A Study on the Revenue Forecast Mechanism –A Company as an Example

• Main Authors: Yu, Ying-Lan, 余英蘭
• Other Authors: Li, Yung-Ming
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/f88h79

碩士 === 國立交通大學 === 管理學院資訊管理學程 === 102 === Nowadays, the company faces challenges to keep the advantage of competition. In the small profit era, how to make better forecasting becomes an important issue for a company to keep the lead-edge of competition and to create more profile. Forecasting now is largely applied in several areas. It reveals that managers of company realize the importance of revenue forecasting in business management. Accurate forecasting can reduce the inventories, increase profit, and also enhance satisfaction of the customers. According to the analysis of past revenue data, the yearly revenue curve for each product line seems to follow some patterns. Therefore, we can dig out useful data from the past data and use the extracted information to answer the questions, such as "Is it possible to reach the target of the revenue?", or "How is the potential for each product line?” Utilizing the statistical method and the artificial intelligence, we can build up the forecasting models. With the help from current IT technology, those models can predict the revenue based on the past data. The proposed model can pre event human judgment failure, save the time for the managers, and help them more focus on process improvement activities. The research uses the methodologies of data mining and the artificial neural network to create forecasting model, which is used to predict the curve of future revenue. We use the performance measures of MAE, MAPE and RMSE to evaluate the forecasting accuracy. Comparing other traditional and benchmark forecasting models, our proposed model gets better forecasting accuracy.