Intermittent demand appears when demand events occur only sporadically.
Typically such time series have few observations making intermittent demand forecasting challenging. Forecast errors can be costly in terms of unmet demand or obsolescent stock. Intermittent demand forecasting has been
addressed using established forecasting methods, including simple moving averages, exponential smoothing and Croston’s method with its variants. This
study proposes a neural network (NN) methodology to forecast intermittent
time series. These NNs are used to provide dynamic demand rate forecasts,
which do not assume constant demand rate in the future and can capture
interactions between the non-zero demand and the inter-arrival rate of demand events, overcoming the limitations of Croston’s method. In order to
mitigate the issue of limited fitting sample, which is common in intermittent
demand, the proposed models use regularised training and median ensembles
over multiple training initialisations to produce robust forecasts. The NNs
are evaluated against established benchmarks using both forecasting accuracy and inventory metrics. The findings of forecasting and inventory metrics
are conflicting. While NNs achieved poor forecasting accuracy and bias, all
NN variants achieved higher service levels than the best performing Croston’s method variant, without requiring analogous increases in stock holding
volume. Therefore, NNs are found to be effective for intermittent demand
applications. This study provides further arguments and evidence against
the use of conventional forecasting accuracy metrics to evaluate forecasting
methods for intermittent demand, concluding that attention to inventory
metrics is desirable.