TY  - GEN
AB  - 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.
DA  - 2019
DO  - 10.4119/unibi/2932990
KW  - Forecasting
KW  - Inventory
KW  - E-commerce
KW  - Retailing
LA  - eng
PY  - 2019
SN  - 2196-2723
TI  - Distributional regression for demand forecasting in e-grocery
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29329909
Y2  - 2024-11-22T04:39:39
ER  -