English  |  正體中文  |  简体中文  |  Items with full text/Total items : 58605/92268 (64%)
Visitors : 553101      Online Users : 130
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    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

    Files in This Item:

    File SizeFormat
    1094-6977_39(2)p223-227.pdf333KbAdobe PDF721View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback