Abstract
ABSTRACTThe objective for an enterprise is to sustain profit by realizing marketing trend and grasping any business opportunities. To achieve this purpose, business planning plays an essential role. However, demand planning is the root for all internal plans and forecasting is the base for all the planning activities within the company. A good demand planning provide for a base for effective execution of business tasks. And it is needless to say that forecasting is the key reference for any important decisions made. The forecasting methods can be classified into qualitative and quantitative approaches. While executing the quantitative analysis, a company is collecting historical data and analyzing the market trend. However, the historical data are usually very complicated. How to develop an easy, fast, and precise forecasting model which is not so sensitive to the rapid environmental changes has become an important target for enterprises. In this research, four time series models including Grey System Theory, Simple Moving Averages, Weighted Moving Averages and Double Exponential Smoothing will be analyzed and compared to find the most optimal model. Constraints of these four models in demand forecasting will also be studied..An empirical study by a case company from her historical data was tested through the four methods, Grey System Theory, Simple Moving Averages, Weighted Moving Averages and Double Exponential Smoothing. The forecasting error was evaluated by the Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). It is found that the Double Exponential Smoothing method has the best performance. Weighted Moving Averages is second, Grey System Theory is the third and the last is Simple Moving Averages. The study has also monitored and evaluated the forecasting models by error control chart and signal tracking. However, there may still exist other forecasting models that can be applied for the similar purpose and therefore it deserves further studies in the future.