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 importance ratings of patients’ requirements with multi-granular linguistic evaluation information

Determination of patients’ requirements (PRs) importance is a critical issue of medical service design. Quality function deployment (QFD) is one of the very effective customer-driven quality system tools typically applied to fulfil PRs. Often the patients cannot easily express their judgements on PRs’ importance with exact numerical values, and they usually present their judgements in different scales. Therefore, this paper aims at providing a systematic method to simultaneously deal with PRs’ fuzziness and multi-granularity in the QFD. Compared to previous research, its contribution is threefold. First, it proposes use of the 2-tuple linguistic model which can effectively manage the imprecise and vague evaluation information in QFD because of its accuracy and no information loss. Second, it develops a new 2-tuple transformation function to solve the unification problem of multi-granular linguistic judgements in QFD. Third, it proposes a qualitative approach to deal with uncertainty existed extensively in establishing modified factors. Finally, a practical case of hospital PRs is provided to illustrate its feasibility and effectiveness of the proposed methodology.     

A combined QFD and integer programming framework to determine attribute levels for conjoint study

Conjoint Analysis (CA) and Quality Function Deployment (QFD) are two popular tools for new product design; marketers frequently use the former and engineers the latter. Typically, in a conjoint study, the attributes and their levels are determined through focus group discussions or market surveys. Sometimes, the market researchers exclude some critical features or include unrealistic attribute levels resulting in infeasible product profiles. Inappropriate selection of attribute levels may render the conjoint study less useful. In QFD, the New Product Development team attempts to identify the technical characteristics (TCs) to be improved (included) to meet the customer requirements (CRs) through a subjective relationship matrix between CRs and TCs. At present there is no methodology that uses the output of QFD to generate feasible product profiles to be used in CA and therefore improve its usefulness. In this paper, QFD is used along with an integer programming (IP) model to determine the appropriate TCs and consequently the right attribute levels. These attribute levels are then used in a conjoint study. It is also proposed to measure the elements of the so-called relationship matrix in QFD in a way so that the right levels of the attributes can be generated from the IP solution. The proposed method is illustrated through a commercial vehicle design problem with hypothetical data.     

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.     

Dynamic Programming for QFD Optimization

Quality function deployment (QFD) is a useful method in product design and development and its aim is to improve the quality and to better meet customers' needs. Due to cost and other resource constraints, trade‐offs are always needed. Many optimization methods have been introduced into the QFD process to maximize customer satisfaction under certain constraints. However, current optimization methods sometimes cannot give practical optimal results and the data needed are hard or costly to get. To overcome these problems, this paper proposes a dynamic programming approach for the optimization problem. We first use an extended House of Quality to gather more information. Next, limited resources are allocated to the technical attributes using dynamic programming. The value of each technical attribute can be determined according to the resources allocated to them. Compared with other optimization methods, the dynamic programming method requires less information and the optimal results are more relevant. Copyright © 2005 John Wiley & Sons, Ltd.     

Prioritising the types of manufacturing flexibility in an uncertain environment

Researchers have stressed that manufacturing system flexibility research requires a quantitative model allowing a manufacturing system to prioritize its flexibility dimension and promote the performance of the manufacturing system. A quantification model presented in the present research is demonstrated to assess the degree of environmental uncertainty and illustrates a method for delivering the requirement of flexibility improvement for the manufacturing system so that the company is able to prioritize the types of manufacturing flexibility which a manufacturing system requires in an uncertain environment. Quantitative approaches including quality function deployment (QFD), analytical hierarchy process (AHP), and grey relational analysis (GRA) have been employed to find a means for improving the flexibility of a manufacturing system to cope with environmental uncertainty. QFD is the focal approach for the deployment of the integrated structure of the research. AHP is applied to explore the relative weighted importance of environmental uncertainty factors, while GRA is used to find out the relationships between manufacturing flexibility and environmental uncertainty. A combination of these approaches reveals a useful tool for managers to prioritize the types of flexibility which a manufacturing system requires for coping with an uncertain environment. In particular, the present research studied the manufacturing flexibility requirements of a food company in Taiwan.     

基于网络评论情感分析和QFD的产品改进设计

目的 分析产品在线网络评论,准确评估用户的使用反馈和需求信息,用于产品改进设计。方法 用网络爬虫采集购物网站的商品评论数据,引入百度AI开放平台的自然语言处理技术,对清洗后的评论数据进行情感分析,计算每条评论的情感极性,结合Nvivo文本分析和人工筛选,得到准确的用户评价。在质量功能展开(QFD)中,评论分析的结果用于产品的目标质量规划,根据质量屋得出的技术特性相对重要度,选择应重点改进的技术属性。结果 该方法发现了产品需要改进的多个重要设计问题,以无线手持吸尘器为例,对比传统调研方法的分析结果,验证该方法的有效性。结论 对网络评论大数据的AI情感分析结合QFD,能发现产品需要改进的大部分问题,具有重要的参考价值,能为产品改进设计提供新方法。     

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.     

A novel fuzzy group decision-making approach to prioritising engineering characteristics in QFD under uncertainties

In new product development, design teams commonly need to define engineering characteristics (ECs) in a quality function deployment (QFD) planning process. Prioritising the engineering characteristics in QFD is essential to properly plan resource allocation. However, the inherent vagueness or impreciseness in QFD presents a special challenge to the effective calculation of the importance of ECs. Generally, there are two types of uncertain input in the QFD process: human perception and customer heterogeneity. Many contributions have been made on methods to prioritise ECs. However, most previous studies only address one of the two types of uncertainties that could affect the robustness of prioritising ECs. To address the two types of uncertainties simultaneously, a novel fuzzy group decision-making method that integrates a fuzzy weighted average method with a consensus ordinal ranking technique is proposed. An example is presented to illustrate the effectiveness of the proposed approach. Results of the implementation indicate that the robustness of prioritising ECs based on the proposed approach is better than that based on the method of Chen et al. (Chen, Y., Fung, R.Y.K., Tang, J.F., 2006. Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. European Journal of Operational Research, 174 (3), 1553–1556).     

基于质量功能展开的校园APP服务质量改进研究

目的 为了提高校园APP服务质量,提出一种基于质量功能展开(Quality Function Deployment,QFD)的改进研究方法,其将定性定量方法相结合以保证改进方案的可信性。方法 首先,基于七维度用户需求列表,使用问卷调查用户对校园APP的使用体验及期望,并对校园APP用户满意度进行了数据信度检验与验证性因子分析,以确保调查数据及用户需求模型的正确性;其次,结合用户需求改进率和熵值法构建了QFD中质量屋的规划矩阵;再次,采用文献调查法与专家意见集合法确认技术需求,并构建了质量屋的关系矩阵及技术关联矩阵;最后,计算管理需求中的技术需求重要度,完成从“用户需求—技术需求”的转换。结果 从质量屋的分析中获得了可提高校园APP服务质量的多个改善建议。结论 结合七维度的用户需求模型,采用QFD方法可以指导设计师对校园APP进行更合理的规划设计与改进,以提高校园APP服务质量,同时也为其他类似问题提供了解决思路。     

Application of Traditional and Fuzzy Quality Function Deployment in the Product Development Process

It is the nature of problems in quality management to contain elements of uncertainty. The uncertainty of input data is the result of a subjective approach to the interpretation of imprecise information. The aim of this article is to present the application of an effective fuzzification instrument in dealing with different kinds of uncertainty and also the application of natural language to model decision making in quality management. The article shows how the integration of fuzzy logic can assist with the planning of a particular product. First, the traditional Quality Function Deployment (QFD) method was used for the purpose of achieving quality improvement in a boiler (house electric water heater). Second, the QFD method combined with fuzzy logic was used for the same purpose. The preferred quality characteristics were defined using both traditional and fuzzy QFD methods, and results obtained were compared and discussed. The implications of the research for practice and future research are outlined.     

技术特征优先度评估的组合赋权法简

 研究利用质量功能配置进行产品规划时确定技术特征优先度的问题,提出先分别利用质量屋中主观和客观信息进行赋权计算、再加权集成的组合赋权法.主观赋权中,考虑多名决策者参加多粒度多语义的情况,使用语言加权平均算子与语言混和集成算子集结群体评估信息,体现数据本身及其所在位置的重要性;客观赋权中,以区间数形式表示无法明确的技术特征值,将含有区间数的技术竞争性数据转化为目标相离度矩阵,然后使用熵值法赋权.综合集成结果既反映了专家的经验判断,又反映了对现实数据的逻辑推理,实现了主观和客观的协调统一.     

Determining the importance ratings of customer requirements in quality function deployment based on interval linguistic information

Quality function deployment (QFD) is a customer-oriented tool and is widely applied to design and improve products and services. Determining the importance ratings (IRs) of customer requirements (CRs) is an essential step in QFD application and will affect the quality of product design and improvement. In this study, a group decision-making method is proposed to obtain realistic IRs. Low-carbon environment is considered in recognising CRs. Interval linguistic information (ILI) is used to express the vague evaluations in product improvement. In addition, an interval linguistic weighted arithmetic averaging operator, a normalised formula, and an expected value operator are integrated to deal with evaluation matrices expressed by ILI. A relationship matrix is used reversely to acquire accurate basic IRs (BIRs). The improved cosine method based on ILI is also employed to derive BIRs. The modified entropy method based on ILI is proposed to determine a competitive priority rating (CPR) of a CR. The final IR of every CR can be obtained by integrating its BIR and CPR. Finally, a practical product improvement of turbine engine is provided to illustrate the validity and feasibility of the proposed approach.     

Improved approach to quality function deployment based on Pythagorean fuzzy sets and application to assembly robot design evaluation

Quality function deployment (QFD) is an effective method that helps companies analyze customer requirements (CRs). These CRs are then turned into product or service characteristics, which are translated to other attributes. With the QFD method, companies could design or improve the quality of products or services close to CRs. To increase the effectiveness of QFD, we propose an improved method based on Pythagorean fuzzy sets (PFSs). We apply an extended method to obtain the group consensus evaluation matrix. We then use a combined weight determining method to integrate former weights to objective weights derived from the evaluation matrix. To determine the exact score of each PFS in the evaluation matrix, we develop an improved score function. Lastly, we apply the proposed method to a case study on assembly robot design evaluation.     

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.     

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.     

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.     

Modelling customer and technical requirements for sustainable supply chain planning

Sustainable supply chains are the need of modern times. In order to make them successful, listening to voice of the customer and integrating them in the design and development phases of supply chains are very important. In this paper, we propose a technique called sustainability function deployment (SFD) developed on the concept of quality function deployment (QFD) to model customer and technical requirements, establish relationship between them and prioritise them for developing sustainable supply chains. SFD gathers customer and technical requirements for sustainable supply chain planning using C-REQ and T-REQ surveys, generates priority of customer requirements using Priority Matrix, performs screening of technical requirements using gap analysis, and generates requirements weights using QFD, thereby providing a structured approach to the decision-makers to select the right areas of improvement. Both the customer and technical requirements are captured from a socio-economic-environmental perspective. Results of application of the proposed technique are provided. The strength of the proposed approach is its novelty in integration of priority matrix and gap analysis in prioritising requirements based on their importance and performance in SFD. Besides, SFD facilitates modelling of complex, interrelated requirements in sustainable supply chain planning.     

An information fusion reliability allocation method considering limited sample size circumstances

As an essential part in product reliability design procedure, reliability allocation can connect the reliability index between the overall product and its multiple subsystems. To obtain a reasonable reliability allocation scheme, especially for limited sample size circumstances commonly encountered in aeronautic and astronautic fields, an information fusion allocation method is constructed in the current study. Grey relational analysis (GRA) is incorporated to analyze objective data, while analytic hierarchy process (AHP) is introduced to implement subjective data analysis. Then a final allocation scheme is derived by fusion of the binary information sources. A real application regarding an aeronautic product reliability allocation is given. The sensitivity of allocation result to limited life information circumstances and comparative analysis with reference methods are implemented to demonstrate the reasonability and effectiveness.     

Using Fuzzy Cost‐Based FMEA,GRA and Profitability Theory for Minimizing Failures at a Healthcare Diagnosis Service

This paper proposes an integrated approach to identify, evaluate and improve the potential failures in a service setting. This integrated approach combines Fuzzy cost‐based service‐specific FMEA (FCS‐FMEA), Grey Relational Analysis (GRA) and profitability theory for better prioritization of the service failures by considering cost as an important issue and using the profitability theory in a way that the corrective actions costs are taken into account. Considering profitability with FCS‐FMEA and GRA reduces the losses caused by failure occurrence. Besides, a maximization linear mathematical problem is used to select the best mix of failures to be repaired. We apply our approach to an academic example concerning the potential failures diagnosis of the Internal Medicine service of a hospital located in Seoul, Korea. We applied our approach and solved the associated maximization problem by a commercial solver, producing an optimal solution which indicates the most convenient mix of failures to be repaired by considering available budget. Copyright © 2013 John Wiley & Sons, Ltd.