Distributional Regression for Demand Forecasting in e-grocery

31 Pages Posted: 9 Jan 2019

See all articles by Hermann Jahnke

Hermann Jahnke

Bielefeld University

Matthias Ulrich

Bielefeld University - Department of Business Administration and Economics

Robert Pesch

Inovex GmbH

Robin Senge

Inovex GmbH

Roland Langrock

Bielefeld University - Department of Business Administration and Economics

Date Written: December 8

Abstract

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

Keywords: Forecasting, Inventory, E-commerce, Retailing

Suggested Citation

Jahnke, Hermann and Ulrich, Matthias and Pesch, Robert and Senge, Robin and Langrock, Roland, Distributional Regression for Demand Forecasting in e-grocery (December 8). Bielefeld Working Papers in Economics and Management No. 09-2018. Available at SSRN: https://ssrn.com/abstract=3312609 or http://dx.doi.org/10.2139/ssrn.3312609

Hermann Jahnke (Contact Author)

Bielefeld University ( email )

Dept. of Business Administration and Economics
Bielefeld, 33501
Germany

Matthias Ulrich

Bielefeld University - Department of Business Administration and Economics ( email )

P.O. Box 100131
D-33501 Bielefeld, NRW 33501
Germany

Robert Pesch

Inovex GmbH ( email )

Ludwig-Erhard-Allee 6
Karlsruhe, DE 76131
Germany

Robin Senge

Inovex GmbH ( email )

Ludwig-Erhard-Allee 6
Karlsruhe, DE 76131
Germany

Roland Langrock

Bielefeld University - Department of Business Administration and Economics ( email )

P.O. Box 100131
D-33501 Bielefeld, NRW 33501
Germany

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