Demand Forecast Model in Wafer Foundry-Analysis by ARIMA Model-

• Main Authors: Yi-Hsiang Kuo, 郭翊翔
• Other Authors: Chi Chiang
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/58763508773673370642

碩士 === 國立交通大學 === 管理學院碩士在職專班管理科學組 === 96 === In the industrial structure of Taiwan, demand forecast is very critical and necessary for business financial planning, inventory management, manufacturing plan, distribution plan, marketing and customer service and the forecast number is the basic and crucial item for every person on daily operation or meeting. No matter how the forecast number comes from, the only requirement is its accuracy. How it will happen when the accuracy of demand forecast is low to a company? As we can understand, Over-forecast will induce many unnecessary inventories and operation cost. Under-forecast also may have a company lose its opportunities and customer satisfactions. For the reason of that, it is an important topic to improve the accuracy of demand forecast and management the uncertainty of demand. How to manage the uncertainty of demand? Many studies focus on analyzing historical data and using time series forecast model to do demand forecast to management the uncertainty of demand. Hence, the main purpose of this research is to find out a useful ARIMA model by adopting the company’s historical sales data from Jan. 1997 to May 2007. The forecast performance of the ARIMA model in this research is evaluated by MAPE value comparing to the company’s actual sales volume for the following several months (2007.6~2007.12). Based on the MAPE value, the outcome or the forecast performance of this research is very reasonable and accurate. Also, it has better performance than the forecast performance of Sale Division, Regional Planning Division and Headquarter Planning Division in the company. In the real world, it is impossible to have a absolutely precise forecast result. But we can do our best to make the distortion smaller by using forecast models and thus company will improve its competitiveness and gain more profit. Hopefully, this research can be helpful to all the wafer foundries when they are working on demand forecasting and planning.