Studies on the Analysis and Forecast of Vietnams Rice Exports

• Main Authors: Tran, Thi-Huyen, 陳氏玄
• Other Authors: Wu, Yan-Kuen
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/373auf

碩士 === 萬能科技大學 === 經營管理研究所 === 107 === The purpose of this study is to explore and forecast the number and amount of Vietnamese rice exports. We use many methods such as moving average method, exponential smoothing method, seasonal factors summing mode, seasonal factors multiplying mode, and simple regression analysis as a predictive tool to forecast the number and amount of Vietnamese rice exports. The real data manipulated in this study were obtained from the Vietnam customs. It includes the monthly number and amount of Vietnamese rice exports in AD 2013 to AD 2017. We select the top ten countries for analysis the actual situation of Vietnamese rice exports to major countries. They are Ivory Coast, Saudi Arabia, Ghana, United States, Hong Kong, Taiwan, Malaysia, Netherlands, Singapore, China. The results of this study reveal that seasonal factors summing mode respectively. Applying the linear regression analysis method for forecasting the amount of Vietnamese rice exports in January 2017 to December 2017 yields the relatively high forecast accuracy. Especially, the value of MAPE is just 4.8% to export to China. In future research we provide the following suggestions: There are many factors affecting the export of rice in Vietnam. This study does not study the influencing factors. When predicting the quantity and amount of rice exports, in addition to the market factors, it is also necessary to consider the amount of future sales due to changes in time or environment. influences. Therefore, subsequent researchers can include these variables in the scope of the study and explore. This study did not study the unit price of Vietnamese rice exported to countries, so follow-up researchers can include this variation factor in the scope of research. Although the various prediction methods in this study have good accuracy, they can be used in conjunction with other prediction methods to improve the prediction accuracy, such as the gray prediction model.