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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/50408


    Title: Mining demand chain knowledge for new product development and marketing
    Authors: 廖述賢;Liao, Shu-hsien;Wen, Chih-hao
    Contributors: 淡江大學經營決策學系
    Keywords: Association rule;data mining;demand chain management;knowledge extraction;marketing segmentation;new product development (NPD)
    Date: 2009-03-01
    Issue Date: 2010-08-09 17:01:54 (UTC+8)
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Abstract: Many enterprises devote a significant portion of their budget to new product development (NPD) and marketing to make their products distinctive from those of competitors, and better fit the needs and wants of consumers. Hence, knowledge and feedback on customer demand and consumption experience has become an important information and asset for enterprises. This paper investigates the following research issues in a world leading bicycle brand/manufacture company, GIANT of Taiwan: what exactly are the customerspsila ldquofunctional needsrdquo and ldquowantsrdquo for bicycles? Does knowledge of the customers and the product itself reflect the needs of the market? Can product design and planning for production lines be integrated with the knowledge of customers and market channels? Can the knowledge of customers and market channels be transformed into knowledge assets of the enterprises during the stage of NPD? The a priori algorithm is a methodology of association rule for data mining, which is implemented for mining demand chain knowledge from channels (sales and maintenance) and customers. Knowledge extraction from data mining results is illustrated as knowledge patterns and rules in order to propose suggestions and solutions to the case firm for NPD and marketing.
    Relation: IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews 39(2), pp.223-227
    DOI: 10.1109/TSMCC.2008.2007249
    Appears in Collections:[Department of Management Sciences] Journal Article

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