Distributional regression for demand forecasting in e-grocery

Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R (2019) Universität Bielefeld Working Papers in Economics and Management; 09-2018.
Bielefeld: Bielefeld University, Department of Business Administration and Economics.

Diskussionspapier | Veröffentlicht | Englisch
 
Download
OA 435.28 KB
Autor/in
; ; ; ;
Abstract / Bemerkung
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.
Stichworte
Forecasting; Inventory; E-commerce; Retailing
Erscheinungsjahr
2019
Band
09-2018
ISSN
2196-2723
Page URI
https://pub.uni-bielefeld.de/record/2932990

Zitieren

Ulrich M, Jahnke H, Langrock R, Pesch R, Senge R. Distributional regression for demand forecasting in e-grocery. Universität Bielefeld Working Papers in Economics and Management. Vol 09-2018. Bielefeld: Bielefeld University, Department of Business Administration and Economics; 2019.
Ulrich, M., Jahnke, H., Langrock, R., Pesch, R., & Senge, R. (2019). Distributional regression for demand forecasting in e-grocery (Universität Bielefeld Working Papers in Economics and Management, 09-2018). Bielefeld: Bielefeld University, Department of Business Administration and Economics. doi:10.4119/unibi/2932990
Ulrich, M., Jahnke, H., Langrock, R., Pesch, R., and Senge, R. (2019). Distributional regression for demand forecasting in e-grocery. Universität Bielefeld Working Papers in Economics and Management, 09-2018, Bielefeld: Bielefeld University, Department of Business Administration and Economics.
Ulrich, M., et al., 2019. Distributional regression for demand forecasting in e-grocery, Universität Bielefeld Working Papers in Economics and Management, no.09-2018, Bielefeld: Bielefeld University, Department of Business Administration and Economics.
M. Ulrich, et al., Distributional regression for demand forecasting in e-grocery, Universität Bielefeld Working Papers in Economics and Management, vol. 09-2018, Bielefeld: Bielefeld University, Department of Business Administration and Economics, 2019.
Ulrich, M., Jahnke, H., Langrock, R., Pesch, R., Senge, R.: Distributional regression for demand forecasting in e-grocery. Universität Bielefeld Working Papers in Economics and Management, 09-2018. Bielefeld University, Department of Business Administration and Economics, Bielefeld (2019).
Ulrich, Matthias, Jahnke, Hermann, Langrock, Roland, Pesch, Robert, and Senge, Robin. Distributional regression for demand forecasting in e-grocery. Bielefeld: Bielefeld University, Department of Business Administration and Economics, 2019. Universität Bielefeld Working Papers in Economics and Management. 09-2018.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Volltext(e)
Name
Access Level
OA Open Access
Zuletzt Hochgeladen
2019-09-06T09:19:04Z
MD5 Prüfsumme
90b27e9f598fd0d6fef3a5b4e0f4c3e3

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Suchen in

Google Scholar