  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. Sparse demand data usually results in lumpy or
intermittent demand patterns, which have sparse and irregular demand intervals.
Usual statistical and machine learning models fail to provide good forecasts in
such scenarios. Our research shows 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 results against baseline methods. Finally, we
propose a novel evaluation criterion of lumpy and intermittent demand
forecasting models' performance. Our research shows that global classification
models are the best choice when predicting demand event occurrence. When
predicting demand sizes, we achieved the best results using Simple Exponential
Smoothing forecast. We tested our approach on real-world data consisting of 516
three-year-long time series corresponding to European automotive original
equipment manufacturers' daily demand.
