E-grocery offers customers an alternative to traditional brick-and-mortar

grocery retailing. Customers select e-grocery for convenience, making use of

the home delivery at a selected time slot. In contrast to brick-and-mortar

retailing, in e-grocery on-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, compared to brick-and-mortar retailing, on-stock

availability of SKUs has a 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 linear regression, quantile regression, and some popular

methods from machine learning. The models are evaluated in a case study,

where we compare their out-of-sample predictive performance with regard to

the service level selected by the e-grocery retailer considered.