Conventional Quality Function Deployment Research Articles

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 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.

Quality improvement initiatives based on customer and service provider perspectives in shopping malls

PurposeThe purpose of this study is to optimize and improve service delivery configurations by integrating both customers’ and service providers’ perspectives into service delivery design processes using quality function deployment (QFD) methodology at shopping malls.Design/methodology/approachQFD is used to determine and close the gap between the most important customer needs and expectations and the opinions of service providers using a unique platform.FindingsOn customer side, the highest relative weight was given to “prompt response to customer concerns”, “not being crowded and loud”, “providing services for disabled customers” and “security of mall” customer expectations. On engineering side, “employees’ attributes, the size of parking area, reliable service, the time to find a product, the size of mall, disabled friendly infrastructure and the number of elevators and escalators” were determined to be the most important technical requirements.Originality/valueIntegrating the voice of customer into the voice of engineering, this study is the first attempt to describe how QFD methodology could be used to holistically optimize service delivery configurations in the mall industry. Unlike conventional QFD that ignores the cost perspective, this study has implications for operations managers with regard to solving resource allocation problems.

New approach for quality function deployment based on proportional hesitant fuzzy linguistic term sets and prospect theory

As a powerful instrument to fulfil customer needs, quality function deployment (QFD) is capable of converting customer requirements (CRs) into appropriate engineering characteristics (ECs) in the product design and development. Nevertheless, the inherent defects of the conventional QFD, such as the description of experts’ opinions with crisp numbers and the non-robust ranking of ECs, confine its efficiency and potential applications. In this study, a novel QFD approach using proportional hesitant fuzzy linguistic term sets (PHFLTSs) and prospect theory is proposed to overcome the insufficiencies of the traditional QFD. Specifically, the relationships between CRs and ECs are represented by PHFLTSs and the weights of CRs are derived with the best-worst method (BWM). An extended prospect theory is employed for the prioritisation of the ECs that have been identified. Finally, two application examples are provided to examine the applicability and advantages of our proposed QFD approach.

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

Quality function deployment (QFD) is a widely used quality system tool for translating customer requirements (CRs) into the engineering design requirements (DRs) of products or services. The conventional QFD analysis, however, has been criticized as having some limitations such as in the assessment of relationships between CRs and DRs, the determination of CR weights and the prioritization of DRs. This paper aims to develop a new hybrid group decision-making model based on hesitant 2-tuple linguistic term sets and an extended QUALIFLEX (qualitative flexible multiple criteria method) approach for handling QFD problems with incomplete weight information. First, hesitant linguistic term sets are combined with interval 2-tuple linguistic variables to express various uncertainties in the assessment information of QFD team members. Borrowing the idea of grey relational analysis (GRA), a multiple objective optimization model is constructed to determine the relative weights of CRs. Then, an extended QUALIFLEX approach with an inclusion comparison method is suggested to determine the ranking of the DRs identified in QFD. Finally, an analysis of a market segment selection problem is conducted to demonstrate and validate the proposed QFD approach.

A novel fuzzy quality function deployment framework

To be competitive in an increasingly competitive global market with short product life cycles, firms today must perform innovative new product development (NPD) that can fulfil dynamic demands such as quality, performance and cost. Quality function deployment (QFD) has been a popular tool in real-practice applications. However, the conventional QFD has some limitations, such as how to generalize the opinions of multiple decision-makers, how to deal with a huge amount of subjective data, how to complete a large dimensional comparison and how to consider the ambiguity and uncertainty in human decision-making. This research, thus, aims to tackle the problems and develops a comprehensive model. The model integrates QFD with fuzzy set theory and decision-making methodologies, including Delphi method, decision-making trial and evaluation laboratory and analytic network process for implementing NPD project. A case study is carried out for a solar cell manufacturer. The proposed model can help designers systematically consider relevant NPD information and develop successful products.

A multidimensional QFD design for the service quality assessment of Kansai International Airport, Japan

This paper investigates multi-agent service quality problems in the area of airport management and proposes the use of the multilayer quality function deployment (QFD) model to compromise the requirements of both airline companies (carriers) and passengers to ensure generality and sustainability of quality implementation. In air transportation, customers and service providers engage each other as a result of the complex supplier–customer structure. An airport provides services simultaneously in a nested framework for both airline companies and passengers. On the other hand, airline companies serve the passengers from their own specific perspective. Therefore, airports deal with the satisfaction of airline companies and passengers, while airline companies distinctly deal with the satisfaction of passengers. The satisfaction of passengers in terms of airport services is derived form of the airline company’s distinct position on service quality. The conventional QFD method is limited to compromise the multilayer customer phenomenon and the multilayer QFD model is capable of analysing the requirements of all agents in order to find appropriate solutions.

Advanced models of quality function deployment: a literature review

In this paper, a literature review conducted to study the characteristics of advanced models of quality function deployment (QFD) that have appeared in the literature arena is reported. QFD technique emerged in Japan in the 1970s. QFD has been proving to be a powerful tool that can be used for translating the voice of customers into technical languages. Yet from the beginning of this century, researchers began to point out the need to refine, modify and improve the features of QFD technique. In order to fulfil this need, few researchers brought out several advanced models of QFD. While conducting the literature review reported in this paper, six types of such advanced models of QFD were identified in the literature arena and their characteristics were studied. The result of this study revealed that the procedural and computational complexities are least in the case of an advanced model called total quality function deployment (TQFD). TQFD technique replaces the complex computations involved in applying conventional QFD technique with simple ratings. Besides the formation of teams to translate the voice of customers into work instructions ensures the quick reactions to the customers’ desires in the actual field of implementation. In this background, at end of this paper, it is suggested to adopt TQFD for implementation in traditional organizations in which the prevalence of adequate education for adopting complex procedures is found to be least.

An analysis of supplier development issues in global context: an approach of fuzzy based modelling

This paper aims to model the supplier development issues in global perspectives under informational vagueness. A Fuzzy Quality Function Deployment (FQFD) model has been used to correlate the buyers’ needs with Suppliers’ attributes. This approach supports buyers’ linguistic vague input in correlation matrix. On the basis of fuzzy correlation, importances of different suppliers’ attributes are determined. It is found that the approach is suitable in converting buyers’ unclear or vague requirements into products/services offered by supplier. FQFD is more complex than conventional Quality Function Deployment. However, this approach helps to investigate the global issues with their importance in supplier development programme.

An integrated fuzzy QFD framework for new product development

An important source of competitive advantage, survival and renewal for firms is the successful new product development (NPD). Quality function deployment (QFD) aims to facilitate the NPD process from product conceptualization to production requirements; however, conventional QFD has its shortcomings. Even though modified QFD models have been proposed in literature, a comprehensive model is necessary. In this paper, factors in each phase of the QFD is prepared first through literature review and interview with domain experts. Fuzzy Delphi method is adopted to select the critical factors, and fuzzy interpretive structural modeling is applied to determine the relationships among the critical factors. The results are then used to construct houses of quality for QFD, which is incorporated by fuzzy analytic network process. A case study of a thin film transistor liquid crystal display firm is carried out to verify the practicality of the proposed framework.

Simulating E-business Innovation Process Improvement with Virtual Teams Across Europe and Asia

In this article, E-business new product development innovation processes were studied at four enterprises across Europe and Asia. E-entrepreneurship innovation was improved using a quality of idea priority model. The conventional quality function deployment phase 1 matrix was revised to increase the voice of customers and engineer quality of idea decision-making. The proposed model was simulated with geographically dispersed virtual teams (based on production data). Statistical analyses were applied to test the hypothesis that an improved innovation process could better discriminate between new product return on investment pass or fail probability.

A FUZZY MULTI-OBJECTIVE MODEL OF QFD PRODUCT PLANNING INTEGRATING KANO MODEL

Quality function deployment (QFD) is a well-known customer driven system and tool for planning and managing new product development. House of quality (HOQ) is used to translate customer requirements (CRs) into each stage of a product development in QFD. In the conventional QFD model, constant coefficients are employed to present the relationships between “What matrix” and “How matrix” and among “How matrixes". In order to break the hindrance of the representation of simple linear function in the structure of HOQ, an integrative approach considering Kano model into QFD planning are employed, and processes are performed in uncertain and vague environments. Therefore, a fuzzy multi-objective model (FMOQFD-Kano) is proposed to reconcile the tradeoff between customer satisfaction and cost. Not only customer satisfaction is taken into account, but also cost is considered in the proposed model. Moreover, a fuzzy solution approach is applied to solve the model for preferred solutions under different alpha-levels, which can give decision-maker more choices to cater for uncertain environment. An example is presented to illustrate the applicability of the proposed model.

A QFD-enabled product conceptualisation approach via design knowledge hierarchy and RCE neural network

In this paper, an approach that attempts to improve conventional quality function deployment (QFD) technique in terms of effective design knowledge handling in product concept development is proposed and described. For this purpose, a QFD-enabled product conceptualisation system was established. It consists of three cohesively interacting modules, namely, design knowledge elicitation module using laddering technique, design knowledge representation module using design knowledge hierarchy (DKH), and design knowledge organisation module using restricted Coulomb energy (RCE) neural network. A case study on wood golf club design was used to illustrate the performance of the proposed approach. From the case study, the prototype QFD-enabled product conceptualisation system has demonstrated its effectiveness in design knowledge acquisition, representation and organisation at an early stage of NPD. The details of the validation are discussed.

Fuzzy logic approach to prioritise engineering characteristics in quality function deployment (FL‐QFD)

Quality function deployment (QFD) is a proven tool for process and product development, which translates the voice of customer (VoC) into engineering characteristics (EC), and prioritises the ECs, based on the customer's requirements. Conventional QFD evaluates these targets for crisp weights of the customer attributes (CA), identified from the VoCs. The VoCs are not crisp and generally exhibit a well‐defined distribution. Crisp weights assigned to non‐crisp CAs can lead to wrong prioritisation of the EC. In the past, fuzzy numbers have been used to represent the imprecise nature of these judgements and to define more appropriately the relationship between EC and CA. This paper proposes fuzzy logic‐quality function deployment (FL‐QFD) – the use of fuzzy logic principles in QFD. It is an innovative method of determining optimum rating of ECs by simulating the QFD matrix for randomized CA rating in the fuzzied range. The rule‐based knowledge system defines the relationship between the ECs and the CAs. The flexible manufacturing system (FMS) design problem investigated by Khoo and Ho (Khoo, L.P. and Ho., N.C., “Framework of a fuzzy quality function deployment system”, International Journal of Production Research, Vol. 34, 1996, pp. 299‐311) is presented to show the application of the proposed model. The results are compared and examined to study the effect of the CA on prioritising the EC. The paper addresses the issue of defining non‐crisp customer attributes in the QFD, and offers practical help to an individual intending to further investigate the proposed model.

Quality function deployment in healthcare

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.

Quality Function Deployment (QFD) and the environment

This paper identifies the Quality Function Deployment (QFD) as a tool for 'eco–design' that can be used to identify customer and community needs and requirements and to evaluate and characterise them into different categories, thereby building up an overall picture of customer value. It further argues that QFD can be more effective to the extent that it incorporates green engineering objectives, because it can incorporate sustainable environmental advantages into the existing set of sustainable competitive advantages that have been determined through the conventional QFD implementation process. The paper argues that, in this way, environmental competencies can also emerge as long–term sources of value or uniqueness, and operate as barriers to imitation in guaranteeing sustainable competitive advantage.

A Fuzzy Quality Function Deployment (FQFD) model for deriving optimum targets

Quality Function Deployment (QFD) is a powerful tool that translates the Voice of the Customer (VoC) into the Engineering Characteristics (ECs), which are those that can be modified in order to meet the desires of the customer. A main objective of QFD is the determination of target values of ECs; however, the conventional QFD aims only empirically at finding these targets, which makes it difficult for the ECs to be optimum. This paper proposes a novel method for determining optimum targets in QFD. Fuzzy numbers are used to represent the imprecise nature of the judgements, and to define more appropriately the relationships between ECs and Customer Attributes (CAs). Constraints such as cost, technical difficulty and market position are considered. An example of a car door is presented to show the application of the method.

Cost engineering with quality function deployment

Conventional Quality Function Deployment (QFD) is technically one-sided. Prioritization of technical attributes, if carried out at all, attempts to maximize customer satisfaction without considering the costs incurred. However, product design is usually a techno-economic process, hence there is always a tradeoff between quality goals and limited budgets. Based on a prioritization method suggested by Wasserman [1], this paper integrates design costs into the QFD framework. This proposed approach enables designers to optimize product development resources towards customer satisfaction and conduct analytical investigations to facilitate decision making in product design and development.

Quantitative QFD for System Engineering

AbstractQuantitative QFD (Q2FD) is an extension of conventional Quality Function Deployment (QFD). Q2FD incorporates quantitative requirements decomposition models into the QFD framework. It also rearranges conventional QFD matrices to facilitate quantitative capture and both forward and backward chaining of performance values. The rearranged matrices also allows full representation of QFD matrices with standard spreadsheets instead of only with special purpose software. Q2FD can serve as a bridge between quality, system, and disciplinary engineering personnel and act as an organizing formalism for all performance aspects of system design.