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Rough set-based approach for modeling relationship measures in product planning

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Abstract

Quality function deployment (QFD) provides a planning and problem-solving methodology that is widely renowned for translating customer requirements (CRs) into engineering characteristics (ECs) for new product development. As the first phase of QFD, product planning house of quality (PPHOQ) plays a very important role in this process. The degrees and directions of the relationship measures between CRs and ECs have serious effects on the special planning of ECs, modeling the relationship measures is an important step in constructing PPHOQ. The current paper presents a rough set (RS)-based approach for modeling relationship measures by determining the knowledge and experience of the QFD team, aided by the introduction of the type factor of a relationship used to express the effects of the relationship types. A study of general cases is used to demonstrate the performances and limitations of the proposed RS-based approach. The results show that the novel approach effectively determines the relative knowledge of the QFD team and facilitates decision-making in new product development.

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... They then proposed a new group decision-making method based on fuzzy preference relation and fuzzy majority to determine the prioritization of ECs. Li et al. [34,35] proposed two rough set-based methods for modeling the correlations and relationships in PPHOQ. ...
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... Rough set theory is developed as a nonparametric data-mining approach that can effectively determine the core relationships amongst a variety of factors [23]. It has been widely used for identifying data dependencies, evaluating the importance of attributes, and seeking the minimum subset of attributes attributes [11,[24][25][26][27]. Utilizing the ability of rough set theory to identify data dependencies, Li et al. [11] identified the correlation measure matrix corresponding to each CR in QFD to design a washing machine. ...
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Preprint
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... In order to better transform CRs into technical characteristics, Zaim et al. [6] propose a hybrid method that integrates analytic network process (ANP)-weighted QFD with fuzzy logic to better rank the technical characteristics of products. Li et al. [7] propose an approach unifying rough set methodology with QFD for modeling relationship measures in the process of product planning. Many variants of CR modeling methodologies have been proposed, such as QFD [8], probability analysis [9,10], and cluster analysis [11]. ...
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In this paper, we introduce the notion of a reference point and provide local approximations for a subset of the universe. The notion of a reference point naturally gives rise to a rough approximations framework, wherein several approximations are possible on the same set. Also, we present an extension to the decision theoretic rough set model by using reference points.
Quality Function Deployment (QFD) has been practiced by leading companies around the world since 1966. Its two-fold purpose is to assure that true customer needs are properly deployed throughout the design, build and delivery of a new product, whether it be assembled, processed, serviced, or even software, and to improve the product development process itself. This paper describes the evolution of the method, its current best practice, and proposals for future direction, not only to log its history and key players correctly, but also to convey the richness and depth of the applications throughout multiple industries.
Article
Quality Function Deployment is a tool for bringing the voice of the customer into the product development process from conceptual design through to manufacturing. It begins with a matrix that links customer desires to product engineering requirements, along with competitive benchmarking information, and further matrices can be used to ultimately link this to design of the manufacturing system. Unlike other methods originally developed in the U.S. and transferred to Japan, the QFD methodology was born out of Total Quality Control (TQC) activities in Japan during the 1960s and has been transferred to companies in the U.S. This article reports on the results of a 1995 survey of more than 400 companies in the U.S. and Japan using QFD. The research questions investigated in this study were developed both inductively from QFD case studies in the U.S. and Japan and deductively from the literature. The reported results are in part counterintuitive. The U.S. companies reported a higher degree of usage, management support, cross-functional involvement, use of QFD driven data sources, and perceived benefits from using QFD. For the most part, the main uses of QFD in the U.S. were restricted to the first matrix (“House of Quality”) that links customer requirements to product engineering requirements and rarely was this carried forward to later matrices. U.S. companies were more apt to use newly collected customer data sources (e.g., focus groups) and methods for analyzing customer requirements. Japanese companies reported using existing product data (e.g., warranty) and a broader set of matrices to a greater extent. The use of analytical techniques in conjunction with QFD (e.g., simulation, design of experiments, regression, mathematical target setting, and analytic hierarchy process) was not wide spread in either country. U.S. companies were more likely to report benefits of QFD in improving cross-functional integration and better decision-making processes compared to Japanese companies. Possible reasons for these cross-national differences as well as their implications are discussed.
Article
Quality function deployment (QFD) provides a systematic methodology to assist companies in developing quality products that are able to satisfy customer needs. The house of quality (HOQ), as the first phase of QFD, plays the most important role in product development. Frequently, fuzzy numbers are used to quantify the vagueness of linguistic terms so as to facilitate subjective assessments in the HOQ. However, the issue concerning how to determine the boundary intervals of fuzzy numbers remains unresolved. This work proposes a novel approach based on rough set theory, and introduces two concepts called rough number and rough boundary interval to address this issue. A comparative case study presented in this work shows that the proposed approach has significant advantages compared to the prevailing fuzzy number based method in processing subjective linguistic assessments in QFD.
Article
The inconsistency of information about objects may be the greatest obstacle to performing inductive learning from examples. Rough sets theory provides a new mathematical tool to deal with uncertainty and vagueness. Based on rough sets theory, this paper proposes a novel approach for the classification and rule induction of inconsistent information systems. It is achieved by integrating rough sets theory with a statistics-based inductive learning algorithm. The framework of a proto-type rough-set-based classification system (RClass) is presented. Two examples are used to verify the prototype system. The results of the validation are discussed.
Article
Product planning is one of four important processes in new product development (NPD) using quality function deployment (QFD), which is a widely used customer-driven approach. In our opinion, the first problem to be solved is how to incorporate both qualitative and quantitative information regarding relationships between customer requirements (CRs) and engineering characteristics (ECs) as well as those among ECs into the problem formulation. Owing to the typical vagueness or imprecision of functional relationships in a product, product planning is becoming more difficult, particularly in a fuzzy environment. In this paper, an asymmetric fuzzy linear regression approach is proposed to estimate the functional relationships for product planning based on QFD. Firstly, by integrating the least-squares regression into fuzzy linear regression, a pair of hybrid linear programming models with asymmetric triangular fuzzy coefficients are developed to estimate the functional relationships for product planning under uncertainties. Secondly, using the basic concept of fuzzy regression, asymmetric triangular fuzzy coefficients are extended to asymmetric trapezoidal fuzzy coefficients, and another pair of hybrid linear programming models with asymmetric trapezoidal fuzzy coefficients is proposed. The main advantage of these hybrid-programming models is to integrate both the property of central tendency in least squares and the possibilistic property in fuzzy regression. Next, the illustrated example shows that trapezoidal fuzzy number coefficients have more flexibility to handle a wider variety of systematic uncertainties and ambiguities that cannot be modeled efficiently using triangular number fuzzy coefficients. Both asymmetric triangular and trapezoidal fuzzy number coefficients can be applicable to a much wider variety of design problems where uncertain, qualitative, and fuzzy relationships are involved than when symmetric triangular fuzzy numbers are used. Finally, future research direction is also discussed.
Article
Quality function deployment (QFD) is a useful analyzing tool in product design and development. To solve the uncertainty or imprecision in QFD, numerous researchers have applied the fuzzy set theory to QFD and developed various fuzzy QFD models. Three issues are investigated by examining their models. First, the extant studies focused on identifying important engineering characteristics and seldom explored the subsequent prototype product selection issue. Secondly, the previous studies usually use fuzzy number algebraic operations to calculate the fuzzy sets in QFD. This approach may cause a great deviation in the result from the correct value. Thirdly, few studies have paid attention to the competitive analysis in QFD. However, it can provide product developers with a large amount of valuable information. Aimed at these three issues, this study integrates fuzzy QFD and the prototype product selection model to develop a product design and selection (PDS) approach. In fuzzy QFD, the α-cut operation is adopted to calculate the fuzzy set of each component. Competitive analysis and the correlations among engineering characteristics are also considered. In prototype product selection, engineering characteristics and the factors involved in product development are considered. A fuzzy multi-criteria decision making (MCDM) approach is proposed to select the best prototype product. A case study is given to illustrate the research steps for the proposed PDS method. The proposed method provides product developers with more useful information and precise analysis results. Thus, the PDS method can serve as a helpful decision-aid tool in product design.
Article
A cost–design parameter method that optimizes cost and design characteristics simultaneously during product development is presented. The method is based on quality function deployment, which relates desired product attributes to design characteristics. The method works at three levels: strategic, tactical and operational. At the strategic level, goals are established for each customer desired, product attribute. At the tactical level, design characteristics are determined using a goal programming technique. Finally, at the operational level, product design characteristics are chosen to improve products while remaining within cost targets. This model is validated through the use of an example, where customer satisfaction versus new expenditure on the product is calculated.
Article
This paper presents a strategic solution to the facility location problem which incorporates both external and internal criteria in the decision-making process. The external components of the model are customers and their wants, competitors, and the characteristics of various locations. The internal components of the model are the critical processes in the manufacturing organization. The framework presented uses quality function deployment (QFD), analytic hierarchy process (AHP) and analytic network process (ANP). QFD matrices with interconnected rows and columns relate market segments, competitive priorities, critical processes, location attributes and various locations. AHP determines the intensity of the relationship between the row and column variables of each matrix. Finally, ANP determines the intensity of synergistic effects among column variables. The model fine-tunes and adds precision to an otherwise qualitative strategic decision process. The applicability of our proposed model is demonstrated with a case study that summarizes an intervention in which the model's framework and basic concepts were applied.
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
Owing to the typical vagueness or imprecision of customer requirements (CRs) in product planning house of quality (PPHOQ), determining the final importance of CRs is very difficult. Combining rough set theory, Kano’s model, analytical hierarchy process (AHP), and scale method, an integrated method is proposed to obtain the final importance of CRs in PPHOQ. Firstly, by using relative reduction and relative core in rough set theory, a decision system is built to acquire CRs in PPHOQ. Secondly, based on relative positive field in rough set, the decision system is simplified and its corresponding new decision system is established to determine the fundamental importance ratings of CRs. Thirdly, by integrating scale method into AHP approach, calculating formulas of the importance rating of achieving the improvement ratio of satisfaction estimation of a CR are developed. Next, for every CR, based on a combination of its fundamental importance rating, the importance rating of achieving the improvement ratio of its satisfaction estimation, and “its sales point”, its final importance rating is determined. Finally, a case study is provided to illustrate the effectiveness of the presented method.
Article
The purpose of this paper is to develop an analytical technique for process selection and evaluation for manufacturing systems. This technique which is based on quality function deployment (QFD), analytic hierarchy process (AHP), and analytic network process (ANP) is used to determine the best process for a new facility. The proposed procedure is a novel prescriptive methodology that is applied to a strategic decision problem involving multiple criteria in manufacturing for which comprehensive and sophisticated decision support tools are lacking. The novelty of the outlined technique lies in the fact that customers’ preferences concerning the product are taken into consideration in making the process choice.
Article
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 aims to maximize customer satisfaction; however, considerations such as cost budget, technical difficulty, etc. 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 fulfillment of design requirements. Linguistic variables are employed to represent the imprecise design information and the importance degree of each design objective. A real-world application illustrates the proposed fuzzy multiple objective decision analysis.
Article
In the stock market, technical analysis is a useful method for predicting stock prices. Although, professional stock analysts and fund managers usually make subjective judgments, based on objective technical indicators, it is difficult for non-professionals to apply this forecasting technique because there are too many complex technical indicators to be considered. Moreover, two drawbacks have been found in many of the past forecasting models: (1) statistical assumptions about variables are required for time series models, such as the autoregressive moving average model (ARMA) and the autoregressive conditional heteroscedasticity (ARCH), to produce forecasting models of mathematical equations, and these are not easily understood by stock investors; and (2) the rules mined from some artificial intelligence (AI) algorithms, such as neural networks (NN), are not easily realized.
Article
Most decisions need to be free from assumptions of independence to be faithful to the complex problems in which they arise. This paper illustrates how to generate priorities for decisions involving general types of dependence of criteria on alternatives, criteria on criteria and alternatives on alternatives. It is based on the feedback system framework of the Analytic Hierarchy Process of which a hierarchy is a special case.
Article
This paper presents a review, analysis, classification and codification of the literature on quality function deployment (QFD) produced between 2002 and 2006. The publications were classified into two main groups: conceptual research and empirical research. The studies focused more on quality matrix problem solving and the main difficulties are reported. However, few studies have been done on solutions for other important aspects. Further research is needed on how to reduce the difficulties of using QFD.
Article
Quality function deployment (QFD) is a methodology for translating customer wants (WHATs) into relevant engineering design requirements (HOWs) and often involves a group of cross-functional team members from marketing, design, quality, finance and production and a group of customers. The QFD team is responsible for assessing the relationships between WHATs and HOWs and the interrelationships between HOWs, and the customers are chosen for assessing the relative importance of each customer want. Each member and customer from different backgrounds often demonstrates significantly different behavior from the others and generates different assessment results, complete and incomplete, precise and imprecise, known and unknown, leading to the QFD with great uncertainty. In this paper, we present an evidential reasoning (ER) based methodology for synthesizing various types of assessment information provided by a group of customers and multiple QFD team members. The proposed ER-based QFD methodology can be used to help the QFD team prioritize design requirements with both customer wants and customers’ preferences taken into account. It is verified and illustrated with a numerical example.
Article
In both the quality improvement and the design of a product, the engineering characteristics affecting product performance are primarily identified and improved to optimize customer needs (CNs). Especially, the limited resources and increased market competition and product complexity require a customer-driven quality management and product development system achieving higher customer satisfaction. Quality function deployment (QFD) is used as a powerful tool for improving product design and quality, and procuring a customer-driven quality system. In this paper, an integrated framework based on fuzzy-QFD and a fuzzy optimization model is proposed to determine the product technical requirements (PTRs) to be considered in designing a product. The coefficients of the objective function are obtained from a fuzzy analytic network process (ANP) approach. Fuzzy analytic hierarchy process (AHP) is also used in the proposed framework. An application in a Turkish Company producing PVC window and door systems is presented to illustrate the proposed framework.
Article
The ability to learn from empirical data or the observation of a real world and accurately predict future instances is an important feature expected in human. It allows knowledge to be gained by experience and decision rules induced from empirical data. One of the major obstacles in performing rule induction from empirical data is the inconsistency of information about a problem domain. Rough set theory provides a novel way of dealing with vagueness and uncertainty. When coupled with genetic algorithms, a rule induction engine that is able to induce probable rules from inconsistent information can possibly be developed. This paper presents an integrated approach that combines rough set theory, genetic algorithms and Boolean algebra, for inductive learning. Using such an approach, a prototype system (RClass-Plus) that discovers rules from inconsistent empirical data, has been developed. The system was validated using the data obtained from a case study. The results of the validation are presented.
Article
Quality function deployment (QFD) is a customer-oriented design tool with cross-functional team members reaching a consensus in developing a new or improved product to increase customer satisfaction. QFD starts with the house of quality (HOQ), which is a planning matrix translating the customer needs into measurable product technical requirements (PTRs). A robust evaluation method should consider the interrelationships among customer needs and PTRs while determining the importance levels of PTRs in the HOQ. This paper employs the analytic network process (ANP) to fulfill this requirement. Furthermore, the proposed analytic procedure should take into account the multi-objective nature of the problem, and thus, incorporate other goals such as cost, extendibility and manufacturability of PTRs. This paper presents a zero–one goal programming methodology that includes importance levels of PTRs derived using the ANP, cost budget, extendibility level and manufacturability level goals to determine the PTRs to be considered in designing the product. A numerical example is presented to illustrate the application of the decision approach.
Article
Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In addition, many international workshops and conferences have included special sessions on the theory and applications of rough sets in their programs. Rough set theory has led to many interesting applications and extensions. It seems that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, knowledge discovery, decision analysis, and expert systems. In the article, we present the basic concepts of rough set theory and point out some rough set-based research directions and applications.
Article
The prioritization of engineering characteristics (ECs) provides an important basis for decision-making in QFD. However, the prioritization results in the conventional QFD may be misleading since it does not consider the uncertainty of input information. This paper develops two robustness indices and proposes the notion of robust prioritization that ensures the EC prioritization to be robust against the uncertainty. The robustness indices consider robustness from two perspectives, namely, the absolute ranking of ECs and the priority relationship among ECs. Based on the two indices, robust prioritization seeks to identify a set of ECs or a priority relationship among ECs in such a way that the result of robust prioritization is stable despite the uncertainty. Finally, the proposed robustness indices and robust prioritization are demonstrated in a case study conducted on the ADSL-based high-speed internet service.
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