Modelling of quality function deployment planning with resource allocation

Quality Function Deployment (QFD) is a well-known customer-oriented methodology, which is widely used to assist decision making in product design and development in various types of production. Determining how and to what extent certain characteristics or technical attributes (TAs) of products are to be met, with a view to gaining a higher level of overall customer satisfaction, is a key success factor in product design and development. An operational QFD planning problem with resource allocation is considered in this paper. The aim is to plan the attainment of TAs by allocating resources among the TAs with a view to achieving maximized overall customer satisfaction. Taking into account the technical and resource constraints, and the impact of the correlation among TAs, the operational QFD planning with resource allocation is formulated as a linear program and solved by a heuristics-combined Simplex Method. An overall procedure is presented to help a design team to implement this QFD design planning with resource allocation in practice. This model can bridge the gap and conflicts between the design targets at the strategic level, and resource allocations in the part deployment and operational process planning level.     

Quantification and integration of Kano’s model into QFD for optimising product design

With increasing concerns on customer needs in today’s competitive market, the issue of incorporating customer requirements into product design arises the interest of both researchers and practitioners. Quality Function Deployment (QFD) is a well-known methodology for customer-driven product design. However, conventionally, QFD analysis has a major challenge in understanding customer needs accurately. Kano’s model, which studies the nature of customer needs, provides a way for a better classification of customer needs. However, seldom research contributions are found in terms of integrating Kano’s model with QFD quantitatively. In this research, a novel integration approach is proposed. At first, Kano’s model is quantified by identifying relationship between customer needs and customer satisfaction (CS). Next, both qualitative and quantitative results from Kano’s model are integrated into QFD. Finally, a mixed non-linear integer programming model is formulated to maximise CS under cost and technical constraints. In this research, an illustrative example associated with the design of notebook computers is also presented to demonstrate the availability of the proposed approach.     

Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment

Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to increase customer satisfaction by integrating marketing, design engineering, manufacturing, and other related functions of an organization. QFD focuses on delivering value by taking into account customer needs and then deploying this information throughout the development process. Although QFD aims to maximize customer satisfaction, technology and cost considerations limit the number and the extent of the possible design requirements that can be incorporated into a product. This paper presents a fuzzy multiple objective programming approach that incorporates imprecise and subjective information inherent in the QFD planning process to determine the level of fulfilment of design requirements. Linguistic variables are employed to represent the imprecise design information and the importance degree of each design objective. The fuzzy Delphi method is utilized to achieve the consensus of customers in determining the importance of customer needs. A pencil design example illustrates the application of the multiple objective decision analysis.     

A novel fuzzy linguistic model for prioritising engineering design requirements in quality function deployment under uncertainties

Quality function deployment (QFD) is a planning and problem-solving tool gaining wide acceptance for translating customer requirements (CRs) into the design requirements (DRs) of a product. Deriving the priority order of DRs from input variables is a crucial step in applying QFD. Due to the inherent vagueness or impreciseness in QFD, the use of fuzzy linguistic variables for prioritising DRs has become more and more important in QFD applications. Existing approaches make use of the associated fuzzy membership functions of linguistic labels based on the fuzzy extension principle. However, an inherent limitation of such fuzzy linguistic approaches is the information loss caused by approximation processes, which eventually implies a lack of precision in the final results. This paper proposes an alternative approach to prioritising engineering DRs in QFD based on the order-based semantics of linguistic information and fuzzy preference relations of linguistic profiles, under random interpretations of customers, design team and CRs. Ultimately, this approach enhances the fuzzy-computation-based models proposed in the previous studies by overcoming the mentioned limitations. A case study taken from the literature is used to illuminate the proposed technique and to compare with the previous techniques based on fuzzy computation.     

A fuzzy expected value-based goal programing model for product planning using quality function deployment

Product planning is one of four important processes in new product development using quality function deployment (QFD), which is a widely used customer-driven approach. In this article, a hierarchical framework for product planning using QFD is developed. To tackle the fuzziness in functional relationships between customer requirements and engineering characteristics (ECs) in product planning, the least squares method is incorporated into fuzzy regression to investigate those functional relationships, by which a more central tendency can be obtained. Furthermore, a fuzzy expected value-based goal programing model is proposed to specify target values of ECs. Different from some fuzzy product planning approaches for QFD, the proposed programing model has unambiguous interpretations. An illustrated example of a quality improvement problem of emulsification dynamite-packing machine design is given to demonstrate the application and performance of the proposed approach.     

Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD

Quality function deployment (QFD) is a planning and problem-solving tool that is renowned for translating customer requirements into the technical attributes of a product. To deal with the imprecise elements in the development process, fuzzy set theory is incorporated into QFD methodology. A novel fuzzy expected value operator approach is proposed in this paper to model the QFD process in a fuzzy environment, and two fuzzy expected value models are established to determine the target values of engineering characteristics in handling different practical design scenarios. Analogous to stochastic programming, the underlying philosophy in the proposed approach is based on selecting the decision with maximum expected returns. Furthermore, the proposed approach considers not only the inherent fuzziness in the relationships between customer requirements and engineering characteristics, but also the correlation among engineering characteristics. These two kinds of fuzzy relationships are aggregated to give the fuzzy importance of individual engineering characteristics. Finally, an example of a quality improvement problem of a motor car design is given to demonstrate the application and performance of the proposed modelling approach.     

Determining design requirements in QFD using fuzzy mixed-integer goal programming: application of a decision support system

In product development, the identification of critical design requirements (DRs) is key to satisfying customer needs because it helps produce more successful products in a shorter time. Quality function deployment (QFD) is a tool used in product development to systematically determine the DRs so as to attain higher customer satisfaction. In the QFD process, the simultaneous optimisation of more than one conflicting objective is generally required. However, it is very difficult for decision makers to determine the goal value of each objective in imprecise and uncertain environments. In order to overcome this problem, the present study proposes a fuzzy mixed-integer goal programming model that determines a combination of optimal DR values. Different from the existing fuzzy goal programming models, the values of the DRs in the proposed model are taken as discrete. Finally, a new Decision Support System is developed. The new system integrates QFD and mathematical programming, enabling the design team to effectively compare product design alternatives and make product development easier and faster. The proposed methodology is illustrated using a real-world application in the Turkish white goods industry.     

Application of fuzzy quality function deployment for sustainable design of consumer electronics products: a case study

With a steep increase in the demand for consumer electronics products, the contemporary manufacturers are committed toward sustainable development of such products. There exists a scope for developing a methodology for enabling sustainable development of consumer electronics products. In this context, fuzzy quality function deployment (QFD) approach has been presented in this article in order to prioritize relevant customer requirements, sustainability parameters and sustainability initiatives. Key influential parameters for sustainable development of consumer electronics products have been identified from the literature. In the first phase of fuzzy QFD, parameters influencing sustainable development have been prioritized in accordance with customer requirements. In the second phase, environmental design initiatives have been prioritized based on critical sustainability parameters. From phase I of fuzzy QFD, ‘reduction in environmental release’ has been found as the most significant sustainability parameter with a crisp value of 22.83, and from phase II, environmental impact assessment is proved to be the significant design method with a crisp value of 20.40. The methodology would provide a comprehensive understanding to practitioners on the interrelationships among customer requirements, sustainability parameters and environmentally benign initiatives for development of consumer electronic products. The generic model developed can be applied to most of the consumer electronics product     

Normalisation models for prioritising design requirements for quality function deployment processes

The prioritisation of design requirements is critical for determining resource allocation in the new product design stage through the quality function deployment (QFD) planning processes. To prioritise design requirements, normalisation models are usually used to perform aggregation and normalisation functions. Recognising the weakness of the Wasserman normalisation model from both theoretical and practical viewpoints, this paper proposes an improved normalisation model for the aggregation and normalisation functions. It is verified that the proposed model satisfies Lyman’s normalisation requirement and avoids the problem of Wasserman’s normalisation model. In addition, a normalisation model that takes into account the correlation among customer requirements is also developed to complete a generalised normalisation model for QFD planning processes. A product design case is presented to demonstrate the advantages of the proposed normalisation models.     

QFD optimization using linear physical programming

A new approach to quality function deployment (QFD) optimization is presented. The approach uses the linear physical programming (LPP) technique to maximize overall customer satisfaction in product design. QFD is a customer-focused product design method which translates customer requirements into product engineering characteristics. Because market competition is multidimensional, companies must maximize overall customer satisfaction by optimizing the design of their products. At the same time, all constraints (e.g. product development time, development cost, manufacturing cost, human resource in design and production, etc.) must be taken into consideration. LPP avoids the need to specify an importance weight for each objective in advance. This is an effective way of obtaining optimal results. Following a brief introduction to LPP in QFD, the proposed approach is described. A numerical example is given to illustrate its application and a sensitivity analysis is carried out. Using LPP in QFD optimization provides a new direction for optimizing the product design process.     

Determining the Importance Weights for the Customer Requirements in QFD Using a Fuzzy AHP with an Extent Analysis Approach

In the Quality Function Deployment (QFD) process, determining the importance weights for the customer requirements is an essential and crucial process. The Analytic Hierarchy Process (AHP) has been used to determine the importance weights for product planning, but this has occurred mainly in crisp (non-fuzzy) decision applications. However, human judgment on the importance of customer requirements is always imprecise and vague. To make up for this deficiency in the AHP, a fuzzy AHP with an extent analysis approach is proposed to determine the importance weights for the customer requirements. In the method, triangular fuzzy numbers are used for the pairwise comparison of a fuzzy AHP. By using the extent analysis method and the principles for the comparison of fuzzy numbers, one can derive the weight vectors. The new approach can improve the imprecise ranking of customer requirements inherited from studies based on the conventional AHP. Furthermore, the fuzzy AHP with extent analysis is simple and easy to implement to prioritize customer requirements in the QFD process compared with the conventional AHP. This paper uses an example of a hair dryer design to illustrate the proposed approach.     

基于Kano模型的用户需求指标体系研究

用户需求是产品设计的源头,设计的目的是为了开发出满足用户需求的产品.本文基于产品的质量指标体系,用Kano模型对用户需求项目进行研究,分析消费者对产品在时间、质量、经济性、服务、社会性等方面的需求,制订了基于Kano模型的用户需求指标体系,为用户需求的知识获取、QFD方法应用及智能设计打下基础.     

Improving Product Design Using Quality Function Deployment: The School Furniture Case in Developing Countries

This paper presents a quality function deployment (QFD) analysis of the design of school furniture in developing countries, using Costa Rica as the baseline. The dynamic hierarchy process model for QFD was used to help the product development team make effective decisions in satisfying the requirements of the customer constrained by limited resources. A number of total quality management (TQM) tools were employed during the development of the school furniture solution. A dynamic, cross-functional team organization was used. A simple form of quality function deployment was used to identify the desirable product design, safety, and service features.     

基于扩展QFD模型的包装机械质量成本分析

在QFD质量屋基础上提出扩展QFD模型,引入质量保证成本评价指标、质量损失成本评价指标及技术要求综合评价指标,对顾客需求转化而成的技术需求进行成本分析,为包装机械产品开发及设计人员选择和确定实际的技术特征提供科学的依据.包装机设计的应用实例表明,利用扩展的QFD模型能够快速、有效的进行产品质量成本分析.     

Hesitant fuzzy integrated MCDM approach for quality function deployment: a case study in electric vehicle

Quality function deployment (QFD) is a product planning management instrument which has been used in a broad range of industries. However, the traditional QFD method has been criticised much for its deficiencies in acquiring experts’ opinions, weighting customer requirements (CRs) and ranking engineering characteristics (ECs). To overcome the limitations, an integrated analytical model is presented in this study for obtaining the importance ratings of ECs in QFD by integrating decision-making trial and evaluation laboratory (DEMATEL) technique and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method under hesitant fuzzy environment. In particular, the hesitant fuzzy DEMATEL is used to analyse the interrelationships among CRs and determine their weights, and the hesitant fuzzy VIKOR is utilised to prioritise ECs. Finally, the feasibility and practicality of the proposed method are verified by an example regarding the product development of electric vehicle.     

基于QFD-TRIZ的电热水器设计

目的 为明确电热水器产品功能要素与需求要素的对应关系,提高电热水器用户的使用满意度。方法 通过构建QFD与TRIZ的集成设计模型,使用亲和图筛选分析顾客需求,利用QFD整理顾客需求权重并构建质量屋,从而明确关键设计要素;通过TRIZ分析方法判断了质量屋中的矛盾类型后,将设计要求转换为与工程要素间的矛盾,并利用阿奇舒勒矩阵中相应的发明原理,提出了解决矛盾的方法。结果 通过模糊综合评价法筛选最优设计方案,最终的评估结果表明新的设计方案满足了实际市场用户需求的目标问题优化。结论 从用户被动发现产品零部件寿命耗尽转换为产品信息主动提示,并具备辅助用户操作的结构,提升了用户需求与产品功能的匹配度,可见质量功能展开理论与发明问题解决理论的集成设计方法对产品概念设计阶段的重要性,为之后的室内智能家电设计研究提供了参考。     

质量屋中顾客需求向技术特征映射的一种方法

在利用质量功能配置(QualityFunctionDeployment,QFD)进行产品规划决策时,根据顾客需求选取技术特征,是实现产品规划中顾客域到技术特征域转换进而进行产品规划的关键。建立了资源约束下最大化顾客满意程度和最大化技术特征集表现度的整数规划模型,实现了顾客需求域到技术特征域的映射,从而确定出质量屋决策中需重点考虑的技术特征项,并给出了实例。     

服务产品设计质量保证的质量功能展开集成方法

为处理服务产品设计中散乱的模糊顾客需求信息,客观反映用户质量需求,解决传统质量功能展开方法中判断一致性差和整体满意度的展开精度低等问题,将模糊聚类分析、模糊层次分析法和质量屋方法集成,引入到服务产品设计的质量保证中。通过模糊聚类对顾客质量要求进行层次划分,在此基础上使用模糊层次分析法确定顾客质量需求权重,继而在质量屋关系矩阵中引入模糊集进行评判,从而将自然语言与模糊数相联系,更加准确地把握顾客需求。同时采用模糊数表示权重分析和关系度评判结果,提高了评判的精度和稳定性,为满足服务产品质量设计阶段质量保证工作提供一种量化分析方法。     

Proceedings Reviews

Abstract:

Quality Function Deployment (QFD) is a systematic process for capturing and integrating the voice of the customer into every aspect of the design and delivery of products and services. Understanding customer wants or needs is crucial to the successful design and development of new products and services. QFD is a system that utilizes customer demand information to design products or services that will meet a client's mission. In addition, the process prioritizes and deploys these customer-driven characteristics throughout the product or service development to meet the customer needs, wants, and expectations. QFD determines effective development targets for the prioritized product and service characteristics. The QFD process has been used and documented extensively in product development. The service industry, however, lacks in the application of this process. The purpose of this article is to show practitioners and researchers how this process, in its entirety, can be used as a planning process to link customer requirements and service characteristics in the service industry. A case study was developed in which QFD was applied to develop recommendations for the American Society of Engineering Management (ASEM) in an effort to increase customer satisfaction and to identify opportunities to improve member benefits. The results of this study are applicable to any organization to improve the design and delivery of products and service regardless of industry.