Study of Agricultural Product Sales Forecast with Grey System Theory and Artificial Neural Networks

• Main Authors: Mi-Ru Cheng, 鄭米茹
• Other Authors: Yung-Hsin Wang
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/71294718138941533948

碩士 === 大同大學 === 資訊經營學系(所) === 93 === Since Taiwan joined the WTO, agricultural product sales is no longer limited to domestic competition but steps to the internationalization. Currently, the sales volume presents an unstable phenomenon, which affects the development and survival of an enterprise that is profit-based. The prediction has the function for forecasting future and the result is often the regulatory authority decision-making basis. Therefore, prediction accuracy influences enterprise's development and plays extremely an important role in the enterprise. The traditional agriculture operators should not only depend on experiences to predict the product sales in order to reduce the risk and truly grasp the market pulsation. Therefore, this study uses the Grey Prediction and the Back Propagation Network in dealing with forecast problems. We use an example of the vegetable middle distributor industry’s supply and marketing situation of Yunlin County as the research case to forecast agricultural product on market sales volume using “scallion” as example. Analysis that makes use of the accuracy of the two models according to their forecast results will provide the factory owner with an effective consultation on matters pertaining to procurement and business operations. The results regarding forecast accuracy measures show that BPN’s error percentage is less than 5%, which is classified as “excellent”, and Grey Prediction’s, on the other hand, has a percentage between 5%~10%, which then is under the “good” class. These two forecast methods are seen to yield considerably accurate results, and are most likely to reach high forecasting quality.