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Abstract

Quality function deployment (QFD) is a widely adopted customer-oriented methodology to assist in product design and development. In a traditional QFD-based product planning process, the importance weights of customer requirements (CRs) in house of quality are obtained by analyzing and generalizing the requirements of various customers, and the purpose of the subsequent optimal setting of engineering characteristics (ECs) and process parameters is to achieve a higher level of overall customer satisfaction (OCS). However, nowadays customers' requirements are increasingly diversified and customers usually have heterogeneous preferences. Consequently, customers in a product market may have different purchasing choice behaviors and satisfaction criteria toward a new product. Therefore, heterogeneity of CRs should be considered in QFD-based optimization models to describe the relationship between CRs and ECs and to model the OCS. In this paper, a novel QFD-based product planning approach is proposed for a product market with diversified CRs by integrating consumer choice behavior analysis. The contributions of this paper include: 1) customers' purchase choice rules are introduced into QFD-based product planning process to simulate the purchase behavior of a customer toward a product; 2) two new QFD-based optimization models under deterministic and multinomial logit consumer choice rules are proposed to help firms improve the product quality under environment of diversified CRs; 3) the established model under deterministic consumer choice rule is transformed into an equivalent linear model to facilitate the solving process; and 4) the established optimization models are further extended for products with both continuous and discrete target values of ECs.

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... It provides a systematic way to catalogue the perceived needs of the customer and to translate them into design specifications, all over product planning, product design, process design, and production planning [11]. The key element of QFD is a combined chart of HoQ to map the CRs (the 'WHATs') into ECs (the 'HOWs') that must be adjusted to fulfill the customer needs in product planning stage, and subsequently into parts characteristics, process plans, and manufacture operations [12]. In general, QFD-based product planning contains four steps: (1) identify CRs; (2) conduct competitive analysis; (3) determine final importance ratings of CRs; (4) map CRs into ECs. ...
Conference Paper
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In today‟s competitive market, OKP companies operate in the “engineer-to-order” business mode, whereby analysing the “voice of customer” promptly and accurately in the early design stage determines the success of product development. However, OKP companies have limited resources. They may not be able to afford the cost of the com-plicated Quality Function Deployment (QFD) product planning process, nor can they obtain abundant CRs information effectively in traditional internet-based environment. This paper proposes a QFD-based approach in the cloud manufacturing (CMfg) environment to enhance OKP com-panies‟ product planning process. CMfg (a newly emerged manufac-turing paradigm) utilizes advanced information technologies and busi-ness mode, which may provide sufficient and cost-effective resources to OKP companies. The interaction process among different cloud service roles is introduced in detail, which contains six main parts: pre-process, identify CRs, competitive marketing analysis, determine final im-portance ratings, mapping CRs to engineering characteristics (ECs), and customer-centric decision making.
... This paper investigates well-established tools typically used in the consumer products industry to focus on pertinent customer requirements (CR) and link them with relevant engineering characteristics (EC). A commonly used tool to facilitate this is quality function deployment (QFD) [3]. To develop a comprehensive early stage design environment with user guidance and decision support capabilities, it is vital that expert knowledge be captured and represented to validate designdecisions. ...
... In order to provide decision-making in QFD, the systematic and rational mathematical programming models for the targets setting of ECs have received flourishing advances in the last decade ( Delice & Güngör, 2009;Luo et al., 2010;Yang & Yoo, 2016;Zhong et al., 2014). In Consideration of CRs' heterogeneity, a novel QFD-based product planning approach for determining optimal target levels of ECs is proposed ( Luo et al., 2015). These models can help a company to make key tradeoff between what the customers want and what the company can afford to build with QFD analysis. ...
Article
In an effort to address the inherent deficiencies of traditional Kano model and quality function deployment (QFD), this paper proposes an improved Kano model named as importance-frequency Kano (IF-Kano) model and integrates IF-Kano model into QFD. Considering the interaction between frequencies and importance weights of customer requirements (CRs), the IF-Kano model adopts the logical Kano classification criteria to categorize CRs. Then, both qualitative and quantitative results derived from IF-Kano model are integrated into QFD with a non-linear programming model. The model aims to determine appropriate Kano categories of CRs and target values of engineering characteristics (ECs) with a view to achieving an optimal design solution under the best balance between enterprise satisfaction and customer satisfaction (CS). To solve the presented model, a multi-population adaptive genetic algorithm (MPAGA) is designed. Finally, an example of a home elevator design is given to demonstrate the feasibility and effectiveness of the developed approach and algorithm.
... 1. Quality function deployment, particularly the House of Quality (HOQ) [6]. 2. Robust design using reduced factorial Taguchi method (TM) [7]. ...
Conference Paper
A conscious effort is underway to explore the paradigm of Set-Based Design (SBD) for development of next generation US Navy ships. The Electric Ships Research and Development Consortium (ESRDC) funded through the Office of Naval Research (ONR) is responsible for developing a state-of-the-art design environment namely, Smart Ships Systems Design (S3D) wherein one focus area is to incorporate SBD functionalities. Impetus and efforts to develop SBD enablers, to be used within a concurrent and collaborative environment like S3D are in its infancy. The first step, is to explore viable well-established tools that are most suitable to perform the fundamental SBD task of feasible-design space reduction subject to user driven requirements and constraints. Once potential tools have been identified, the next step is to investigate their suitability for integration with S3D, with further studies into extent of necessity of modifications. This paper illustrates the use of full-factorial design analysis as one potential tool to facilitate SBD and discusses relevant aspects and future work. The benchmark medium voltage DC (MVDC) equipment considered for this study is the modular multilevel converter (MMC). Index Terms-Design methodology, full factorial design, set reduction tools, SBD, S3D.
... QFD has proven to have many advantages ever since its first application, such as: improved customer satisfaction, reduced product development cost, shortened time-to-market, and enhanced multi-disciplined teamwork in the product development process [54]. The key element of QFD is a combined chart, which is called the 'house of quality' (HoQ), to map the CRs (the 'WHATs') into corresponding adjusted engineering characteristics (the 'HOWs'), that fulfil the CRs in the product planning stage, and subsequently into parts characteristics, process plans, and manufacturing operations [45,55]. Since CR information processing is critical, in the literature, the major concerns of QFD are to determine the final importance rating of CRs and their mapping to the ECs at the product planning stage [56,57]. ...
... As another key issue of QFD, the prioritization of DRs have been extensively researched and various methods have been suggested in the QFD literature. For example, Luo et al. [34] proposed a new QFD-based product planning approach to determine the optimal target levels of DRs for a product market with heterogeneous CRs by integrating consumer choice behavior analysis. Zhong et al. [35] constructed a fuzzy chance-constrained programming model to determine the target values of DRs in QFD and designed a hybrid intelligent algorithm by integrating fuzzy simulation and genetic algorithm to solve the proposed model. ...
Article
Quality function deployment (QFD) is a widely used quality system tool for translating customer requirements (CRs) into the engineering design requirements (DRs) of products or services. The conventional QFD analysis, however, has been criticized as having some limitations such as in the assessment of relationships between CRs and DRs, the determination of CR weights and the prioritization of DRs. This paper aims to develop a new hybrid group decision-making model based on hesitant 2-tuple linguistic term sets and an extended QUALIFLEX (qualitative flexible multiple criteria method) approach for handling QFD problems with incomplete weight information. First, hesitant linguistic term sets are combined with interval 2-tuple linguistic variables to express various uncertainties in the assessment information of QFD team members. Borrowing the idea of grey relational analysis (GRA), a multiple objective optimization model is constructed to determine the relative weights of CRs. Then, an extended QUALIFLEX approach with an inclusion comparison method is suggested to determine the ranking of the DRs identified in QFD. Finally, an analysis of a market segment selection problem is conducted to demonstrate and validate the proposed QFD approach.
... In the last few years, scholars have had differing opinions on the issues of requirements transformation and dynamic requirements uncertainty. Luo et al. [1] propose a product design approach based on QFD for determining the optimal target levels of engineering characteristics with reference to CRs. Sheng et al. [2] propose a method based on the House of Quality for mapping from CRs to technical weights and target values. ...
... Currently, manufacturing industries are challenged with meeting diverse and uncertain CRs in short product life cycles with a variety of product functions. On the one hand, customers in the same market may have heterogeneous preferences for the product, which leads to the different purchasing choice behaviors toward a new product (Luo et al. 2015). On the other hand, for a product, some of the functional requirements remain unknown until the later stages of product development, which is attributed to the market dynamic adjustment associated with the continuous changes in customer expectations (La Rocca et al. 2016). ...
Article
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Timely identification of heterogeneous customer requirements serves as a vital step for a company to formulate product strategies to meet the diverse and changing needs of its customers. By relaxing the search for global patterns in classical clustering, we propose a biclustering-based method, BiHCR, to identify heterogeneous customer requirements from the perspective of local patterns detection. Specifically, conforming to customers’ attitudes toward products derived from customer participation, we first transform the original data matrix with customers as rows and customer requirements as columns into a binary matrix. Then, by combining the two significant biclustering algorithms, Bimax and RepBimax, we design BiHCR to identify the biclusters embedded in the binary matrix to improve the detection results from the larger biclusters and their overlaps. Furthermore, the empirical case of smartphone development in a Chinese company verifies that BiHCR can identify homogeneous subgroups of customers with similar requirements without redundant noise compared with Bimax. Additionally, in contrast to RepBimax, our proposed BiHCR can also detect the intractable overlapping biclusters in the binary matrix used to describe the heterogeneity of customer requirements. Since the process of customer participation in product development gradually became a dominant approach to collecting customer requirements information for many industries, a conceptual framework of customer requirements identification is constructed and the detailed steps are clarified for manufacturers.
... QFD has proven to have many advantages ever since its first application, such as: improve customer satisfaction, reduce product development cost, shorten the time-to-market, and enhance the multidisciplined teamwork in the product development process (Cohen and Cohen 1995). The key element of QFD is a combined chart which is called the house of quality (HoQ) to map the CRs (the 'WHATs') into corresponding adjusted engineering characteristics (the 'HOWs') that fulfil the CRs in product planning stage, and subsequently into parts characteristics, process plans, and manufacture operations (Luo et al. 2015;Zheng et al. 2015). The major issue of QFD product planning is to determine the final importance ratings of CRs (Li et al. 2012), as its accuracy will largely affect the product success. ...
Article
Full-text available
Customer requirements (CRs) play a significant role in the product development process, especially in the early design stage. Quality function deployment (QFD), as a useful tool in customer-oriented product development, provides a systematic approach towards satisfying CRs. Customers are heterogeneous and their requirements are often vague, therefore, how to determine the relative importance ratings (RIRs) of CRs and eventually evaluate the final importance ratings is a critical step in the QFD product planning process. Aiming to improve the existing approaches by interpreting various CR preferences more objectively and accurately, this paper proposes a weighted interval rough number method. CRs are rated with interval numbers, rather than a crisp number, which is more flexible to adapt in real life; also, the fusion of customer heterogeneity is addressed by assigning different weights to customers based on several factors. The consistency of RIRs is maintained by the proposed procedures with design rules. A comparative study among fuzzy weighted average method, rough number method and the proposed method is conducted at last. The result shows that the proposed method is more suitable in determining the RIRs of CRs with vague information.
... However, with time, the customers' needs have diversified due to their varied preferences. Luo et al. [26] proposed a QDF-based product planning approach that considers the heterogeneity of CRs by implementing consumer choice behavior analysis. The author introduced customers' purchase choice rules in the QDF-based product planning method, proposed two new QDF-based optimization models under deterministic and multinomial logit consumer choice rules, and extended these models for both continuous and discrete target values. ...
Article
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Customization of products or services is a strategy that the business sector has embraced to build a better relationship with the customers to cater to their individual needs and thus providing them a fulfilling experience. This whole process is known as customer relationship management (CRM). In this context, we extensively surveyed 138 papers published between 1996 and 2021 in the area of analytical CRM. Although this study consisted of papers from different business sectors, a fair share of focus was directed to the telecommunication industry and generalized CRM techniques usages. Different science and engineering-based data repositories were studied to ascertain significant studies published in scientific journals, conferences, and articles. The research works on CRM were considered and separated into IT and non-IT-based techniques to study the methods used in different business sectors. The main target behind implementing CRM is for the better revenue growth of the company. Different IT and non-IT-based techniques are used in the analytical CRM area to achieve this target, and researchers have been actively involved in this domain. The purpose of the research was to show the impact of IT-based techniques in the business world. A detailed future course of research in this area was discussed.
... is the value of a single RPN, so it changes from 1 to 1000 (in the case when the 1 to 10 scale is chosen during evaluation). So, it is necessary to find the relation between the corrective action cost value and risk reduction value (Luo et al., 2015). ...
Article
FMEA is a very popular and effective analysis. The main advantage is the arrangemnet of expert groups, which define risks, their effects and organize corrective and preventive actions. But such analysis also has some disadvantage, first of all it is the uncertainties, the other one is the need to choose the corrective event among those that have been suggested. Besides, a typical model for assessing the risks of potential failures of the coating applied by the method of gas-thermal plasma spraying on the blades of a gas turbine of a gas turbine engine has been developed. The model is based on the Design Failure Mode and Effect Analysis. The structural and functional analysis of the coating design was carried out. The failures resulting from the failure of the coating to perform the function are determined. The potential causes and consequences of failures have been identified. An assessment of the risks of failures was carried out and the priority of actions for their elimination was established. Measures to improve the quality of the coating applied by the method of gas-thermal plasma spraying are described.
... • Product market share: Assessing and evaluating the impact of changes of the provisioned software on its market share, particularly through gap analysis between potential and actual market share, competitor analysis and value-estimation of new features [67,170]. Similar to ecosystem valuation, this theme requires quantification of external factors e.g., through mining textual product reviews, designing novel features, and weighting them according to estimated economic value added (EVA) and return on invest (ROI) [168,169]. In DevOps, external factors such as e.g., the results from A/B testing, must then be considered in conjunction with internal factors such as e.g., business performance indicators, to decide upon concrete software engineering activities [136]. ...
Preprint
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Software non-functional requirements address a multitude of objectives, expectations, and even liabilities that must be considered during development and operation. Typically, these non-functional requirements originate from different domains and their concrete scope, notion, and demarcation to functional requirements is often ambiguous. In this study we seek to categorize and analyze relevant work related to software engineering in a DevOps context in order to clarify the different focus areas, themes, and objectives underlying non-functional requirements and also to identify future research directions in this field. We conducted a systematic mapping study, including 142 selected primary studies, extracted the focus areas, and synthesized the themes and objectives of the described NFRs. In order to examine non-engineering-focused studies related to non-functional requirements in DevOps, we conducted a backward snowballing step and additionally included 17 primary studies. Our analysis revealed 7 recurrent focus areas and 41 themes that characterize NFRs in DevOps, along with typical objectives for these themes. Overall, the focus areas and themes of NFRs in DevOps are very diverse and reflect the different perspectives required to align software engineering with technical quality, business, compliance, and organizational considerations. The lack of methodological support for specifying, measuring, and evaluating fulfillment of these NFRs in DevOps-driven projects offers ample opportunities for future research in this field. Particularly, there is a need for empirically validated approaches for operationalizing non-engineering-focused objectives of software.
... • Product market share: Assessing and evaluating the impact of changes of the provisioned software on its market share, particularly through gap analysis between potential and actual market share, competitor analysis and value-estimation of new features [67,170]. Similar to ecosystem valuation, this theme requires quantification of external factors e.g., through mining textual product reviews, designing novel features, and weighting them according to estimated economic value added (EVA) and return on invest (ROI) [168,169]. In DevOps, external factors such as e.g., the results from A/B testing, must then be considered in conjunction with internal factors such as e.g., business performance indicators, to decide upon concrete software engineering activities [136]. ...
Technical Report
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Context: Software non-functional requirements address a multitude of objectives, expectations, and even liabilities that must be considered during development and operation. Typically, these non-functional requirements originate from different domains and their concrete scope, notion, and demarcation to functional requirements is often ambiguous. Objective: In this study we seek to categorize and analyze relevant work related to software engineering in a DevOps context in order to clarify the different focus areas, themes, and objectives underlying non-functional requirements and also to identify future research directions in this field. Method: We conducted a systematic mapping study, including 142 selected primary studies, extracted the focus areas, and synthesized the themes and objectives of the described NFRs. In order to examine non-engineering-focused studies related to non-functional requirements in DevOps, we conducted a backward snowballing step and additionally included 17 primary studies. Results: Our analysis revealed 7 recurrent focus areas and 41 themes that characterize NFRs in DevOps, along with typical objectives for these themes. Overall, the focus areas and themes of NFRs in DevOps are very diverse and reflect the different perspectives required to align software engineering with technical quality, business, compliance, and organizational considerations. Conclusion: The lack of methodological support for specifying, measuring, and evaluating fulfillment of these NFRs in DevOps-driven projects offers ample opportunities for future research in this field. Particularly, there is a need for empirically validated approaches for operationalizing non-engineering-focused objectives of software. Remark This paper is an addition to our peer-reviewed publication in [1] and contains a more elaborate presentation of our findings. When citing our work, please always cite the peer-reviewed publication; citing this technical report should always be optional for cases where you explicitly reference aspects that were not published in the peer-reviewed publication.
... One of the potentially useful systematic methods for making these links between customer requirements (CRs) and products/services is quality function deployment (QFD). As a customer-oriented tool, QFD is widely applied to design and improvement of products and services, and it is an efficient method for translating requirements from customers and the environment into various phases of product planning, part deployment, process planning and production planning (Akao and Mazur 2003;Luo, Kwong, and Sun 2015). QFD can enable enterprises to increase customer satisfaction and market share, reduce cost, shorten cycle time and enhance quality in the design and improvement of the product and service (Cristiano, Liker, and White 2000;Kim et al. 2000;Carnevalli and Miguel 2008;Min and Kim 2008;Chen and Ko 2011;Jin et al. 2016). ...
Article
Full-text available
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.
Article
Quality function deployment (QFD) is a widely adopted customer-oriented product development methodology by analyzing customer requirements. It is a main activity in QFD planning process to determine the optimal values of the technical attributes (TAs) so as to achieve the customer requirements (CRs) from the House of Quality (HoQ). In most of the previous research, all the TAs in QFD are assumed to have either continuous or discrete values. In the real world applications, the continuous TAs and the discrete TAs are often mixed in QFD. In this paper, a mixed integer linear programming model is formulated to obtain the optimal values for the continuous TAs and the discrete TAs in QFD planning as well as Branch and Bound (B and B) algorithm is proposed as the solution approach. Finally, the proposed model and solution approach are illustrated with an office chair under multi-segment market, and the sensitivity analysis is performed to study how the proposed model and its solutions respond to the variation for the two elements which are budget and CRs` weights.
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The complexity of construction processes often means interaction between various stakeholders, activities and tasks in order to deliver the expected outcomes. The intensity and dynamics of front-end design (FED) mean decision techniques and methods are important in supporting projects benefits delivery more importantly those based on utility of decision making. This paper explores a new utilitarian decision-making approach based on a systematic literature review of FED decision making. It presents the state of the art in design decision making concepts and analysis of tools over the last 10 years (2009-2019). From a total of 111 peer-reviewed journal papers, fifteen decision-making techniques are identified as dominant in design decision making, broadly grouped in four major categories as explanatory/rational, Multi-criteria decision-Making techniques (MCDM), Hybrid and Visual methods. The review finds that the most applied of the MCDM is Quality Function Deployment (QFD); while among the rational/explanatory techniques is set-based design (SBD). While there is limited application of Multi Attribute Utility Theory (MAUT) in decision making, the paper finds that the robust consistency and structured approach better captures the intricate dynamics of FED; including modelling of the subjectivity, interdependencies and uncertainty in design discourse.
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Chapter
Quality Function Deployment (QFD) is a methodology for transforming customers' wishes into quality requirements for a product, service or process. QFD methodology was originally developed by Japanese researchers who designed the approach for transforming customers' wishes (real or supposed) into detailed product characteristics using special matrices. QFD methodology provides better understanding of customers' expectations in the process of design and development of products, services and processes and helps to consider real or supposed customers' requirements. House of Quality is used to show the relationship between customers' requirements and product characteristics. Product characteristics are realized using appropriate technological operations and equipment. If we know the methods for quality assessment of a separate operation (Cp indices, control charts etc.), we can complete the House of Quality with the results of technological equipment analysis. Such data integration allows complex solution of a problem of product competitiveness improvement. Using quality assessment methods for technological equipment, we acquire knowledge about defect probability at each separate production stage. Quality Function Deployment and integration of mentioned results (amount of defects, process stability) allow approaching assessment of each product characteristic with regard to this importance for a customer as well as with regard to technical possibility to implement it.
Chapter
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Quality Function Deployment (QFD) is a methodology for transforming customers’ wishes into quality requirements for a product, service or process. QFD methodology was originally developed by Japanese researchers, who designed the approach for transforming customers’ wishes (real or supposed) into detailed product characteristics using special matrices. QFD methodology provides better understanding of customers’ expectations in the process of design and development of products, services, and processes and helps to consider real or supposed customers’ requirements. House of Quality is used to show the relationship between customers’ requirements and product characteristics. Product characteristics are realized using appropriate technological operations and equipment. If we know the methods for quality assessment of a separate operation (Cp indices, control charts, etc.), we can complete the House of Quality with the results of technological equipment analysis. Such data integration allows the complex solution of a problem of product competitiveness improvement. Using quality assessment methods for technological equipment, we acquire knowledge about defect probability at each separate production stage. Quality Function Deployment and integration of mentioned results (amount of defects, process stability) allow approaching assessment of each product characteristic with regard to its importance for a customer as well as with regard to the technical possibility to implement it.
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Quality function deployment (QFD) is an important tool in product planning that could contribute to increase in customer satisfaction and shorten product design and development time. During the QFD process, determination of the importance weights of customer requirements is a crucial and essential step. The analytic hierarchy process (AHP) has been used in weighting the importance. However, due to the vagueness and uncertainty existing in the importance attributed to judgement of customer requirements, the crisp pairwise comparison in the conventional AHP seems to be insufficient and imprecise to capture the degree of importance of customer requirements. In this paper, fuzzy number is introduced in the pairwise comparison of AHP. An AHP based on fuzzy scales is proposed to determine the importance weights of customer requirements. The new approach can improve the imprecise ranking of customer requirements which is based on the conventional AHP. Finally, an example of bicycle splashguard design is used to illustrate the proposed approach.
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Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics. This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data. Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis. Key Features: textbullet} Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis. textbullet{ Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies textbullet Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data. Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.
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Aligning its quality initiatives in synchronization with the customer's perception of values is one of the key management strategies for improving the competitive edge of an organization. Therefore, it will be a distinct advantage if one can succeed in effectively capturing the genuine and major customer attributes (requirements), systematically analysing and duly transforming them into the appropriate product attributes (features). This paper puts forward a novel approach for analysing customer attributes and projecting them into the relevant design, engineering and product attributes in order to facilitate decision-making and to guide downstream manufacturing planning and control activities. The proposed hybrid system incorporates the principles of quality function deployment, analytic hierarchy process and fuzzy set theory to tackle the complex and often imprecise problem domain encountered in customer requirement management. It offers an analytical and intelligent tool for decoding, prioritizing and inferring the qualitative, sometimes vague and imprecise Voice of Customer. As a result, the appropriate product attributes can be mapped out and their relevant design targets can be determined quantitatively and consistently. The software supporting the hybrid system is constructed within a generic framework which can be easily customized and configured into specific enterprise models capable of offering more timely responses to the dynamic market demand.
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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.
Article
A decision model for the prioritization of design requirements during the Quality Function Deployment (QFD) planning process is introduced. The concept of deployment normalization as advocated by Lyman [8] is extended to properly account for dependencies which may exist between design requirements. A mathematical justification for prioritizing design resources by ranking the index of technical importance to cost, as first proposed by Hales et al. [5], is provided.
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This paper reviews recent research in marketing on product positioning and product design. Although the literature generally treats these two decisions independently, we propose a framework that integrates them into a single decision. Consumers make choices in the marketplace on the basis of perceptual product attributes that can be influenced by various factors under the firm's control such as product design and marketing mix of the product. This review suggests that a firm should optimize its goals with respect to product attributes and then translate these attributes into product characteristics and levels of marketing mix variables. The proposed framework is used to integrate the present literature in marketing and to suggest how the two problems should be approached. We also suggest directions for future research.
Article
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.
Article
The House of Quality has been widely discussed as a mechanism for converting customer attributes into engineering characteristics to ensure the design quality of new products and processes. In the past, this process has been subjective and heuristic. In this paper, we present a mathematical programming model for determining the optimal settings for engineering characteristics based on value functions constructed to capture customer preferences. The model can be used with either traditional subjective measures of customer preference or incorporate empirical models based on quantitative data. The robustness of the optimal solution to randomness in parameter estimates is investigated. An example is used to demonstrate the procedure.
Article
Product line design is a key decision area that a product development team has to deal with in the early stages of product development. Previous studies of product line design have focused on single-objective optimization. However, several optimization objectives may be simultaneously pursued, and the solutions that can address the objectives are required in many practical scenarios. In this research, we propose a one-step multiobjective optimization approach for product line design. The proposed optimization model has three objectives: 1) maximizing the market share of a company's products; 2) minimizing the total product development cost of a product line; and 3) minimizing the total product development cycle time. A curve-fitting method is introduced into the part-worth utility models so that the optimization model can be applied to products with level-based attributes and attributes that have continuous values. A multiobjective genetic algorithm is adopted to solve the optimization model, obtaining a set of nondominated solutions. With the solutions, a new product development team can select a preferred solution interactively in a 2-D graph. An example of the optimal design of a product line of digital cameras is used to illustrate the proposed approach.
Article
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.
Article
Customers often have various requirements and preferences on a product. A product market can be partitioned into several market segments, each of which contains a number of customers with homogeneous preferences. In this paper, a methodology which mainly involves a market survey, fuzzy clustering, quality function deployment (QFD) and fuzzy optimization, is proposed to achieve the optimal target settings of engineering characteristics (ECs) of a new product under a multi-segment market. An integrated optimization model for partitioned market segments based on QFD technology is established to maximize the overall customer satisfaction (OCS) for the market considering the weights of importance of different segments. The weights of importance of market segments and development costs in the model are expressed as triangular fuzzy numbers in order to describe the imprecision caused by human subjective judgement. The solving approach for the fuzzy optimization model is provided. Finally, a case study is provided for illustrating the proposed methodology.
Article
In design and quality improvement, the engineering characteristics that affect product performance are identified and improved to maximize customer satisfaction. This is usually done empirically in conventional implementation of quality function deployment. The limited resource and increased market competition and product complexity require more accurate and optimal solutions. A new approach is proposed to address the difficulties due to the uncertainty of data and lack of quantitative tools. It prioritizes engineering characteristics through a fuzzy ranking procedure and optimizes the improvements using a mixed integer program. Numerical experiments are designed to verify the models and investigate computational efficiency.
Article
A novice-friendly decision support system prototype for quality function deployment (QFD) called QFD Optimizer is developed based upon an integrated mathematical programming formulation and solution approach. QFD Optimizer not only helps a design team build a house of quality chart, but also supports them in understanding and analyzing the system interrelationships, as well as obtaining optimal target engineering characteristic values. QFD Optimizer was tested experimentally and in a real design setting on students and practitioners to ascertain its potential viability and effectiveness. The results suggest that it has the potential to help users find improved feasible designs yielding higher customer satisfaction (i.e., improving quality of design) more rapidly (i.e., reduce the design cycle time), compared with the current manual, ad hoc approach. QFD Optimizer can be used by novice as well as expert users, and leads to a better understanding of complex interrelationships between customer needs and the engineering characteristics and among the engineering characteristics. Hence, it can and has been used as an effective quality improvement training tool, and shows promise for application in practice. © 1997 Elsevier Science Ltd
Article
An integrated formulation and solution approach to Quality Function Deployment (QFD) is presented. Various models are developed by defining the major model components (namely, system parameters, objectives, and constraints) in a crisp or fuzzy way using multiattribute value theory combined with fuzzy regression and fuzzy optimization theory. The proposed approach would allow a design team to reconcile tradeoffs among the various performance characteristics representing customer satisfaction as well as the inherent fuzziness in the system. In addition, the modeling approach presented makes it possible to assess separately the effects of possibility and flexibility inherent or permitted in the design process on the overall design. Knowledge of the impact of the possibility and flexibility on customer satisfaction can also serve as a guideline for acquiring additional information to reduce fuzziness in the system parameters as well as determine how much flexibility is warranted or possible to improve a design. The proposed modeling approach would be applicable to a wide spectrum of design problems where multiple design criteria and functional design relationships are interacting and/or conflicting in an uncertain, qualitative, and fuzzy way.