Fuzzy Quality Function Deployment Methodology Research Articles

Interval-valued spherical fuzzy quality function deployment methodology: Metaverse collaborative system design application

The metaverse collaborative system (MCS) aims to assist users in working together, sharing resources, and participating in joint activities in the metaverse environment. As an interactive system, identifying essential design requirements (DRs) based on user requirements (URs) during the conceptual design phase of MCS is challenging as it directly affects the cost of subsequent optimization design and user experimentation, as well as user experience. Therefore, we propose a quality function deployment (QFD) method based on multi-criteria decision-making (MCDM) in an interval-valued spherical fuzzy (IVSF) environment to establish a mapping relationship between URs and DRs and identify human-centered DRs to guide the conceptual design process of MCS. Firstly, we summarized the URs for MCS design and calculated their weights using IVSF based analytical hierarchy process (IVSF-AHP). Afterward, we determined DRs based on URs and established the UR-DR relationship in the house of quality of QFD. Next, we integrate the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method in the IVSF environment into QFD to determine the priority of DRs. The results indicate that system functional design, scene layout design, compatibility design, and multi-user collaborative optimization design are important DRs for designing MCS and should be carefully considered. We validated the applicability of this method through an urban planning MCS design example and provided a conceptual design case including functional blueprints. Sensitivity analysis and comparative analysis demonstrate the flexibility and reliability of the proposed method.

QFD-Based Product Design for Multisegment Markets: A Fuzzy Chance-Constrained Programming Approach

Modern companies are moving rapidly toward more customer-oriented approaches for new product design and development. In real-world applications, customer requirements are more diverse and rather heterogeneous in practice, which requires the targeted market to be multisegmented. In this article, employing the quality function deployment (QFD) and assuming that parameter uncertainty exists, we propose a new fuzzy QFD model for use in multisegment markets, to search for optimal engineering characteristics by maximizing the overall customer satisfaction score. Under that framework, and when the credibility measure is employed, we adopt a fuzzy chance-constrained programming setting to convert the fuzzy model into its crisp counterpart. Afterwards, an analytical method based on fuzzy operational law is proposed to further transform the chance-constrained programming into an equivalent explicit model that can be solved directly by a standard commercial software. For a targeted group of customers, a new yoghurt design is presented to illustrate the effectiveness and superiority of our treatment. We report that our methodology not only permits the selection of the preferred solution based on an acceptable level of risk, which is very important in practice, but also guarantees higher customer satisfaction scores. This fact extends the fuzzy QFD methodology to a wider range of applications.

Evaluation of service quality in public bus transportation using interval-valued intuitionistic fuzzy QFD methodology

This study aims to develop a quantitative assessment framework for public bus operators to translate the passenger demands into service quality specifications. The results of a customer satisfaction survey, carried out to identify the passenger demands, are used as an input to measure the level of service quality in a public transport operator. Firstly, the number of customer satisfaction criteria is reduced with Principal Component Analysis (PCA) and secondly the evaluation factors are identified with Quality Function Deployment (QFD). Both methods are integrated with an interval-valued intuitionistic fuzzy (IVIF) approach to reflect the uncertainty in the QFD evaluations of decision makers. By this way, our approach synthesizes customer and design requirements via interval-valued intuitionistic fuzzy QFD. The results show that the proposed method helps decision makers of public bus operators to focus on high priority areas so that they can effectively deploy their resources to address people's mobility needs.

Passenger Satisfaction Evaluation of Public Transportation in Istanbul by Using Fuzzy Quality Function Deployment Methodology

Quality Function Deployment (QFD) is known as a planning process and methodology that helps companies for listening voice of customers. This process assists company to produce new or improve current products and services according to voice of customers. QFD provides this by transforming voice of customers into the technical language of the company. The aim of this study is to increase service quality and passenger satisfaction by listening voice of passengers, determining their requirements, and integrating these requirements into public transportation services properly. On the other hand, the nature of uncertainty and subjectivity in service delivery processes makes difficult employing QFD effectively. Thus, fuzzy QFD methodology is applied to evaluate public transportation firms in Istanbul including BRT, IETT, Otobus Inc., and Private Busses. At the end of the study, conclusions and discussions are drawn concerning the requirements of passengers to reduce the complaints and improve public transportation services quality.

Extending Fuzzy QFD Methodology with GDM Approaches: An Application for IT Planning in Collaborative Product Development

Collaborative product development (CPD) is a technology intensive process and effective planning in information technologies (IT) can improve the performance of CPD activities. Considering that quality function deployment (QFD) can be utilized as a planning tool for products and systems, the use of the QFD can enable IT systems to align their structure for CPD. In the QFD approach, the house of quality matrix is used for converting customer requirements into engineering characteristics. The challenge in this process is the profile of the inputs that are collected from the participants who provide feedback on customer requirements. Individual judgments of these participants can be in various formats, or can indicate preferences that are incomplete. This study aims to tackle with this issue by making use of an extended QFD methodology in CPD for IT planning. For this purpose, two group decision making (GDM) approaches are used to address both challenges of multiple preference formats and incomplete information. The validity of the developed IT planning model for CPD is tested with a case study at a Turkish software company using the two GDM approaches, and the obtained results are discussed. As customer requirements are important at every phase of CPD, the proposed model can help implementers to improve their decision processes.

The prioritisation of service dimensions in logistics centres: a fuzzy quality function deployment methodology

This study takes a customer focus that prioritises the service-offering dimensions of logistics centres (LCs) by considering potential LC customer expectations. Applying a survey and a quality function deployment methodology to logistics service providers, the study explores, categorises and prioritises LC customer expectations and LC service characteristics. The results indicate that customer preferences mainly prioritise infrastructure, and warehouse and intermodal dimensions. However, when the cost dimension is included, higher utility values are delivered through soft service dimensions like value-added or standard services. LC investors or undertakers can use these results to guide their design of market offerings by using the same methodology to assess expectations in their target markets.

A fuzzy QFD methodology to improve logistics service

Article history: Received October 15, 2012 Accepted January 24, 2014 Available online March 6 2014 Customer service is increasingly being recognized as a source of competitive advantage. The keys to provide effective customer service are determining the customer needs, accurately, and meeting and exceeding the needs in a consistent manner. Companies should adapt a strategic, proactive focus on customer service based on understanding logistic processes and designing the logistics system to meet their needs. This paper proposes an approach based on the quality function deployment (QFD), for ranking strategic actions to improve logistics service. The paper addresses the issue of how to deploy the house of quality (HOQ) to effectively and efficiently improve logistics processes and thus customer satisfaction. For data collection, fuzzy logic is used to deal with the ill-defined nature of the qualitative linguistic judgments required in the proposed HOQ. The methodology has been tested by means of a real case application, which refers to an Iranian company operating in the manufacturing industry. © 2014 Growing Science Ltd. All rights reserved.

A fuzzy QFD approach to determine supply chain management strategies in the dairy industry

The aim of this study is to identify the crucial logistics requirements and supply chain management (SCM) strategies for the dairy industry. For product or service development, quality function deployment (QFD) is a useful approach to maximize customer satisfaction. The determination of design requirements and supply chain management strategies are important issues during QFD processes for product or service design. For this reason, a fuzzy QFD methodology is proposed in this study to determine these aspects and to improve customer satisfaction. Qualitative information is converted firstly into quantitative parameters, and then this data is combined with other quantitative data to parameterize two multi-objective mathematical programming models. In the first model, the most important logistic requirements for the company are determined based on total technical importance, total cost, total feasibility and total value increment objectives, and in the second model, based on these objectives, appropriate supply chain management strategies are determined. Finally, a case study from the Turkish dairy industry is given to illustrate the proposed approach.

Towards a QFD-based expert system: A novel extension to fuzzy QFD methodology using rough set theory

Quality function deployment (QFD) has been widely recognized as an effective means to develop quality products that can maximize customer satisfactions. This paper presents a novel extension to the fuzzy QFD methodology using rough set theory, with the aim to facilitate decision making in the early stages of product development and lead to the establishment of a QFD-based expert system for product design. The proposed rough-fuzzy QFD system combines fuzzy arithmetic operations with the two novel concepts of rough number and rough boundary interval that are derived from rough set theory. A comparison between the proposed methodology and the traditional fuzzy QFD was performed. It has been shown that the proposed methodology not only can provide more insights into the vague voices of customers and technologists, but also can suppress the enlargement of boundary intervals after each arithmetic operation in QFD analysis. This would help in improving the discernibility of design objectives and thus facilitate the decision making in product development.

Analyzing a Quality Function Deployment Matrix: An Expert System-based Approach to Identify Inconsistencies and Opportunities

Identification of a need or a deficiency triggers conceptual system design. The first step in conceptual design is to analyze and translate the need or deficiency into specific qualitative and quantitative customer and design requirements. Design methods such as quality function deployment (QFD), parameter taxonomies and input/output matrices provide a useful framework for this translation. Well defined and unambiguous requirements enhance communications and can potentially reduce the number of 'detours' during subsequent design and development phases. However, imprecision and vagueness characterize the conceptual design phase. To accommodate imprecision, the QFD method and the concept selection methodology, initially proposed by Pugh, have been modified and extended by applying concepts from fuzzy set theory. The extended approach provides a rigorous yet graceful mechanism for dealing with imprecise requirements, priorities and correlations as prerequisites to concept selection. This technical paper presents an expert system-based extension to the fuzzy QFD methodology. Emphasis is on the: (a) identification of strategic market and product opportunities, (b) identification of applied research focus areas and (c) isolation of inconsistencies between customer articulation of functional requirements and the definition of system requirements and parameter target values. An expert system-based parser has been embedded within the fuzzy QFD tool to facilitate strategic product planning, early design decision-making and parameter target setting.