Keep it flowing: A supply chain management experiment

Author

Ma, Yang ; Levi, Abraham

Author_Institution

Coll. of Eng. & Appl. Technol., Singapore Inst. of Technol., Singapore, Singapore

fYear

2010

fDate

6-9 Oct. 2010

Firstpage

1

Lastpage

5

Abstract

In recent years we have witnessed the development of new and improved Information Technology (IT) methods for Supply Chain Management. This study employs decision support techniques in Supply Chain Management scenarios in order to address some of the shortcomings of existing models. A three-step decision structuring framework is used to develop a model based on Bayesian networks. The model is then used to support management risk strategies under different scenarios. In addition, this study provides insights into how decision support, and especially Bayesian networks, can enhance IT methods.

Keywords

Bayes methods; decision support systems; production engineering computing; supply chain management; Bayesian networks; decision support techniques; information technology methods; management risk strategies; supply chain management; Analytical models; Bayesian methods; Business; Logic gates; Mathematical model; Product development; Production; Bayesian networks; Information Technology; Supply Chain Management;

fLanguage

English

Publisher

ieee

Conference_Titel

Supply Chain Management and Information Systems (SCMIS), 2010 8th International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-962-367-696-0

Type

conf

Filename

5681774