On Determination of Early Warning Grade Based on AHP Analysis in Warranty Database

Authors:
Sanghyun Lee;Mintae Lee;Culhyun Kim;Chulsu Park;Seungbeom Park;Yuyang Liu;Byungki Kim
Affiliations:
Department of Computer Engineering, Chonnam National University, Korea;Department of Computer Engineering, Chonnam National University, Korea;Department of Computer Engineering, Chonnam National University, Korea;Department of Computer Engineering, Chonnam National University, Korea;Department of Computer Engineering, Chonnam National University, Korea;Department of Computer Engineering, Chonnam National University, Korea;Department of Computer Engineering, Chonnam National University, Korea
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

A warranty claims information system is in general developed as three steps. The first step is a quantitative analysis through the time series detection of warranty claims data. The second step is early warning grade determination, considering both of the quantitave analysis and qualitative factors related to the early warning one. The third step is unit list sampling with pure warranty claims in all these activities. Especially, the considerations in early warning grade determination are the qualitative factors such as change due to complaints of customers, variations between regions, unit types and models, parts significance and so on. AHP analysis is appropriate in connection with these problems. This paper suggests a neural network learning model in determining early warning grade of warranty claims data, which includes AHP analysis and knowledge of quality experts. The early warning grade of warranty claims data using this model can compromise a dispute with rapid quality improvement and cost efficiency. The test result also suggests that the proposed method enhances accuracy of early warning grades in warranty claims database, which is at national famous automobile company.