%0 Journal Article %1 IJACSA.2012.031120 %A Attariuas Hicham Bouhorma Mohammed, Sofi Anas %D 2012 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K Component; Delphi Hybrid Method; Sales approach; back clustering; forecasting; fuzzy intelligence network. propagation system; %N 11 %T Hybrid intelligent system for Sale Forecasting using Delphi and adaptive Fuzzy Back-Propagation Neural Networks %U http://ijacsa.thesai.org/ %V 3 %X Sales forecasting is one of the most crucial issues addressed in business. Control and evaluation of future sales still seem concerned both researchers and policy makers and managers of companies. this research propose an intelligent hybrid sales forecasting system Delphi-FCBPN sales forecast based on Delphi Method, fuzzy clustering and Back-propagation (BP) Neural Networks with adaptive learning rate. The proposed model is constructed to integrate expert judgments, using Delphi method, in enhancing the model of FCBPN. Winter’s Exponential Smoothing method will be utilized to take the trend effect into consideration. The data for this search come from an industrial company that manufactures packaging. Analyze of results show that the proposed model outperforms other three different forecasting models in MAPE and RMSE measures.

@article{IJACSA.2012.031120, abstract = {Sales forecasting is one of the most crucial issues addressed in business. Control and evaluation of future sales still seem concerned both researchers and policy makers and managers of companies. this research propose an intelligent hybrid sales forecasting system Delphi-FCBPN sales forecast based on Delphi Method, fuzzy clustering and Back-propagation (BP) Neural Networks with adaptive learning rate. The proposed model is constructed to integrate expert judgments, using Delphi method, in enhancing the model of FCBPN. Winter’s Exponential Smoothing method will be utilized to take the trend effect into consideration. The data for this search come from an industrial company that manufactures packaging. Analyze of results show that the proposed model outperforms other three different forecasting models in MAPE and RMSE measures.}, added-at = {2014-02-21T08:00:08.000+0100}, author = {{Attariuas Hicham Bouhorma Mohammed}, Sofi Anas}, biburl = {https://www.bibsonomy.org/bibtex/234b9dd968c35422b59f9cdc68753b9d5/thesaiorg}, interhash = {38040eceb2e9589ac080d7ca9a20c50f}, intrahash = {34b9dd968c35422b59f9cdc68753b9d5}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {Component; Delphi Hybrid Method; Sales approach; back clustering; forecasting; fuzzy intelligence network. propagation system;}, number = 11, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Hybrid intelligent system for Sale Forecasting using Delphi and adaptive Fuzzy Back-Propagation Neural Networks}}, url = {http://ijacsa.thesai.org/}, volume = 3, year = 2012 }