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.     

Product design resources optimization using a non-linear fuzzy quality function deployment model

Quality Function Deployment (QFD) is a well-known customer-oriented methodology that is widely used to assist decision-making in product design and development in various types of production including highly customized One-of-a-Kind Production (OKP), batch production as well as continuous/ mass production. Determining how and to what extent (degree) certain characteristics/technical attributes (TA) of products are to be met with a view to gaining a higher level of overall customer satisfaction is a key to successful product design and development. Most of the existing approaches and models for QFD planning seldom consider the resource constraints in product design, nor do they normally take into account the impacts of the correlation among various TA. In other words, most of the existing QFD applications assume that the resources committed fully to attaining the design target for one TA have no impacts on those for other TA. Hence, the costs/resources required are usually worked out individually by linear formulation. In practice, design resource requirements should be expressed in fuzzy terms to accommodate the imprecision and uncertainties innate in the design process, such as ill-defined or incomplete understanding of the relationship between a given set of customer requirements (CR) and TA, the complexity of interdependence among TA, etc. A non-linear fuzzy model is proposed here to offer a more practical and effective means of incorporating the resource factors in QFD planning. The impacts of the correlation among TA are also considered. In the model, the resources for achieving the design target for a certain TA are expressed in a non-linear formulation of its relationship, correlation as well as interdependence with other customer requirements or TA. The concepts of the achieved attainments and planned attainments for TA, and the corresponding primary costs, planned costs and actual costs are introduced. Solutions to the non-linear fuzzy model can be obtained using a parametric optimization method or a hybrid genetic algorithm. A case study is also given to illustrate how the proposed fuzzy model and the optimization routine can be applied to help decision-makers in a company deploy their design resources towards gaining better overall customer satisfaction.     

Robot selection using an integrated approach based on quality function deployment and fuzzy regression

The decision-makers have been experiencing difficulties in determining the most suitable robot alternative due to the increase in number of robots and the diversity in their application areas. A robust decision framework for robot selection should consider multiple and conflicting criteria and the dependencies among them. This paper introduces a decision model for robot selection based on quality function deployment (QFD) and fuzzy linear regression. The proposed approach benefits from the fact that QFD focuses on delivering value by taking into account the customer requirements and then by deploying this information throughout the development process, and applies this perspective to robot selection. Fuzzy linear regression is considered as an alternative decision aid for robot selection problems where imprecise relationships among system parameters exist. An example robot selection problem is presented to illustrate the proposed decision-making approach.     

Prioritizing quality characteristics in dynamic quality function deployment

Due to the combination of rapid influx of new technology, high pressure on time-to-market and increasing globalization, the number of products that have highly uncertain and dynamic specifications or customer requirements might significantly increase. In order to deal with these inherently volatile products or services, we need to adopt a more pro-active approach in order not to produce an unwanted product or service. Thus, based on the idea of the quality loss function and the zero-one goal programming, an intuitively simple mathematical model is developed to prioritize the quality characteristics (QCs) in the dynamic quality function deployment (QFD). It incorporates a pro-active approach towards providing products and services that meet the future voice of the customer (FVOC). The aim is to determine and prioritize only the ‘important’ QCs with a greater confidence in meeting the FVOC. It is particularly useful when the number of the potentially dominant QCs is very large so that, by using the prioritization, the size of the QFD can be effectively reduced. Some constraints, such as minimum customer satisfaction level and limitation on budget are also taken into consideration. A sensitivity analysis is suggested to give an insight to the QFD users in the change of parameters of the proposed model.     

Using quality function deployment to capture the voice of the customer and translate it into the voice of the provider.

BACKGROUND: Health care has a number of historical barriers to capturing the voice of the customer and to incorporating customer wants into health care services, whether the customer is a patient, an insurer, or a community. Quality function deployment (QFD) is a set of tools and practices that can help overcome these barriers to form a process for the planning and design or redesign of products and services. The goal of the project was to increase referral volume and to improve a rehabilitation hospital's capacity to provide comprehensive medical and/or legal evaluations for people with complex and catastrophic injuries or illnesses. HIGH-LEVEL VIEW OF QFD AS A PROCESS: The steps in QFD are as follows: capture of the voice of the customer, quality deployment, functions deployment, failure mode deployment, new process deployment, and task deployment. The output of each step becomes the input to a matrix tool or table of the next step of the process. CASE STUDY EXAMPLE: In 3 1/2 months a nine-person project team at Continental Rehabilitation Hospital (San Diego) used QFD tools to capture the voice of the customer, use these data as the basis for a questionnaire on important qualities of service from the customer's perspective, obtain competitive data on how the organization was perceived to be meeting the demanded qualities, identify measurable dimensions and targets of these qualities, and incorporate the functions and tasks into the delivery of service which are necessary to meet the demanded qualities. DISCUSSION: The future of providing health care services will belong to organizations that can adapt to a rapidly changing environment and to demands for new products and services that are produced and delivered in new ways.     

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.     

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.     

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.     

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     

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.     

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.     

An industrial workstation characterization and selection using quality function deployment

In flexible manufacturing, the need to evaluate various process design concepts that can survive and deliver better performance through many families and generations of products becomes more urgent. It is important to derive characteristics of the assembly station that are measurable and controllable. These characteristics should satisfy process and products needs. In this paper, we report on the findings of a cross-functional team, representing Product Development and Manufacturing Engineering, that was assigned the design of the advanced assembly station required to load magnetic heads suspended by actuator arms in between a stack of media disks in the assembly of direct access storage devices (DASD) also known as hard disk drives (HDD). The core of our work was based on a Quality Function Deployment (QFD) approach that included the construction of a house of quality (John Hauser and Don Clausing, ‘The house of quality’ Technical Report, Operations Department, Harvard Business School, Boston, MA 02163; Bob King, ‘Better Designs in Half the Time, Implementing Quality Function Deployment in America, 3rd edn, Goal/QPC Publishing, MA, 1989) which treated this process step as a system. We present results of the team efforts and describe the progressive stages that took place from the gathering of the station customers’ wants and needs to the selection of a station design. © 1997 John Wiley & Sons Ltd.     

Intelligent packaging in pursuit of customer needs

Customer needs during the 1990s and beyond will focus upon quality, low cost and increased variety against the background of a faster rate of technological innovation. The intelligent packaging stakeholders will need to balance these drives with the fundamental need to protect consumers and the brands they enjoy from criminal attack. A systematic approach to evaluation, development and implementation of intelligent packaging is required together with a willingness to balance competitive behaviour with industry sector needs.     

Information deduction approach through quality function deployment for the quantification of the dependency between design tasks

The design structure matrix (DSM) has been widely known as an effective approach for the modelling and analysis of a design process for the manufacture of a product from the perspective of information flow. It provides a formal method to capture and manage the interactions and interdependences among design tasks. Nonetheless, the difficulties in constructing a reliable numerical DSM prevent wider applications of DSM and its derived techniques. In this work, an approach to quantify systematically the dependency between design tasks in a DSM is proposed. The proposed approach aims at estimating the coupling strength of tasks in a DSM by making full use of the information contained in quality function deployment matrices. To realize this, a house of quality matrix is first constructed, followed by transferring the information in the house of quality to an extended design deployment matrix, and then the dependence strength of each task–attribute pair in the extended design deployment matrix is analysed. The details of the proposed approach are presented. The performance of the proposed prototype is illustrated by using a case study on a burn-in system. The results obtained from the case study are discussed.     

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.     

Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.     

关于顾客满意调查及评价方法的思考

作为质量管理体系业绩的一种测量,ISO 9001-2008标准第8.2.1条规定组织应对顾客有关组织是否已满足其要求的感受的信息进行监视,同时要求确定获取和利用这种信息的方法。     

Assessing the relative importance weights of customer requirements using multiple preference formats and nonlinear programming

Quality function deployment (QFD) is a methodology to ensure that customer requirements (CRs) are deployed through product planning, part development, process planning and production planning. The first step to implement QFD is to identify CRs and assess their relative importance weights. This paper proposes a nonlinear programming (NLP) approach to assessing the relative importance weights of CRs, which allows customers to express their preferences on the relative importance weights of CRs in their preferred or familiar formats. The proposed NLP approach does not require any transformation of preference formats and thus can avoid information loss or information distortion. Its potential applications in assessing the relative importance weights of CRs in QFD are illustrated with a numerical example.