Demand forecast for short life cycle products

Abstract

Accurate forecast for demand of short life cycle products is a subject of special interest for many companies and researchers. However common forecasting approaches are not appropriate for this type of products due to the characteristics of their demand. This work proposes a method to forecast the demand of short life cycle products. Clustering techniques will be used to obtain natural groups in the time series. This analysis allows to extract relevant information for the forecasting method. The results of the proposed method will be compared to other approaches to forecast the demand of short life cycle products. Several time series datasets of different type of products are considered.