• DocumentCode
    3167060
  • Title

    Comprehensive prediction model of supply chain performance

  • Author

    Dong, Huizhong ; Shi, Chengdong

  • Author_Institution
    Bus. Sch., Shandong Univ. of Technol., Zibo, China
  • fYear
    2011
  • fDate
    8-10 Aug. 2011
  • Firstpage
    257
  • Lastpage
    260
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • 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
  • Type

    conf

  • DOI
    10.1109/AIMSEC.2011.6010245
  • Filename
    6010245