國立臺灣大學工業工程學研究所Chen, ArgonArgonChenGuo, Ruey-ShanRuey-ShanGuoChang, Shi-ChungShi-ChungChangChen, KenkKenkChenHsia, ZivZivHsiaLan, JakeyJakeyLan2006-09-272018-06-292006-09-272018-06-292002http://ntur.lib.ntu.edu.tw//handle/246246/200609271216531324842002 deliverable report:. forecasting methodologies for multidimensional aggregated demands. task 879.001: intelligent demand aggregation & forecast solutions. project 879: demand data mining & planning in semiconductorThe demand uncertainty propagated and magnified over the semiconductor demand-supply network is the crucial cause of poor manufacturing/logistic plans. To manage the demand variability, appropriate demand aggregation/forecasting approaches are known to be effective. In the second year of this research task, there are three main research accomplishments: optimum demand aggregation/disaggregation hierarchy, and forecasting by dynamic EWMA demand disaggregation. In the first accomplishment, we have defined and proposed optimum demand planning hierarchy that can greatly improve the quality of demand plans. Finally, the dynamic EWMA demand disaggregation approaches improve the demand forecasts by taking into account the dynamic changes of product mix.application/001.pdf283270 bytesapplication/pdfzh-TWconference paperhttp://ntur.lib.ntu.edu.tw/bitstream/246246/20060927121653132484/1/2002 deliverable report 879.001.pdf