A fuzzy robust evaluation model for selecting and ranking NPD projects using Bayesian belief network and weight-restricted DEA

Chiang, Tzu-An; H., Z.

doi:10.1016/j.eswa.2010.04.034

Cited by 61 publications

(29 citation statements)

References 22 publications

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“…Then, in aggregating the values for several criteria, previous studies have adopted a multi-criteria decision-making (MCDM) model, which helps a decision-maker to choose the most desirable options (R&D projects in our case) based on conflicting criteria. The MCDM models frequently applied include AHP [3], analytic network process (ANP) [24], the technique for order of preference by similarity to ideal solution (TOPSIS) [22], data envelopment analysis (DEA) [25], fuzzy-set theory [4,5], and decision-making trial and evaluation laboratory (DEMATEL) [26]. Sometimes, these techniques are integrated to better support decision-making (e.g., [27,28]).…”

Section: Randd Project Evaluationmentioning

confidence: 99%

“…According to Cooper et al (2000) [2], companies that use formal project selection approaches show greater project launch success as well as better sales and profit performance than others. As a result, extensive academic research has been conducted to help organizations make better decisions in R&D project selection, and a number of decision models and methods, such as analytic hierarchical process (AHP)-based modes [3], fuzzy evaluation processes [4,5], and portfolio-based methods [6,7], have been developed in the past four decades. However, current research findings indicate that many such complex models and methods are not being used, having only limited effects on decision-making for real-world project selection [8,9].…”

Section: Introductionmentioning

confidence: 99%

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R&D Project Selection Incorporating Customer-Perceived Value and Technology Potential: The Case of the Automobile Industry

et al. 2017

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Abstract:As user-centric innovation has recently emerged as a successful way of developing new products, services, and concepts, it is worth considering the perspectives of potential technology users during R&D project selection processes. Nevertheless, little effort has been made to reflect customer-perceived value in establishing selection criteria, with the focus mainly on technological potential instead. Therefore, this study aims to develop an R&D project selection model incorporating not only technological potential but also customer-perceived value. For this purpose, a new R&D project evaluation model and process is proposed, and its feasibility is tested by potential users in a real scenario. The automobile industry is suitable for our evaluation model because it is a B2C and system-based industry where customer needs are critical to market success and a number of R&D projects are proposed every year. Finally, a supporting tool is developed to help interact with various evaluators and visualize the evaluation results, as customer involvement is recommended for accurate project evaluation from the perspective of technology users. This study is one of the earliest attempts to reflect customer-perceived value in R&D project selection, and practically, the research outputs are expected to be useful to automobile manufacturers in creating value from R&D projects.

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Section: Randd Project Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

R&D Project Selection Incorporating Customer-Perceived Value and Technology Potential: The Case of the Automobile Industry

et al. 2017

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“…This paper provide a weight-restricted DEA model, with more subjective factors of decision ideology and value judgement. Evaluation results can be more approximates the real facts by adding the appropriate weights constraint conditions [6]. The rest of this article is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Research on The Efficiency of Commercial Bank Branches Based on Weight-restricted DEA Model

Jia¹,

Li²,

Wang³

et al. 2017

Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)

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Financial industry is facing unprecedented challenges, especially fierce competition between commercial banks, so it is urgent for us to solve a problem of comprehensive analysis and appropriate suggestions for the development of commercial banks. This survey uses a commercial bank's input and output data, provides an overall efficiency ranking of bank branches by calculating through the traditional DEA model(C 2 R), DEA Overlapping Efficiency Model and Weight-restricted DEA model. The experimental results provide the basis for understanding of each branch, which is affiliated to the commercial bank. Keywords:Bank efficiency, DEA model, Input-output efficiency. 1.IntroductionCommercial banks can assess their strengths and weakness by implementing comprehensive performance evaluation of management. To promote commercial banks' competitiveness, analysis of experimental results will provide insights into assigning the limited financial resources scientifically and rationally to decision-maker. As recognized by all economists in the western theory of economics, both the definition and description of bank efficiency are the contrasting relationship between investment and yield in their business activities. With the deepening of reform and opening up, the financial industry is facing unprecedented challenges, especially between interbank market. The question of how to achieve optimal financial resources for a single unit within the bank is a problem that needs to be addressed. Twenty years after Farrell's seminal work,and building on those ideas, Charnes et al. (1978),proposed data envelopment analysis(DEA) [1]. DEA(data envelopment analysis) Model is a commonly used method for evaluating company's operating efficiency [2]. However, the fact is that each decision unit is maximizing its efficiency rating index [3], which tends to use extreme and unreasonable weight distribution for the input and output indices, the traditional DEA method's result is somewhat unsatisfactory and cannot identify strengths and weaknesses between all the DMU. DEA Overlapping Efficiency Model partially compensates for these shortcomings of the traditional DEA method [4]. Traditional DEA model and DEA Overlapping Efficiency Model are too objective for the efficiency evaluation of commercial banks [5]. This paper provide a weight-restricted DEA model, with more subjective factors of decision ideology and value judgement. Evaluation results can be more approximates the real facts by adding the appropriate weights constraint conditions [6]. The rest of this article is organized as follows. Section 2 describes the experimental dataset of a commercial bank. Section 3 explains the models for bank efficiency and how to calculate the efficiency values of bank branches. In section 4, we show the ranking list, compare our approach results and discuss the possible causes for the ranking results. Finally, we give concluding remarks in Section 5. 2.DatasetsWe used seventeen sub-branches' input-output data of a commercial bank , input data includ...

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“…When a new product is being developed, it is not normally possible to elicit explicit data because of the implicit nature of early-stage product conceptualisation (Yan et al 2006). Since Zadeh (1965) introduced fuzzy set theory, and Bellman and Zadeh (1970) described the decision-making method in fuzzy environments, an increasing number of manufacturing studies have dealt with fuzzy-logic-based decisionmaking models for new product development (Bu¨yu¨-ko¨zkan and Feyzio glu 2004, Mikhailov and Tsvetinov 2004, Feyzio glu and Bu¨yu¨ko¨zkan 2008, Zhang and Chu 2009, Chiang and Che 2010 and cooperative games where the knowledge about the worth of coalitions is described by fuzzy intervals (Nishizaki and Sakawa 2000, Mares 2001, Tsurumi et al 2001, Espin et al 2007, Al-Ahmari 2008, Jing and Lu 2010, Mallozzi et al 2011. According to Zadeh (1975), it is very difficult for conventional quantification to reasonably express complex situations and it is necessary to use linguistic variables whose values are words or sentences in a natural or artificial language.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy e-negotiation support system for inter-firm collaborative product development

Zandi

Tavana

2012

International Journal of Computer Integrated Manufacturing

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Significant advances in manufacturing technology and the rapid intensification of the Internet and electronic commerce diffusion have given rise to competitive differentiation and rapid adaptability to competitive change. New product development is a complex and collaborative process that requires negotiation and joint decisionmaking. We propose a fuzzy electronic negotiation (e-negotiation) support system based on cooperative multicriteria game theory. The proposed system comprises three major processes: initialisation, e-negotiation and joint agreement. The Internet is used to facilitate the e-negotiation process and to minimise the response time in the decision-making process. The fuzzy sets are used to overcome issues related to the imprecise or vague judgments and incomplete information in the negotiation process. The proposed system enables the manufacturing parties involved in the negotiation process to determine the optimal coalition form for new product development and choose a common strategy to improve the payoffs of the members of the coalition group. Finally, a case study is used to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms in the mobile telecommunications industry.

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A fuzzy robust evaluation model for selecting and ranking NPD projects using Bayesian belief network and weight-restricted DEA

Cited by 61 publications

References 22 publications

R&D Project Selection Incorporating Customer-Perceived Value and Technology Potential: The Case of the Automobile Industry

R&D Project Selection Incorporating Customer-Perceived Value and Technology Potential: The Case of the Automobile Industry

Research on The Efficiency of Commercial Bank Branches Based on Weight-restricted DEA Model

A fuzzy e-negotiation support system for inter-firm collaborative product development

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