A Study on Forecasting Intermittent Demand with theApplication of Resampling Method

• Main Authors: Chih-I Huang, 黃志一
• Other Authors: 申生元
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/09756419731489968432

碩士 === 元智大學 === 資訊管理學系 === 95 === Forecasting has long been an important technique for enterprises. Most business sectors need forecasting, such as finance, marketing, and logistics. However, different types of time series data exist such as distinct patterns between demands of fast moving items and slow moving items. Hence, selecting an appropriate forecasting model that takes the demand characteristic into account can not be overlooked if we want to obtain a more accurate forecast. This study focuses on a specific type of time series data, called intermittent demand, for which traditional forecasting methods may not be appropriate. In contrast to the forecasting consumer goods, for example, we are likely to be more difficult to accurately predict the demand of spare part due to its nature barrier of intermittent needs. In the thesis, we propose two forecasting algorithms which is essentially based on the concept of bootstrap re-sampling and Markov chain. The idea underlying our method is originated from a patented algorithm proposed by Willemain et al. Differing from the method of Willemain et al , we consider Markov transition probability between periods with zero and non-zero values simultaneously such that we might enhance the precision for forecasting the demand of spare part. Using generated data sets, we compared our algorithms with other methods such as simple exponential smoothing, Croston, modified Croston’s, and bootstrapping. Experimental results demonstrate that our methods have relatively well predictability higher than that of Willemain bootstrapping method. On the other hand, computational studies also revealed that Croston’s and Modified Croston’s methods are quite well in forecasting data with intermittency, but there was no strong evidence to show which method is always the winner.