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The Needs and Benefits of Applying Textual Data Mining within the Product Development Process
Author(s) -
Me Rakesh,
Tong Loh Han,
Sathiyakeerthi S.,
Brombacher Aarnout,
Leong Christopher
Publication year - 2004
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.536
Subject(s) - computer science , process (computing) , quality (philosophy) , product (mathematics) , new product development , reliability (semiconductor) , implementation , task (project management) , data science , data mining , software engineering , marketing , engineering , business , systems engineering , philosophy , power (physics) , physics , geometry , mathematics , epistemology , quantum mechanics , operating system
As a result of the growing competition in recent years, new trends such as increased product complexity, changing customer requirements and shortening development time have emerged within the product development process (PDP). These trends have added more challenges to the already‐difficult task of quality and reliability prediction and improvement. They have given rise to an increase in the number of unexpected events in the PDP. Traditional tools are only partially adequate to cover these unexpected events. As such, new tools are being sought to complement traditional ones. This paper investigates the use of one such tool, textual data mining for the purpose of quality and reliability improvement. The motivation for this paper stems from the need to handle ‘loosely structured textual data’ within the product development process. Thus far, most of the studies on data mining within the PDP have focused on numerical databases. In this paper, the need for the study of textual databases is established. Possible areas within a generic PDP for consumer and professional products, where textual data mining could be employed are highlighted. In addition, successful implementations of textual data mining within two large multi‐national companies are presented. Copyright © 2003 John Wiley & Sons, Ltd.