DocumentCode
1167198
Title
Mining Demand Chain Knowledge for New Product Development and Marketing
Author
Liao, Shu-Hsien ; Wen, Chih-Hao
Author_Institution
Dept. of Manage. Sci. & Decision Making, Tamkang Univ., Taipei
Volume
39
Issue
2
fYear
2009
fDate
3/1/2009 12:00:00 AM
Firstpage
223
Lastpage
227
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.
Keywords
bicycles; customer satisfaction; data mining; product design; product development; production planning; GIANT; a priori algorithm; association rule; bicycle brand/manufacture company; consumption experience; customer demand; data mining; enterprises; knowledge assets; knowledge extraction; knowledge patterns; market channels; marketing; mining demand chain knowledge; new product development; product design; product planning; production lines; Association rule; data mining; demand chain management; knowledge extraction; marketing segmentation; new product development (NPD);
fLanguage
English
Journal_Title
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
Publisher
ieee
ISSN
1094-6977
Type
jour
DOI
10.1109/TSMCC.2008.2007249
Filename
4785499
Link To Document