargaining function. In the proposed methodology, the players are viewed as the membership functions of ECs to formulate the bargaining function. The solution for the proposed model is Pareto-optimal. An illustrated example is cited to demonstrate the application and performance of the proposed approach.

A novel fuzzy group decision-making approach to prioritising engineering characteristics in QFD under uncertainties

In new product development, design teams commonly need to define engineering characteristics (ECs) in a quality function deployment (QFD) planning process. Prioritising the engineering characteristics in QFD is essential to properly plan resource allocation. However, the inherent vagueness or impreciseness in QFD presents a special challenge to the effective calculation of the importance of ECs. Generally, there are two types of uncertain input in the QFD process: human perception and customer heterogeneity. Many contributions have been made on methods to prioritise ECs. However, most previous studies only address one of the two types of uncertainties that could affect the robustness of prioritising ECs. To address the two types of uncertainties simultaneously, a novel fuzzy group decision-making method that integrates a fuzzy weighted average method with a consensus ordinal ranking technique is proposed. An example is presented to illustrate the effectiveness of the proposed approach. Results of the implementation indicate that the robustness of prioritising ECs based on the proposed approach is better than that based on the method of Chen et al. (Chen, Y., Fung, R.Y.K., Tang, J.F., 2006. Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. European Journal of Operational Research, 174 (3), 1553–1556).

A methodology of determining aggregated importance of engineering characteristics in QFD

Quality function deployment (QFD) is a planning and problem-solving tool that is gaining acceptance for translating customer requirements (CRs) into engineering characteristics (ECs) of a product. Deriving the importance of ECs is a crucial step of applying QFD. However, the inherent fuzziness in QFD presents a special challenge to effectively evaluate the importance of ECs and correlation among them. Furthermore, degree of impact of an engineering characteristic (EC) on the other ECs also reflects the importance of the ECs. In previous studies, those impacts were neglected or simply represented using a linear combination in determining the importance of ECs. To address this issue, in this paper, a new methodology of determining aggregated importance of ECs is presented which involves the consideration of conventional meaning of importance of ECs as well as the impacts of an EC on other ECs. In the proposed methodology, fuzzy relation measures between CRs and ECs as well as fuzzy correlation measures among ECs are determined based on fuzzy expert systems approach. These two types of measures are then used to determine the aggregated importance of ECs. An example of design of a digital camera is used to illustrate the proposed methodology.

Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD

Quality function deployment (QFD) is a planning and problem-solving tool that is renowned for translating customer requirements into the technical attributes of a product. To deal with the imprecise elements in the development process, fuzzy set theory is incorporated into QFD methodology. A novel fuzzy expected value operator approach is proposed in this paper to model the QFD process in a fuzzy environment, and two fuzzy expected value models are established to determine the target values of engineering characteristics in handling different practical design scenarios. Analogous to stochastic programming, the underlying philosophy in the proposed approach is based on selecting the decision with maximum expected returns. Furthermore, the proposed approach considers not only the inherent fuzziness in the relationships between customer requirements and engineering characteristics, but also the correlation among engineering characteristics. These two kinds of fuzzy relationships are aggregated to give the fuzzy importance of individual engineering characteristics. Finally, an example of a quality improvement problem of a motor car design is given to demonstrate the application and performance of the proposed modelling approach.