An Agent-Based Expert System Architecture for Product Return Administration
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.1 pp.73-80
- Publication Date
- 2013/01/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.73
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
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Chen-Shu WANG, "An Agent-Based Expert System Architecture for Product Return Administration" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 1, pp. 73-80, January 2013, doi: 10.1587/transinf.E96.D.73.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.73/_p
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@ARTICLE{e96-d_1_73,
author={Chen-Shu WANG, },
journal={IEICE TRANSACTIONS on Information},
title={An Agent-Based Expert System Architecture for Product Return Administration},
year={2013},
volume={E96-D},
number={1},
pages={73-80},
abstract={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.},
keywords={},
doi={10.1587/transinf.E96.D.73},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - An Agent-Based Expert System Architecture for Product Return Administration
T2 - IEICE TRANSACTIONS on Information
SP - 73
EP - 80
AU - Chen-Shu WANG
PY - 2013
DO - 10.1587/transinf.E96.D.73
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2013
AB - 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.
ER -
English
