基于 QFD的物流供应商的选择问题研究

为降低物流供应商选择过程中的模糊不确定性和主观性,将QFD、三角模糊数和Kano模型三者结合起来提出一种新的物流供应商的选择方法。文中应用QFD,首先结合三角模糊数法和专家评估法确定企业需求指标项的初始相对权重、企业需求与技术特性间的关系矩阵和各技术特性间的关联矩阵,同时对各三角模糊数进行去模糊处理。然后,根据权重对全部企业需求进行排序,将排序结果区分为Kano模型中的3类需求,并用Kano系数表示,同时据此计算各技术特性的权重值。最后,综合技术特性权重值和候选物流供应商在各特性上的分值,经加权求和得出各供应商的总得分。通过算例证实了所提方法的可行性和有效性,能够给企业提供具体的、可操作的指导。     

基于FAHP的汽车制动盘质量评价及权重确定

现采用模糊层次分析法（FAHP）对汽车制动盘质量影响因素进行分析,建立了评价模型,将模糊一致矩阵引入层次分析中,通过建立模糊一致矩阵获得了接近实际的权重分析。最后以某企业所生产的汽车制动盘为例,验证了用FAHP法来确定影响汽车制动盘质量因素的可行性。     

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。     

模糊层次分析法在企业技术创新能力指标权重确定中的应用

技术创新能力是决定企业生存和发展的重要因素。利用三角模糊数构造模糊互补判断矩阵,进而确定体系中各指标的权重。模糊层次分析法考虑到评判人思维判断和评价对象的模糊性,避免了权重确定中的随意性。     

基于AHP和模糊贴近度的员工配置问题研究

文章首先对通用胜任力素质模型进行系统分析,以探索不同行业的胜任力特点,进一步确定各主要行业工作人员的胜任力指标,进而利用层次分析法（AHP）确定各胜任力指标在模型中所占的权重,得到权重矩阵,然后根据权重矩阵和模糊贴近度计算效率矩阵,利用指派模型将合适人才配置到合适的岗位上去。     

供应链协同中供应商梯形模糊评价研究

首先给出面向供应链协同管理的供应商评价指标体系,然后基于梯形模糊数及模糊一致矩阵构建了供应商模糊综合评价方法,侧重设计了指标预处理和优先关系矩阵。对定性指标采用梯形模糊数进行预处理,实现无量纲化并避免设计隶属度函数;构建优先关系矩阵时对定性指标采用两两比较法,对定量指标采用模糊一致判断矩阵元素与权重之关系式的途径,以提高定量指标数据的区分精度。案例计算表明,该评价方法对定性指标和定量指标的处理技术更加科学客观,比传统模糊评价方法更合理,能为供应链协同管理中供应商选择提供科学的决策支持。     

基于模糊QFD和模糊TOPSIS的工序质量改进研究

将质量功能展开(QFD)方法应用到工序质量评价中,通过构建工序质量评价质量屋模型,找出影响工序质量的因素及权重;将模糊TOPSIS方法应用到工序质量改进中,确定工序的改进需求度,以量化的方法决定所需要改进的因素及工序质量的综合水平,从而为提高工序质量提供明确的方向。     

模糊QFD在提升港口服务质量中的应用

将质量功能展开(QFD)方法应用到提高港口服务质量中,通过构建港口服务质量屋模型,采用模糊层次分析法(FAHP)确定顾客需求权重,找出影响顾客对港口服务满意度的关键因素及存在的问题,以量化的方法决定所需要改进的关键服务措施与手段,从而为提升港口服务水平和竞争力提供有效的建议。     

装备维修过程质量评估研究

对装备维修质量的评估,不仅要针对维修后各种指标的恢复程度,还要对维修过程中的各种因素进行评价,以便维修企业对维修过程进行控制以及改进。运用基于AHP的模糊综合评价法对装备维修过程进行评估,并利用最优传递矩阵法对标度矩阵判断法确定权重进行改进,从而避免一致性检验,评价结果更加科学合理。     

基于borda序值法和模糊层次分析法对跨国公司R&D外包的风险度量

本文采用德尔菲法识别研发外包项目中可能出现的主要风险,再运用改进后的风险矩阵对已识别出的主要风险因素量化分析,然后采用Borda序值法初步对风险因素初步排序,最后运用模糊层次分析法对各风险要素进行权重计算并排序,确定风险的相对大小。本文采用的模糊层次分析法主要包括建立层次结构模型、构造模糊互补判断矩阵、计算各风险因素的权重、模糊互补判断矩阵的一致性检验以及总排序。     

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.     

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.     

Applying fuzzy quality function deployment to identify service management requirements for customer quality needs

In this paper a fuzzy quality function deployment to identify service management requirements for customer quality needs is developed. First, customer quality needs with importance degree have been investigated followed by development of service management requirements for satisfying the observed customer quality needs. Furthermore, construct a fuzzy relation matrix to link service management requirements and customer quality needs based on cross-functional expertise. The aggregated fuzzy assessments of various service management requirements are ranked to determine their importance priority. Finally, an empirical study for identifying the service management requirements for customer quality is used to demonstrate the proposed approach.     

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 method for determining customer requirement weights based on TFMF and TLR

‘Customer requirements’ (CRs) management plays an important role in enterprise systems (ESs) by processing customer-focused information. Quality function deployment (QFD) is one of the main CRs analysis methods. Because CR weights are crucial for the input of QFD, we developed a method for determining CR weights based on trapezoidal fuzzy membership function (TFMF) and 2-tuple linguistic representation (TLR). To improve the accuracy of CR weights, we propose to apply TFMF to describe CR weights so that they can be appropriately represented. Because the fuzzy logic is not capable of aggregating information without loss, TLR model is adopted as well. We first describe the basic concepts of TFMF and TLR and then introduce an approach to compute CR weights. Finally, an example is provided to explain and verify the proposed method.     

Applying fuzzy quality function deployment (QFD) to identify solutions of service delivery system for port of Kaohsiung

The main purpose of this paper is to apply fuzzy quality function deployment (QFD) model to identify solutions of service delivery system (SDS) for port of Kaohsiung from the viewpoints of customers. At first, to facilitate the main issue of the QFD problem, however, the ‘what’ question of customer needs and ‘how’ problem of the services have to be made, which are two major components and be emphasized on the house of quality (HOQ) matrices. In conjunction with fuzzy sets theory, hence, the systematic procedures using fuzzy QFD were proposed in this paper. Subsequently, a case study for port of Kaohsiung demonstrated the systematic appraisal process for identifying solutions of SDS. The results of empirical study show that (1) 10 key factors are deemed as to have priority to improve the quality of SDS for Kaohsiung port; and (2) eight feasible solutions for improving service quality performance are identified. Moreover, it is suggested that port Authority of Kaohsiung should listen attentively the voice of customers and emphasize on exploiting these customer requirements effectively. And then develop the ‘how’ issues of profiles of solutions, which should continuously strengthen the perspectives of customer, internal business process, and learning and growth, respectively.     

Determination of an optimal set of design requirements using house of quality

Quality Function Deployment (QFD) has been used to translate customer needs and wants into technical design requirements in order to increase customer satisfaction. QFD utilizes the house of quality (HOQ), which is a matrix providing a conceptual map for the design process, as a construct for understanding Customer Requirements (CRs) and establishing priorities of Design Requirements (DRs) to satisfy them. Some methodological issues occurring in the conventional HOQ are discussed, and then a new integrative decision model for selecting an optimal set of DRs is presented using a modified HOQ model. The modified HOQ prioritization procedure employs a multi-attribute decision method for assigning relationship ratings between CRs and DRs instead of a conventional relationship rating scale, such as 1–3–9. The proposed decision model has been applied to an indoor air quality improvement problem as an illustrative example.     

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.     

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.