Forecasting hourly foodservice sales during geopolitical and economical disruption using zero-inflated mixed effects...

Accurate predictions of product sales are essential to the foodservice sector, for planning and saving of resources. In this paper, a zero-inflated negative binomial mixed-effects model with several factors was used to predict the total sales of different product categories, taking into consideration different sites, time and weather conditions. It fits quickly by maximising the ordinary Monte Carlo likelihood approximation. The model succeeded in accurate predictions with limited data where the random effects fitted well to the exogenous factors that added noise to the dataset. This enabled an improved inference from the model by reducing the variance in the estimates of fixed effects used in the interpretation of the results. This shows how statistical modelling, using less data, can improve predictions in the foodservice industry during times of volatile demand.