Why the “best” point forecast depends on the error or accuracy measure

Irregular demand in retail is characterized by stock keeping units (SKUs) that present high intermittency, and simultaneously, high erraticness (classified as lumpy demand) or low erraticness (classified as intermittent demand), following the classification of Syntetos et al. (2005). These SKUs are basically defined by periods of zero sales interleaved with positive sales producing series with some degree of variability. Many SKUs at the store/daily level can be characterized as presenting such a type of demand. Therefore methods for adequately forecasting irregular time series are necessary for proper inventory management. This article derives models for intermittent and lumpy time series using the framework of score-driven models as developed by Creal et al. (2013) and Harvey (2013). More precisely we derive Poisson, negative binomial, hurdle Poisson, and hurdle negative binomial models and apply them to real data obtained from a large Brazilian retail chain, comparing the performance of the proposed models to adequate competing methods from the ‘slow’/intermittent demand forecasting literature. Forecasting accuracy is evaluated based on point forecasts and the entire predictive distribution. Our results show that the score-driven models perform well compared to intermittent traditional forecasting methods, providing competitive forecasting models for irregular demand in retailing.