DocumentCode
2022993
Title
Notice of Retraction
Forecasting based inventory management for supply chain
Author
Kung-Jeng Wang ; Makond, B. ; Lin, Y.S.
Author_Institution
Dept. of Ind. Manage., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan
Volume
6
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
2964
Lastpage
2967
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper presents a new approach for forecasting inventory policy performances efficiently. Multiple regression-based forecasting models are investigated for prediction of the supplier´s total profit in a two-echelon supply chain. The proposed models are constructed by employing a weighting-factor method and data transformation to give higher forecasting accuracy than conventional regression models. In addition, the proposed models consume less than 1% computing time than a regular inventory control model in previous literature.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper presents a new approach for forecasting inventory policy performances efficiently. Multiple regression-based forecasting models are investigated for prediction of the supplier´s total profit in a two-echelon supply chain. The proposed models are constructed by employing a weighting-factor method and data transformation to give higher forecasting accuracy than conventional regression models. In addition, the proposed models consume less than 1% computing time than a regular inventory control model in previous literature.
Keywords
forecasting theory; inventory management; regression analysis; stock control; supply chain management; data transformation; forecasting based inventory management; inventory control model; inventory policy forecasting accuracy; multiple regression-based forecasting model; two-echelon supply chain; weighting-factor method; Biological system modeling; Computational modeling; Correlation; Data models; Forecasting; Predictive models; Supply chains; Supply chain performances; forecasting; multiple regression;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on
Conference_Location
Yantai
Print_ISBN
978-1-4244-5931-5
Type
conf
DOI
10.1109/FSKD.2010.5569080
Filename
5569080
Link To Document