An Agent-Based Expert System Architecture for Product Return Administration

Chen-Shu WANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.1   pp.73-80
Publication Date: 2013/01/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.73
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
product return,  intelligent agent,  case-based reasoning,   intelligent system design,  

Full Text: PDF(1.9MB)>>
Buy this Article

Product return is a critical but controversial issue. To deal with such a vague return problem, businesses must improve their information transparency in order to administrate the product return behaviour of their end users. This study proposes an intelligent return administration expert system (iRAES) to provide product return forecasting and decision support for returned product administration. The iRAES consists of two intelligent agents that adopt a hybrid data mining algorithm. The return diagnosis agent generates different alarms for certain types of product return, based on forecasts of the return possibility. The return recommender agent is implemented on the basis of case-based reasoning, and provides the return centre clerk with a recommendation for returned product administration. We present a 3C-iShop scenario to demonstrate the feasibility and efficiency of the iRAES architecture. Our experiments identify a particularly interesting return, for which iRAES generates a recommendation for returned product administration. On average, iRAES decreases the effort required to generate a recommendation by 70% compared to previous return administration systems, and improves performance via return decision support by 37%. iRAES is designed to accelerate product return administration, and improve the performance of product return knowledge management.