Warranty service is getting important since it is an agreement between manufacturers and consumers. An issue is to find out a lower level of agreement from the perspective of manufacturers and consumers. Thus, it is very important to determine early warning/detection degree of defected parts through warranty claims data. However, there are qualitative factors more than quantitative ones in the determination. The study thus provides a part-significance knowledge extraction method based on analytic hierarchy process analysis which is appropriate to analyze those qualitative factors as well as a process to extract a list of defected parts using neural network learning.

Item Type:	Refereed Conference Paper
Keywords:	warranty claims data, reliability, neural network
Research Division:	Information and Computing Sciences
Research Group:	Artificial Intelligence and Image Processing
Research Field:	Neural, Evolutionary and Fuzzy Computation
Objective Division:	Information and Communication Services
Objective Group:	Computer Software and Services
Objective Field:	Application Software Packages (excl. Computer Games)
UTAS Author:	Yeom, SJ (Dr Soonja Yeom)
ID Code:	123784
Year Published:	2007
Web of Science® Times Cited:	3
Deposited By:	Information and Communication Technology
Deposited On:	2018-01-29
Last Modified:	2018-03-29
Downloads:	0