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

Double hierarchy hesitant fuzzy linguistic term set and MULTIMOORA method: A case of study to evaluate the implementation status of haze controlling measures

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.

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.

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.

In order to develop usable products, human needs and requirements have to be taken into consideration. By employing the QFD (Quality Function Deployment) method the human needs are systematically matched with the product characteristics, which can help to improve the product quality. In this paper the use of QFD in the area of usable products is discussed and a case study is presented to improve the supply chain of mazandaran spinning industry. The QFD method was used in the case study to translate the needs of usable yarn in spinning the carpet in the moist climate into product characteristics. The QFD analysis of the usable carpet revealed that the characteristics of the yarn were receiving the highest overall quality, which means that improving the design of the best quality yarn Proportional of the moist climate for spinning the carpet would lead to higher customer satisfaction. The importance of developing products that meet the customer or human needs is a priority area in the product development process. With knowledge in Selection of raw materials according to weather conditions it is possible to reveal information of the human needs and requirements. However, in the product development process it also is important to match the climate needs with the product characteristics, which can be achieved by using the QFD method. The knowledge of Selection of raw materials according to weather conditions and the use of the QFD method are therefore useful tools in designing high quality products.

Security assessment and diagnosis for industrial water resources using TODIMSort considering Best–Worst Method with double hierarchy hesitant fuzzy linguistic term set

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

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The critical product planning phase, early in the product development cycle, requires a design tool to establish engineering priorities, select the preferred design concept, and set target levels of engineering performance while considering the needs of both the consumer and producer. The quality function deployment (QFD) method was developed as a design process tool to translate customer needs into engineering characteristics; however, limitations have been identified in using the QFD method for product planning. In this paper, a new design tool called product attribute function deployment (PAFD), based on the principles of decision-based design (DBD), is introduced as a decision-theoretic, enterprise-level process tool to guide the conceptual design phase. The PAFD method extends the qualitative matrix principles of QFD while utilizing the quantitative decision-making processes of DBD. The PAFD method is built upon established methods in engineering, marketing, and decision analysis to eliminate the need for the user ratings and rankings of performance, priority, and attribute coupling in the QFD method. The differences between the QFD and the PAFD processes are compared and contrasted, and the conceptual design of an automotive manifold absolute pressure sensor is used as a case study to demonstrate the features and benefits of the PAFD method. </para>

This paper discusses the development of E-saving glass (ESG) in Malaysia by using quality function deployment (QFD) method for market transformation. This paper focuses on two objectives, i.e., (i) to investigate the suitability of the ESG in Malaysian market for customer satisfaction, product quality and manufacturer competitiveness by using systematic tools of QFD process, and (ii) the implementation of ESG by looking at factors analysis based on QFD method and Malaysian market transformation. The QFD method is utilised by a multidisciplinary team to translate a set of customer requirements (the “voice of customer” (VOC)), market research and technical benchmarking data into an appropriate number of prioritised engineering targets that could be met by a new product design.

Quality function deployment (QFD) — A means for developing usable products

Quality function deployment (QFD) has been a popular product or service development method to convert customer needs (CNs) into technical requirements (TRs). Nevertheless, the traditional QFD has vagueness inherent in experts' opinions in determining CNs' weights and the relationship between CNs and TRs. In the previous research works, fuzzy set theory is popular to deal with imprecise information in QFD. However, it still suffers from several deficiencies. For example, it needs prior information that leads to relatively fixed intervals expressing vagueness; and it assumes the membership degrees are crisp. To solve the issues, this article develops an integrated QFD method, where rough sets and cloud model are utilized for treating uncertain information. The former expresses impreciseness without any other assumptions and the latter considers expert evaluations' randomness. In order to obtain more comprehensive importance of CNs, a combination weighting method is utilized. In the end, a compressor rotor's industrial service design is conducted utilizing the proposed method, where its effectiveness is verified.

As a generalization to hesitant fuzzy linguistic term set (HFLTS), double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is presented which circumvents the weakness of HFLTS in representing complex linguistic terms. DHHFLTS has two linguistic hierarchies with the second hierarchy supplementing the primary which enables decision-makers (DMs) to represent complex linguistic terms better. Motivated by the power of DHHFLTS, in this paper, a new decision framework is presented under DHHFLTS context. Initially, a new aggregation operator called double hierarchy hesitant fuzzy hybrid aggregation (DHHFHA) operator is proposed for sensible aggregation of DMs’ preference information. Further, weights of attributes are calculated by extending statistical variance (SV) method under DHHFLTS context. Objects are prioritized by extending the popular WASPAS (weighted aggregated sum product assessment) method to DHHFLTS context. The applicability and usefulness of the proposed framework are realized by demonstrating a risk management technique (RMT) selection problem for a construction project. Finally, the superiority and weakness of the proposed framework are discussed by comparison with other methods.

As the development and extension of hesitant fuzzy linguistic term sets (HFLTSs), free double hierarchy hesitant fuzzy linguistic term sets (FDHHFLTSs) can describe the evaluation information in more detail. In addition, in practical multiple attribute decision-making (MADM) problems, priority relations and interaction relations usually exist among attributes, and the prioritized interactive Choquet (PIC) operator based on generalized prioritized measure-guided aggregation (GPMGA) operator and Choquet integral (CI) operator is an effective tool for dealing with decision-making problems with priority relations and interaction relations. Under the above contexts, we improve the PIC operator and propose the FDHHFLPIC operator which can aggregate the evaluation information under free double hesitant fuzzy linguistic environment and take both priority and interaction relations into account. Furthermore, we propose an approach based on the FDHHFLPIC operators to address the real MADM problem, and the proposed method considering the different relations between attributes in decision-making process. Finally, two numerical examples are given to demonstrate the effectiveness and advantages of the proposed method which can consider priority relations and interaction relations under the FDHHFLTSs.

Radiation safety management in health care – The application of Quality Function Deployment

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.