Distributional regression for demand forecasting in e-grocery

Abstract

E-grocery offers customers an alternative to traditional store grocery retailing. Customers select e-grocery for convenience, making use of the home delivery at a selected time slot. In contrast to store retailing, in e-grocery in-stock information for stock keeping units (SKUs) becomes transparent to the customer before substantial shopping effort has been invested, thus reducing the personal cost of switching to another supplier. As a consequence, in-stock availability of SKUs has a particularly strong impact on the customer’s order decision, resulting in higher strategic service level targets for the e-grocery retailer. To account for these high service level targets, we propose a suitable model for accurately predicting the extreme right tail of the demand distribution, rather than providing point forecasts of its mean. Specifically, we propose the application of distributional regression methods — so-called Generalised Additive Models for Location, Scale and Shape (GAMLSS) — to arrive at the cost-minimising solution according to the newsvendor model. As benchmark models we consider various regression models as well as popular methods from machine learning. The models are evaluated in a case study, where we compare their out-of-sample predictive performance with respect to the service level provided by the e-grocery retailer analysed.