Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand

Abstract

Demand forecasting is a crucial component of demand management. While shortening the forecasting horizon allows for more recent data and less uncertainty, this frequently means lower data aggregation levels and a more significant data sparsity. Furthermore, sparse demand data usually result in lumpy or intermittent demand patterns with irregular demand intervals. The usual statistical and machine learning models fail to provide good forecasts in such scenarios. Our research confirms that competitive demand forecasts can be obtained through two models: predicting the demand occurrence and estimating the demand size. We analyze the usage of local and global machine learning models for both cases and compare the results against baseline methods. Finally, we propose a novel evaluation criterion for the performance of lumpy and intermittent demand forecasting models. Our research shows that global classification models are the best choice when predicting demand event occurrence. We achieved the best results using the simple exponential smoothing forecast to predict demand sizes. We tested our approach on real-world data made up of 516 time series corresponding to the daily demand, over three years, of a European original automotive equipment manufacturer.