Building Forecasting Model of Automobile Industry Based On Grey Theory - A Case Study of Nissan Motor Corporation
| Autor: | Le Thanh Vinh, 黎成榮 |
|---|---|
| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 102 In this cutting-edge epoch, high technology has gone very fast where forecasting methods especially play a very important role by forecasting future development in various fields, ranging from microeconomic business such as agricultural and automobile industries to macroeconomic matters such as income, employment, and global economy. Whatever the situation is, the most important point is which forecasting methods can provide the most accurate prediction and to what extent the results can be accepted and applied in due course. In that, recently the grey forecasting model has achieved good prediction accuracy with limited data and has been widely used in various research fields. This study presents a review of theory on Grey system theory to form the basis for forecasting the performance of automobile companies in the next few years. Grey theory is truly a multidisciplinary and generic theory that deals with systems characterized by poor or insufficient information. It is based on Grey system theory to forecast the net sales with few data, in which the behaviors of systems are unknown. Data used in this study are obtained from the 2014annual report of the Nissan Motor Corporation by which the successive net sales in the coming 4 years are forecasted (i.e., 2014 to 2017). In the current research, therefore, firstly the original predicted values of net sales are obtained individually by the GM (1,1) and DGM (1,1) model. Secondly, the forecasting results of two models are compared by mean absolute percentage error (MAPE). The findings are that, first, the accuracy levels of these two models are much the same with the excellent ability, second, the grey forecasting model performs well with poor information, and, third, it is used for individuals or organizations rather than system development. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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