استفاده برای تئوری رقابت از تصمیم گیری سرمایه گذاری؛ یک مطالعه موردی صنعتی از صنعت مونتاژ تایوان IC

This study empirically analyzes model accuracy, and applies grey forecasting to handle non-linear problems, insufficient data resources and forecasting involving small samples, and to construct the co-opetition diffusion model for the Lotka–Volterra (L.V.) system. Furthermore, this study examines historical data comprising revenue trends in the Taiwanese IC assembly industry during the past ten years and selects from a range of forecasting models. Empirical study uses MAPE to precisely analyze revenue trends in the L.V. dynamic co-opetition diffusion model relation to the IC assembly industry. The nine companies will be selected from 4 to 11 of the modeling, the results of the LV model 64 accuracy test, its accuracy is higher than 95% accounted for 59 times, five times better than the grey prediction, showing LV competing diffusion model not only with grey prediction, and better than the traditional grey forecasting model to make a higher accuracy of the predicted value. Like grey forecasting, MAPE can promptly respond even given insufficient data. Additionally, MAPE is able to provide more accurate forecasting values than the traditional Grey forecasting model. This study demonstrates the applicability of the dynamic co-opetition theory forecasting model to the Taiwanese IC assembly industry and provides management with a reference for use in decisions aimed to increase managerial competitiveness.

Under different research backgrounds, this study uses different forecasting tools that produce forecasts of different effectiveness. This study uses Grey theory statistics to achieve considerable accuracy and easy computation, and to test forecasting advantages and disadvantages of Lotka–Voltera (herein after referred as L.V.) in the short term. After collecting the relevant literature, this study uses the background of the Taiwan IC assembly industry as a prerequisite and divides the collected time series into two parts for evaluation. First, this study substitutes the revenue statistics of the main IC assembly makers in Taiwan into the L.V. system forecasting model, then adopts the traditional grey theory GM (1,1) shadow model, and finally evaluates the advantages and disadvantages of various models to select the best model obtained by model construction during different steps. This study uses the “Semiconductor Yearbook” along with other references to obtain operational statistics for the main IC assembly makers in Taiwan from 2000 to 2010. Based on limited data, this research adopts the L.V. forecasting model to test its applicability, compares it with the Grey model, which is good for short-term forecasting, and discusses the best forecasting method under different forecasting backgrounds.

This studyconcludesthefollowingbasedonthemodel evaluation results:  SIMPLY TheL.V.modelislesslimited: This studyfindsthatthenewlydevelopedL.V.forecasting model canbeusedinapplicationsotherthantraditional forecasting. Datasubstitutiondoesnotnecessarilyconsider data numberanddoesnothavetousetoomanyhistoricaldata for estimate.  L.V. computationissimpleandeasy: Compared withthetraditionalGreymodel,theL.V.model does notinvolvecomplicatedmatrixcomputationandnor does itrequiresoftwareassistancetoobtainresults.L.V. computation isveryconvenient.  The L.V.modelhasexcellentforecastingaccuracy: In Table 1, theL.V.modeltakesup59itemsinthe95%of accuracy, whiletheGreymodelaccountsfor5items.However the accuracyofGreymodelfallsbetween85%and95%.Though the Greymodelhasacertainforecastinglevel,itremains inferior totheL.V.model.  The L.V.modelcanbeusedtoforecasttrendsinthesemi- conductor industry: EmpiricalresultsfromapplyingtheL.V.modeltothesemi- conductorindustrydemonstratethatboththeL.V.andGrey modelscanmakeforecastsunderconditionsoflimitedor incompletedata.HowevertheL.V.modelismoreaccuratethan theGreymodel,andisalsobetteratshort-termforecasting.