Neural Forecasting of Intermittent Demand

Intermittent demand appears when there are several periods in a time series with no demand occurs and when it occurs it does not have a constant size. Furthermore, intermittent demand time series have typically few observations. These factors make intermittent demand forecasting challenging and forecast errors can be costly in terms of unmet demand or obsolescent stock.

Intermittent demand forecasting problems have been addressed using established forecasting methods, like simple moving averages, exponential smoothing and Croston's method with its variants. A new study proposes a neural network (NN) methodology to forecast intermittent time series.

NNs are used to provide both constant demand rate forecasts, as the Croston's method that is the norm for intermittent demand problems, and dynamic demand rate forecasts, which do not assume that the demand rate stays constant in the future. A key NN limitation that is addressed in this study is the small time series sample size, which can hinder NNs' training.

The methods are compared on a dataset of 3000 real time series, from the automotive industry, using the mean absolute scaled error that has been found appropriate for intermittent demand forecasting evaluations.

The out-of-sample comparisons indicate that NNs forecasting constant demand rate have superior performance in comparison to established competing methodologies, while dynamic demand NN forecasts also rank high, indicating that the implications of this alternative should be considered.

In order to explore this further, an inventory simulation has been performed. The methods are evaluated directly on service level and not using forecast error measures. The findings from both evaluations are contrasted providing insights on the performance of the methods and discussing whether forecast errors are a good proxy for service levels.