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This paper aims to classify journal papers in the context of hybrid quality function deployment QFD and multi-criteria decision-making (MCDM) methods published during 2004–2021.

A conceptual classification scheme is presented to analyze the hybrid QFD-MCDM methods. Then some recommendations are given to introduce directions for future research.

The results show that among all related areas, the manufacturing application has the most frequency of published papers regarding hybrid QFD-MCDM methods. Moreover, using uncertainty to establish a hybrid QFD-MCDM the relevant papers have been considered during the time interval 2004–2021.

There are various shortcomings in conventional QFD which limit its efficiency and potential applications. Since 2004, when MCDM methods were frequently adopted in the quality management context, increasing attention has been drawn from both practical and academic perspectives. Recently, the integration of MCDM techniques into the QFD model has played an important role in designing new products and services, supplier selection, green manufacturing systems and sustainability topics. Hence, this survey reviewed hybrid QFD-MCDM methods during 2004–2021.

Supplier evaluation and selection is an essential (multi-criteria decision-making) MCDM process that considers qualitative and quantitative factors. This research work attempts to use a MCDM technique based on merging fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) and Quality Function Deployment (QFD) ideas. The study attempts to find the supplier's attributes (HOWs) to accomplish its goals after determining the product's characteristics to suit the company's needs (WHATs).

The proposed research methodology comprises the following four steps: Step 1: Determine the product purchase requirements (“WHATs”) and those pertinent to supplier evaluation (“HOWs”). In Step 2, the relative importance of the “WHAT-HOW” correlation scores is determined and also the resulting weights of “HOWs”. In Step 3, linguistic evaluations of possible suppliers in comparison to subjective criteria are given to the decision-makers. Step 4 combines the QFD and F-TOPSIS techniques to select suppliers.

A fuzzy MCDM method based on fusing and integrating fuzzy information and QFD is presented to solve the drawbacks of conventional decision-making strategies used in supplier selection. Using the F-TOPSIS method, fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS), the relative closeness coefficient values for all alternatives are computed. The suppliers are ranked by relating the closeness of coefficient values. This method permits the combination of ambiguous and subjective data expressed as fuzzy-defined integers or linguistic variables.

QFD and TOPSIS, two widely used approaches, are combined in this article to rank and evaluate suppliers based on the traits that the suppliers choose to prioritize. This study demonstrates that the method employed could address multiple-criteria decision-making scenarios in a computationally efficient manner. The effectiveness and applicability of the method are illustrated using an example.

The paper aims to review the fundamental concept of quality function deployment (QFD) and discusses the fact that a road to success for a new product development is the identification of customers' requirements and their conversion into engineering design requirements. Thereafter, it seeks to review the subject and to study four new cases on the topic of QFD.

The paper discusses key elements of QFD and the fact that the vision for the development of a comprehensive quality system can be built upon the principles of QFD taking customer requirements into consideration and relating that to the design requirements.

To make product development task successful and bringing competitive advantages to the core business, management must be committed to the needs of the customers through marketing surveys and implementing that into the process of product development by converting them into engineering design requirements.

The method has been used successfully in practice in areas such as: facility locations, marketing strategies, robot selection, ERP selection, software development, and sports.

The paper reviews QFDs, and its extensions such as fuzzy QFD, analytic hierarchy process, analytic network process and QFD, statistically extended QFD, dynamic QFD, and other extensions of that. In addition to that, cases covering topics of: ship of quality, cost‐design parameter modeling, enhanced version of the quality function development, financial factors and uncertainties in the product design process with fuzzy formulation, and a model for prioritizing and designing rule changes for the game of soccer, are also reviewed.

The author aims to review the fundamental concept of quality function deployment and to discuss the facts that the road to success for new product development is the identification of customers' requirements and their conversion into engineering design requirements. Thereafter, the author seeks to present an in‐depth review of the subject and to study five new cases on the topic of quality function deployment.

The paper discusses the key elements of quality function deployment and the fact that the vision for the development of a comprehensive quality system can be built on the principles of quality function deployment taking customer requirements into consideration and relating them to design requirements.

To make the product development task successful and bring competitive advantages to the core business, management must be committed to the needs of customers through marketing surveys and implementing these in the process of product development by converting them into engineering design requirements.

This article reviews quality function deployment and its extensions such as fuzzy QFD, AHP and QFD, statistically extended QFD, dynamic QFD, and other extensions. In addition, cases covering the topics of a ship of quality, cost‐design parameter modeling, an enhanced version of quality function development, financial factors and uncertainties in the product design process with fuzzy formulation, and a model for prioritizing and designing rule changes for the game of soccer, are also reviewed.

Quality function deployment (QFD) is a management tool that provides a visual connective process to help teams focus on the needs of the customers throughout the total development cycle of a product or process. It provides the means for translating customer needs into appropriate technical requirements for each stage of a product/process‐development life‐cycle. It helps to develop more customer‐oriented, higher‐quality products. While the structure provided by QFD can be significantly beneficial, it is not a simple tool to use. This article outlines how techniques such as fuzzy logic, artificial neural networks, and the Taguchi method can be combined with QFD to resolve some of its drawbacks, and proposes a synergy between QFD and the three methods and techniques reviewed.

The purpose of this paper is to apply a framework for value stream mapping (VSM) integrated with fuzzy quality function deployment (QFD) for enabling scientific prioritization of improvement proposals to improve leanness.

The literature was reviewed from the perspectives of VSM, QFD and fuzzy logic applications. The current state map was developed for the case component; fuzzy QFD was used for prioritizing improvement proposals and prioritized proposals were incorporated in the future state map.

The approach enabled the scientific mapping of wastes with improvement proposals and thereby enabling systematic implementation of improvement proposals. The conducted pilot study resulted in 4 percent reduction in cycle time. As the lean implementation is a continuous process, furthermore improvements are expected in near future.

The study was conducted in an Indian camshaft manufacturing organization. The improvements in terms of leanness parameters were quantified.

The findings determined from the study has practical relevance. Besides, managerial implications were also discussed.

The study presented in this paper was conducted in a real time manufacturing environment. Hence the contributions of study are found to be valuable among academic and practicing communities.

The purpose of this paper is to report on research in which a fuzzy quality function deployment (QFD) approach has been used for enhancing agility improvement of a traditional manufacturing organization.

QFD approach incorporated with fuzzy logic was used for scientific prioritization of agile decision domains, agile attributes and agile enablers. Data were gathered from a traditional manufacturing organization and the prioritization was carried out.

The implementation experience indicated that fuzzy QFD approach could be used for enabling agility improvement.

The implementation study was carried out by substituting the data gathered from only one manufacturing organization. Yet the findings and contribution of this research work would be useful to the captains of the majority of the manufacturing companies situated in the world.

The approach used in this paper will indicate the vital agile decision domains, agile attributes and agile enablers. This process will enable the organizations to compete in the global market.

A case study has been reported to indicate the feasibility of deploying fuzzy QFD approach in the industrial scenario for enabling agility improvement. Hence, the findings are original.

The purpose of this paper is to propose a novel lean management tool to provide a comprehensive and flexible evaluation model while converting customer voices into technical characteristics in lean implementations.

For this purpose, the proposed model was constructed by belief space-evaluations, quality function deployment (QFD) and analytic hierarchy process (AHP) in interval type-2 fuzzy (IT2F) environment. This model involves three phases: determining the linguistic weights and belief-based relations with their IT2F-sets, processing information about IT2F-based belief-evaluations and ranking the technical characteristics using the defuzzified belief-based relative importance values.

The proposed model was applied to automotive after-sales service in Turkey to demonstrate its use in lean service-decisions. This model was compared with its classical and type-1 fuzzy versions. The ranking-results of the proposed model differed from those of the other versions. The reason is that the IT2F-environment offers a sensitive and flexible evaluation of the model’s linguistic scales.

Calculations in the proposed model may be quite involved for practitioners. An Excel-dashboard was created to simplify the computational complexity.

Researchers/practitioners can apply this model to any lean manufacturing/service implementation.

Company managers/employees/customers can recognize their perception-mechanisms via belief space-evaluations and experience how uncertainty in the perception-mechanism affects their decisions.

The proposed model provides a new lean tool due to the Bayesian model combined with QFD-AHP in IT2F-environment. This model eliminates the ambiguity in conceptual change-based lean decisions.

The purpose of the paper is to develop a hybrid method to prioritize risk factors (RFs) of sustainable supply chain (SSC) considering sustainable customer requirements (CRs) and uncertain evaluation.

In the proposed method, fuzzy Kano model (FKM) is applied to prioritize sustainable CRs considering customer satisfaction (CS) and objective weight of each CR, the interval-valued intuitionistic fuzzy (IVIF) set theory is integrated with quality function deployment (QFD) to translate the sustainable CRs into RFs of SSC under uncertain environment and the IVIF cross-entropy is used to conduct objective analysis to prioritize RFs. Finally, a case in air-conditioner-manufacturing company is presented to demonstrate the proposed method.

A case study of SSC risk management, the comparative analysis and associated discussions are conducted to illustrate the feasibility and effectiveness of the proposed method. The results obtained from the case study shows that RF5 (market share reduction) is the most important RF in the SSC. Compared with the existing methods, the proposed method can integrate sustainable CRs into SSC's RFs, handle uncertain information effectively and obtain objective importance of RFs.

Theoretically, the paper develops a customer-oriented model based on the FKM, QFD, IVIF sets and entropy theory to prioritize RFs of SSC under uncertain environment. The model enables to integrate sustainable CRs into RFs managements and is efficient to deal with the subjectivity and conduct objective analysis to prioritize RFs. In practice, the systematic and correct RFs' priorities analysis provides reliable decision support for the managers to take measures to avoid or mitigate the critical RFs.

Smart senior care industry in China currently faces a series of practical difficulties such as an imbalance in the demand and supply structure, service products unable to cater to the actual needs of the elderly and a low degree of marketization. This study therefore proposes using grey relational analysis and the Fuzzy-quality function development (QFD) quality improvement method to help solve these problems.

The proposed method converts the fuzzy requirements of the elderly into the technical characteristics of technologically augmented senior care service products. It then, uses the QFD relationship matrix, combined with grey relational analysis, to analyze the relationship between the needs of elderly and the converted technical characteristics, and subsequently identifies key technical characteristics.

Results show that an improvement in the smart senior care service platform according to the differences of the elderly's preferences can significantly improve users' satisfaction with the service in addition to enhancing market competitiveness of the technologically assisted senior care service products.

A novel method to improve the need of smart senior care is proposed by considering age difference. The proposed grey relational analysis and Fuzzy-QFD quality improvement method can help improve the service quality of the smart senior care service platform.