质量机能展开(QFD)研究综述

文章以对QFD的EI检索结果为基础，从QFD的发展、内容、应用及方法论研究四个方面对QFD的研究进行综述，以便于理论和实践工作者进一步开展工作。     

供应链环境下的质量机能展开应用研究

基于供应链环境下QFD的理论研究，从供应链上厂商集合体与终端顾客间的关系为切入点，对产品／服务组合的QFD进行了定性分析，并以手机产品的供应链为例，应用QFD进行了质量目标的设计，最后得出了在供应链竞争的环境下以产品／服务组合的QFD比单一节点的QFD更能适应市场竞争需要的结论。     

QFD理论下高校教学质量设计与评估

运用QFD理论于高等职业教育教学质量设计反映以"顾客"需求出发的设计思想,通过量化学生能力需求及与课程的关系,提出了QFD理论下高等职业教育教学质量屋的设计方法,并以广东纺织职业技术学院2010纺织品设计专业为例,对课程体系的设计进行了量化评估。     

“质量功能展开”（QFD）研究的最新动向（上）——第三代QFD的七个应用领域

质量功能展开（QFD）是制造业成功开发新产品和持续提高产品质量的一种有效手段。QF嗽的研究始于1966年，从初期理论的构建到“质量表”形式的普及进而到后来向“开发管理工程学”理论的拓展，现在的QFD已是第三代。近年来，QFD的发展尤为显著，在当前质量管理领域内倍受注目的管理和改进方法已同QFD紧密结合，内容越来越丰富，形式越来越多样，需要及时加以总结和推广。为此，日本《质量管理》杂志最近组织编写了一个名为“质量功能展开的最前沿”专辑，日本玉川大学国际经营学专业永井一志准教授以“第三代QFD的最新动向”为题发表了长篇专题论文，系统地论述了QFD的7个应用领域（即e7-QFD：（1）质量保证、（2）业务改进、（3）问题解决和课题实现、（4）与统计法结合、（5）新产品开发、（6）作为实时数据库、（7）作为可持续质量管理体系）。第三代QYD的研究还在进行之中，好多方面尚未定型，本文摘录编译了论文中的主要部分，仅供参考。     

模糊聚类方法在QFD中的应用——以汽车安全带为例

随着QFD应用的不断深入与发展,将模糊理论应用于QFD系统已成为QFD的重要研究方向之一。在获取顾客需求信息的基础上,重点研究模糊聚类方法在获得的顾客需求信息分类中的应用,使得所分的类别更加清晰地对应技术特性。通过把模糊聚类方法应用于汽车安全带设计开发顾客需求信息分类处理中,获得更加合理、科学的顾客需求分类,最大化地满足顾客对产品特性的需求。     

质量管理中的QFD法

QFD是Quality（质量）、Function（功能）与Development（发展）的缩写,中文译为质量功能展开。质量功能展开由赤尾洋二和水野滋两位日本教授于上个世纪六十年代作为一项质量管理系统提出,目的是为了设计、生产充分满足顾客需求的产品和服务。丰田公司于70年代后期使用QFD取得了巨大的经济效益。福特公司于1985年在美国率先采用QFD方法。今天,在美国,许多公司都采用了QFD方法,QFD技术受到世界各国重视。     

基于质量功能展开的连锁家饰店服务质量研究

质量功能展开(Quality Function Deployment,QFD)是一种透过质量屋的运作,系统地将顾客需求转换成工程要求的技术的方法。文中应用QFD导入PZB服务质量模式,整合模糊理论、Kano二维质量模式及顾客满意效益系数,分析连锁家饰店顾客服务质量需求的权重与排序,再以灰关联分析理论确定连锁家饰店各项工程要求质量的灰关联度值与排序,有助于经营者明确提高服务质量的重点。     

基于QFD的水龙头概念设计方法研究

QFD已成为当今世界普遍公认的现代科学管理方法和技术手段之一.它强调将客户的要求准确无误地体现到产品的设计、制造、寿命、循环当中.在产品开发和设计阶段就讲究质量,保证质量,控制质量,预防因质量问题产生浪费或成本过高,实施对产品适用性的全方位保证.本文依托于QFD的相关理论,主要研究了其在水龙头概念设计中的应用.     

“质量功能展开”（QFD）研究的最新动向（下）——第三代QFD的七个应用领域

（3）解决问题和实现课题用QFD QFD利用二元表可起到发现信息遗漏的作用以及筛选应解决问题的作用。然而，QFD通过编制二元表只是能发现某些问题，而并不能直接显示出对所发现问题的解决方法。也就是说，QFD只是用于发现问题的一种手段而已。QFD必须与统计手段、可靠性手段以及“创意法”等同时并用才能取得更多的效果。     

QFD在大规模定制下快速设计中的应用研究

文章基于质量功能配置(QFD)方法适用性的分析,提出了对QFD方法进行改进以适应大规模定制下快速设计产品开发模式,并对QFD方法的主要工具质量屋,从内容和形式上进行了重构,最后进行了例子分析。     

Using OWA aggregation technique in QFD: a case study in education in a textile engineering department

Quality Function Deployment (QFD) is a systematic approach that considers customer needs through design, production, marketing, and support stages. Customer needs are the main input for QFD, so voice of customer must be understood well and changes, innovations, and treatments must be held in this view. In QFD applications, determining the priorities of customer needs is a fairly important stage. This is mostly held by Analytic Hierarchy Process (AHP) a multicriteria decision making technique. Nonetheless, Ordered Weighted Averaging (OWA) is an aggregation technique mostly used in decision making for multicriteria decision problems. So, combining these two techniques will give a different viewpoint for prioritizing the customer needs in QFD applications. The aim of this study is to show the use of Ordered Weighted Averaging (OWA) aggregation technique in QFD applications. For this purpose a case study in Dokuz Eylül University Textile Engineering Department was held. It was aimed to support the efforts on increasing the education quality by determining the students’ needs and opinions using QFD with OWA.     

质量机能展开在新产品开发中的应用研究

文章以L冰箱公司所生产的家用冰箱为研究对象，演绎了把QFD技术引入到冰箱的开发中，以顾客和市场需求为出发点，进行质量机能展开，完成生产规划、零件配置、工艺规划、生产控制四阶段的质量屋过程，最后对具体实施QFD时可能遇到的问题提出了一些建议。     

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.     

QFD方法与应用研究理论综述

本文回顾了质量功能展开(QFD)方法提出的背景及发展状况,总结了一些学者对QFD方法的定义,以及它的应用情况,最后简单总结了QFD的应用模式。     

A combination of QFD and imprecise DEA with enhanced Russell graph measure: A case study in healthcare

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 prioritizes the ECs, in terms of customer's requirements. Traditionally, QFD rates the design requirements (DRs) with respect to customer needs, and aggregates the ratings to get relative importance scores of DRs. An increasing number of studies stress on the need to incorporate additional factors, such as cost and environmental impact, while calculating the relative importance of DRs. However, there is a paucity of methodologies for deriving the relative importance of DRs when several additional factors are considered. Ramanathan and Yunfeng [43] proved that the relative importance values computed by data envelopment analysis (DEA) coincide with traditional QFD calculations when only the ratings of DRs with respect to customer needs are considered, and only one additional factor, namely cost, is considered. Also, Kamvysi et al. [27] discussed the combination of QFD with analytic hierarchy process–analytic network process (AHP–ANP) and DEAHP–DEANP methodologies to prioritize selection criteria in a service context. The objective of this paper is to propose a QFD–imprecise enhanced Russell graph measure (QFD–IERGM) for incorporating the criteria such as cost of services and implementation easiness in QFD. Proposed model is applied in an Iranian hospital.     

A group decision-making method with fuzzy set theory and genetic algorithms in quality function deployment

Quality function deployment (QFD) has been developed by Toyota Motor Corporation in order to reduce time and shorten design times. QFD is composed of a set of matrices referred to as the house of quality (HOQ). A HOQ matrix can help the cross-functional team to translate customer requirements (CRs) into engineering goals. The importance of CRs and the relationships between CRs and technical characteristics (TCs) are obtained by a group of people with vague and fuzzy decision-making processes in the HOQ. In the conditions, a group decision-making method by using the combination of fuzzy set theory and genetic algorithms (GAs) can be used in QFD to determine the importance of each TC. Besides, a numerical example is illustrated to show that this group decision-making method by using the combination of fuzzy set theory and GAs can be reliably and precisely applied in QFD including TCs at the two-level hierarchy with the consideration of some constraints regarding budget and time limits of TCs for prioritizing TCs to effectively make decisions with fuzziness and ambiguousness.     

QFD在服装产品开发中的应用

首先对QFD作了简单介绍,然后结合顾客对服装产品的需求观念的改变,探讨了动态QFD在服装产品开发中的应用研究。     

Using QFD method for the ecological designing of products and services

QFD method is a method in the area of quality management. In this publication, it has been modified and used in environmental management. As an example, ecologically modiefied QFD was implemented for Bedzin district in Poland. Implementing this method in a town allows to select activities that are crucial for fulfilling ecological requirements and it leads to the improvement of the life quality of the citizens.     

Multi-layer quality function deployment (QFD) approach for improving the compromised quality satisfaction under the agency problem: A 3D QFD design for the asset selection problem in the shipping industry

The aim of this paper is to improve the quality function deployment (QFD) method by utilizing requirements of both the major customer and the service provider. The QFD method was first applied to the shipbuilding industry and the main goal of the method is to improve the production processes by using customer requirements in connection with the related technical measures of the product. However, one of the critical criticisms of the QFD is based on the lack of proper budget assessment and the satisfaction of the producer (or service provider). The multi-layer QFD design is proposed to collect responses from both customer and the service provider so as to ensure satisfaction of all parties including financial feasibility of the intended improvements. Hence, the agency problem between parties will be eliminated.     

A fuzzy group decision-making approach in quality function deployment

Quality function deployment (QFD) is comprised of two major group decision-making processes. One is to collect the customer requirements from a group of customers, whereas the other is to determine the relationship between customer requirements and technical measures by a cross-functional team. Generally, different and/or even subjective opinions are quite often in a group decision-making process due to the limitations of experience and impreciseness. Obviously, the importance of each customer requirement and the relationship between customer requirements and technical measures are determined by a group of people with imprecision and vagueness. Under such circumstances, a fuzzy group decision-making approach can be applied in QFD to deal with a group decision-making process when the information is imprecise and fuzzy. Moreover, an example is provided as well as the computational steps to show this fuzzy group decision-making approach can be effectively used in QFD to make decisions with imprecision and vagueness.