Application of a FWA based Fuzzy MCDM Model to the Evaluation and Selection of New Product Development Projects

• Main Authors: Wu Sing-Hong, 吳星宏
• Other Authors: Chu Ta-Chung
• Format: Others
• Language: en_US
• Published: 101
• Online Access: http://ndltd.ncl.edu.tw/handle/36222851484246243172

碩士 === 南台科技大學 === 科技管理研究所 === 100 === To survive and maintain competitiveness under increasing competitive market, it is more and more important for corporations to develop new products or enhance the added value of existing products in order to increase their competitiveness. To outstand itself in the market, a corporation must develop new products to meet customer demands earlier than its competitors. Therefore, corporations need to continuously develop new products to maintain competitiveness. However, owing to the resource is limited, corporations are not able to execute all new product development projects. It is necessary for a corporation to carefully evaluate and rank the new product development projects to effectively concentrate its resource onto the selected projects. Thus, new product development projects selection has become one of the most important issues for corporation development. New product development projects selection needs to consider a variety of criteria, including qualitative ones (e.g. technology) and quantitative ones (e.g. return on investment). In addition, decision makers may have different opinions for different criteria. Thus, it is suitable to evaluate new product development projects via fuzzy multiple criteria decision making (FMCDM) model. The purpose of this research is to utilize fuzzy multiple criteria decision making to construct a new product development projects selection model. The suggested model simultaneously considers qualitative and quantitative criteria as well as the importance of various criteria. In the suggested model, the rated value of each alternative versus each criterion is firstly multiplied by the weight given to each criterion by each decision maker, and then the final fuzzy evaluation value of each alternative can be obtained through aggregating weighted ratings from all decision makers across all criteria. The membership function of the final fuzzy evaluation value can be developed by α-cuts and interval arithmetic of fuzzy numbers. And centroid method is applied to defuzzify and rank the fuzzy numbers for decision making. The defuzzification process can be clearly expressed by mathematical formulas. Finally, a numerical example is used to verify the applicability of the proposed model.