Title :
Comprehensive prediction model of supply chain performance
Author :
Dong, Huizhong ; Shi, Chengdong
Author_Institution :
Bus. Sch., Shandong Univ. of Technol., Zibo, China
Abstract :
Performance prediction of supply chain is an important content of supply chain management. This paper establishes a supply chain performance prediction model by using fuzzy neural net in combination with fuzzy rough set based on Knowledge Discovery in Data (KDD) and Data-mining Technology. Through a case of supply chain performance prediction, the author reduces the indexes of an evaluation index system based on balanced scorecard system. Then the remaining indexes are inputted into BP neural network for intelligent training. Finally, the prediction sample data is inputted into the trained network BP, we can get supply chain performance prediction value. The result shows that the model has much higher precision and less errors and the predicted result accords with the experiment data basically.
Keywords :
backpropagation; content management; data mining; fuzzy set theory; neural nets; prediction theory; production engineering computing; rough set theory; supply chain management; BP neural network training; balanced scorecard system; data mining; data sampling; evaluation index system; fuzzy neural net; fuzzy rough set; intelligent training; knowledge discovery; supply chain management; supply chain performance prediction model; Biological neural networks; Indexes; Neurons; Performance evaluation; Set theory; Supply chains; Training; BP neural network; performance prediction; rough sets; supply chain;
Conference_Titel :
Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on
Conference_Location :
Deng Leng
Print_ISBN :
978-1-4577-0535-9
DOI :
10.1109/AIMSEC.2011.6010245