Quality Function Deployment Team Research Articles

A Systematic Decision-Making Approach for Quality Function Deployment Based on Hesitant Fuzzy Linguistic Term Sets

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.

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.

Large-scale group decision-making for prioritizing engineering characteristics in quality function deployment under comparative linguistic environment

Quality function deployment (QFD) is a well-known tool for quality management. With its systematic operation mechanism for translating customer requirements (CRs) into corresponding engineering characteristics (ECs), QFD highly facilitates product/service design and improvement. However, the evolution of the decision-making environment has made the quantification within the traditional QFD operation could hardly able to meet the demanded efficiency as well as the accuracy of the generated results. To cope with the problem, an enhanced large-scale group decision-making method that integrates proportional hesitant fuzzy linguistic term sets (PHFLTSs) and the cumulative prospect theory (CPT) is put forward by this study to determine the ranking priority of ECs in doing QFD. To facilitate QFD team members carry out an easier and more accurate evaluation, they would use comparative linguistic expressions in making judgments, and the comparative linguistic expressions are subsequently transformed into hesitant fuzzy linguistic term sets (HFLTSs). The obtained HFLTSs are transformed into PHFLTSs based on the statistical method under a group decision-making environment for preventing information loss, and, at the same time, improving calculation accuracy. Taking decision-makers’ heterogeneity and risk attitudes into consideration, the CPT method is adopted and extended under the PHFL environment to prioritize the ECs in QFD operation. To illustrate how the proposed large-scale group decision-making-based QFD method is to be applied, a case study about solar photovoltaic cell development is presented. Furthermore, a comparative analysis is conducted to exhibit the effectiveness of the proposed QFD method.

Coping with diversity ratings in prioritizing design requirements in quality function deployment: A consensus-based approach with minimum-maximum adjustments

Quality function deployment (QFD) is a powerful customer-driven quality management tool that improves product quality by translating customer requirements (CRs) into design requirements (DRs) of a product. It is essential in QFD to obtain the importance ratings of DRs from the importance ratings of CRs and the relationship between CRs and DRs. However, two basic challenges hinder the effective implementation of QFD: (i) vague assessments of the relationship between CRs and DRs due to the inherent uncertainty in human judgement; and (ii) diverse importance ratings of DRs owing to the heterogeneity of the QFD team members. To this end, this study provides a consensus-based QFD to derive the consensual importance ratings of DRs, where linguistic distributions are used by the QFD team to express their assessments of the relationship between CRs and DRs. In the consensus-based QFD, a minimum–maximum adjustment consensus model is presented to assist the QFD team in deriving a consensual assessment regarding the relationship between CRs and DRs. This model is designed by minimizing the maximum adjustment between the original and the adjusted linguistic distribution assessments. Moreover, some desirable properties of the minimum–maximum adjustment consensus model are investigated. Finally, a case study, a detailed sensitivity analysis, and a comparison analysis are presented to illustrate the validity of our proposal.

A New Method for Quality Function Deployment With Extended Prospect Theory Under Hesitant Linguistic Environment

Quality function deployment (QFD) is an efficient planning and problem-solving method to convert customer requirements (CRs) into the engineering characteristics (ECs) of a product or service. Nevertheless, in the traditional QFD, the assessment of relationships between CRs and ECs and the prioritization of ECs have been criticized for various reasons in previous studies. This paper aims to propose a novel QFD method integrating extended hesitant fuzzy linguistic term sets (EHFLTSs) and prospect theory to overcome the limitations of the traditional QFD. First, EHFLTSs are used for the elicitation of hesitant linguistic assessment information of QFD team members. Considering the interrelations between CRs, Choquet integral is applied to obtain the aggregated relationship evaluation results. An extended prospect theory is suggested to derive the ranking orders of ECs. Finally, the efficiency and effectiveness of the presented QFD method are illustrated by an example of an electric vehicle manufacturing company.

Innovative new product development: a case study

Innovative new product development has increased high attraction by firms. New product development is an integral part of R&D research. Therefore, new product development process should be systematically performed to increase the impact of the new product on financial data of firms. In this study, Quality Function Deployment (QFD) methodology is utilized for electric vehicle technology. The technology developed is a type of electric towing vehicle. A customer-driven new product development process is utilized thanks to the QFD methodology. At the beginning of the research, a QFD team is formed in the company. A survey study is performed to gather customer requirements. 87 customer requirements are identified based on the survey study. These requirements are classified into five clusters, namely performance, ergonomics, security, maintenance & after sales service, and functionality. Technical requirements to satisfy the customer requirements are determined by the engineers in the company. Relationships between the technical requirements and the customer requirements are found in this study. Subsequently, relationships between the technical requirements are examined to construct the roof of the house of quality for the QFD. Then, the relative importance of the technical requirements are calculated to reflect the customer requirements. Finally, a new product is designed through use of the QFD methodology. The new product developed is 100% electric vehicle and multi-functional product.

A Multi-Granularity 2-Tuple QFD Method and Application to Emergency Routes Evaluation

Quality function deployment (QFD) is an effective approach to satisfy the customer requirements (CRs). Furthermore, accurately prioritizing the engineering characteristics (ECs) as the core of QFD is considered as a group decision making (GDM) problem. In order to availably deal with various preferences and the vague information of different experts on a QFD team, multi-granularity 2-tuple linguistic representation is applied to elucidate the relationship and correlation between CRs and ECs without loss of information. In addition, the importance of CRs is determined using the best worst method (BWM), which is more applicable and has good consistency. Furthermore, we propose considering the relationship matrix and correlation matrix method to prioritize ECs. Finally, an example about evaluating emergency routes of metro station is proposed to illustrate the validity of the proposed methodology.

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.

Approach based on fuzzy goal programing and quality function deployment for new product planning

Quality function deployment (QFD) is a useful planning tool for facilitating new product planning (NPP) to maximize customer satisfaction. Although customer satisfaction is an important goal in NPP, other goals must also be taken into account. The evaluation of QFD involves vagueness and imprecision and thus fuzzy approaches have been applied. This study considers the satisfaction of each customer requirement (CR) as the response variable and the fulfillment levels of the corresponding design requirements (DRs) as the explanatory variables. Each CR's satisfaction expression is formulated using the mathematical programing method. Experimental design and fuzzy sets are employed to collect the input–output data set based on the evaluated relationships between CRs and DRs and the correlations among DRs in QFD processes based on the QFD team's ability, experience, and knowledge. Considering three objectives, namely the maximum customer satisfaction, minimum incremental cost and minimum technical difficulty of DRs in NPP, an additive fuzzy goal programing model is proposed to obtain the optimal satisfaction under the preemptive priority structure of all goals. Furthermore, considering that customer satisfaction complies with Herzberg's two-factor theory, this study incorporates the concept of motivation and hygiene factors into the model, to modify the fulfillment levels of DRs to enhance customer satisfaction. A numerical example is used to demonstrate the applicability of the proposed model.

The quality function deployment method under uncertain environment using evidential reasoning: a case study of compressor manufacturing

In recent years, the issue of customer satisfaction achieving on the basis of improving service quality has been widely investigated. To achieve this goal, one of well-structured method is quality function deployment (QFD). QFD is an approach defining customer requirements (CRs) and translating them into relevant design requirements (DRs). The successful implementation of QFD requires a significant number of subjective evaluations of both customers and QFD team members. The QFD team members evaluate relationships between DRs and CRs and interrelationships between DRs. The customers evaluate relative importance of each CRs. In the basic QFD, crisp values are used for determining relationships between DRs and CRs, but the mentioned method are not suitable to address the subject of uncertainty, since in most cases QFD team express their opinions with uncertainty and therefore resulting in inappropriate implementation of QFD. This paper aims to apply a QFD method based on evidential reasoning approach to handle uncertain evaluation information provided by QFD team in compressor manufacturing. This method is able to consider uncertainties such as interval, imprecise and incomplete data in utilizing belief structure and then aggregating them to prioritize engineering DRs according to CRs.

Fuzzy Optimization Modeling Approach for QFD-Based New Product Design

Quality function deployment (QFD) is a planning and problem-solving tool for translating customer requirements (CRs) into the engineering characteristics (ECs) of a product. Owing to the typical vagueness of functional relationships in a new product, product planning is becoming more difficult under uncertainties. To tackle the vagueness or imprecision in QFD, numerous scholars have applied the fuzzy set theory to QFD and proposed various fuzzy QFD models. In this study, a fuzzy linear programming model is developed to determine the optimal level of ECs, where the objective function is the overall customer satisfaction and the cost constraint is fuzzified. Finally, we use a software product design as a numerical example, which demonstrates that the proposed methodology can help the QFD team realize the overall customer satisfaction of new products catching up with or exceeding the competitors in the target market.

Fuzzy Approaches for Constructing House of Quality in QFD and Its Applications: A Group Decision-Making Method

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.

A fuzzy-normalisation-based group decision-making approach for prioritising engineering design requirements in QFD under uncertainty

Quality function deployment (QFD) is often implemented to transform customer requirements (CRs) into the engineering design requirements (DRs) of a new product. Prioritising engineering DRs is essential to reasonably allocate resources for the new product development, and often must be done under uncertainty due to the vagueness and impreciseness of the judgments made by customers and QFD team members, and is a typical group decision behaviour that requires the active participation of multiple customers and multiple QFD design team members. Fuzzy set theory provides a theoretical basis for solving such group decision-making problems under uncertainty. Existing approaches for prioritising DRs under uncertainty usually simplify the group decision-making problem as non-group decision making for solution by simply aggregating individuals’ opinions using their arithmetic averages. This paper proposes a different fuzzy group decision-making procedure for prioritising DRs under uncertainty. The proposed approach does not aggregate the individual judgments of customers and QFD design team members, but rather aggregates the technical importance ratings of DRs, and can produce more rational and robust priority rankings for DRs through fuzzy normalisation. A real design case of a flexible manufacturing system (FMS) is examined to show the effectiveness and the potential applications of the proposed approach. Results are compared with those obtained by two known approaches.

Rough set-based approach for modeling relationship measures in product planning

Quality function deployment (QFD) provides a planning and problem-solving methodology that is widely renowned for translating customer requirements (CRs) into engineering characteristics (ECs) for new product development. As the first phase of QFD, product planning house of quality (PPHOQ) plays a very important role in this process. The degrees and directions of the relationship measures between CRs and ECs have serious effects on the special planning of ECs, modeling the relationship measures is an important step in constructing PPHOQ. The current paper presents a rough set (RS)-based approach for modeling relationship measures by determining the knowledge and experience of the QFD team, aided by the introduction of the type factor of a relationship used to express the effects of the relationship types. A study of general cases is used to demonstrate the performances and limitations of the proposed RS-based approach. The results show that the novel approach effectively determines the relative knowledge of the QFD team and facilitates decision-making in new product development.

A rough set approach for estimating correlation measures in quality function deployment

Quality function deployment (QFD) is a planning and problem-solving methodology used to translate customer requirements (CRs) into engineering characteristics (ECs) in the course of new product development (NPD). Estimating the correlation measures among ECs is a crucial step in the product planning house of quality (PPHOQ) construction process because these measures seriously affect the planning of development efforts. This study presents a rough set-based approach used to estimate the correlation measures by revealing the knowledge of a QFD team. The approach involves introducing the category factor of a correlation to express the influences of the correlation categories on the corresponding correlation measures. A case study of a two-cylinder washing machine is used to illustrate the proposed approach. The result shows that the novel approach is effective in revealing the related knowledge of the QFD team and facilitating NPD decision making.

PROMETHEE Group Decision Support System and the House of Quality

Quality function deployment (QFD) is a multi-step method that monitors customer needs throughout a product development process. The House of Quality (HOQ) exercise undertaken in the first phase of QFD is considered as the most important, since customer needs must be accurately translated into a set of technical requirements for the final product. This paper provides a PROMETHEE group decision support system (GDSS) approach that integrates the design preferences of the QFD team. We highlight the selection and ranking of the technical requirements in the HOQ exercise, where a group of multidisciplinary decision makers (DMs) in a globally dispersed QFD team is required to input their individual preferences. Our approach advances the HOQ group decision making context in three important areas. First, it treats each criterion and DM as unique in terms of the preference function and threshold levels. Second, it seeks a multi-criteria approach for the HOQ process, where some DMs may play a more important role than others on a certain criterion. Third, sensitivity analysis through the Geometrical Analysis for Interactive Assistance (GAIA) plane provides valuable information about the conflicts, similarities, or independencies between the criterion and the DMs, respectively. A case on an automotive part illustrates the performance of the PROMOTHEE approach with GAIA.

Customer-driven product planning using conjoint analysis and QFD ANP methodology

Quality function deployment (QFD) is a customer-driven strategic product-planning tool which translates the customer needs (CNs) into design requirements (DRs). During the implementation of QFD approach, certain shortcomings are encountered. One of the major difficulties in using QFD is the complexity in the construction of house of quality (HOQ) when the customer needs portion is of unreasonable size. Factor analysis is used in the proposed methodology to resolve this problem. Another drawback is the scope for existence of conceptual gap between CNs and DRs, as the priority structure of CNs is developed through assigning different importance weights for CNs based on QFD team members' direct experience with the customers. In this paper, conjoint analysis is adopted to bridge the gap. Sometimes the CNs and DRs are not independent and linear. To address interdependencies between CNs and DRs as well as the intra-dependence among them, the methodology of analytic network process is employed in the present work to build HOQ for product planning. A case study on domestic refrigerator is presented to illustrate the proposed methodology.

Integrating preference analysis and balanced scorecard to product planning house of quality

Product planning house of quality (PPHOQ) is of fundamental and strategic importance in quality function deployment (QFD). Determining the aggregated priority ratings (PRs) of engineering characteristics (ECs) is a crucial step of constructing PPHOQ. A QFD team often involves a group of cross-functional team member from marketing, design, quality, plan, finance, logistics, and after service. Each member of the QFD team from different backgrounds often demonstrates significantly different behavior from the others and generates different preference assessment to the competitive products. The gross benefit of implementing the improvement goal of the performance value (IGPV) of each EC should be estimated from the short- and long-term perspectives in a comprehensive and accurate way. In this paper, we propose an extended PPHOQ by using a least deviation based approach (LDBA), and balanced scorecard (BSC), and develop a comprehensive and systematic approach to determine the aggregated PRs of ECs in the extended PPHOQ. The proposed approach is based on the presented LDBA for dealing with competition and preference information to determine the technical point of each EC, and the combination of BSC and analytic hierarchy process for estimating the gross benefit of implementing the IGPV of each EC. Finally, a case study is provided to illustrate the effectiveness of the proposed approach.

An evidential reasoning based approach for quality function deployment under uncertainty

Quality function deployment (QFD) is a methodology for translating customer wants (WHATs) into relevant engineering design requirements (HOWs) and often involves a group of cross-functional team members from marketing, design, quality, finance and production and a group of customers. The QFD team is responsible for assessing the relationships between WHATs and HOWs and the interrelationships between HOWs, and the customers are chosen for assessing the relative importance of each customer want. Each member and customer from different backgrounds often demonstrates significantly different behavior from the others and generates different assessment results, complete and incomplete, precise and imprecise, known and unknown, leading to the QFD with great uncertainty. In this paper, we present an evidential reasoning (ER) based methodology for synthesizing various types of assessment information provided by a group of customers and multiple QFD team members. The proposed ER-based QFD methodology can be used to help the QFD team prioritize design requirements with both customer wants and customers’ preferences taken into account. It is verified and illustrated with a numerical example.

Dealing with subjectivity in early product design phase: A systematic approach to exploit Quality Function Deployment potentials

Quality Function Deployment (QFD), as a customer-driven tool, is generally used in the early phase of new or improved products/services design process, and therefore most of the input parameters are highly subjective in nature. The five major input components of the QFD, which are laid in the House of Quality (HOQ), namely, the customer requirement, the technical attribute, the relationship matrix, the correlation matrix, and the benchmarking information, play a central role in determining the success of QFD team. Accurate numerical judgment representations are of high importance for the QFD team to fill in the values of each of those components. In this paper, a generic network model, based on Analytic Network Process (ANP) framework, will be proposed to systematically take into account the interrelationship between and within those components simultaneously and finally derive their relative contribution. In particular, with respect to a rapidly changing market, the incorporation of the new product development risk, the competitors’ benchmarking information, and the feedback information into the network model may be considered as a novel contribution in QFD literature. Not only does this network model improve the QFD results’ accuracy, but it also serves as a generalized model of the use of ANP in QFD with respect to the previous research. A simple illustrative example of the proposed network model will be provided to give some practical insights.