A data-driven newsvendor problem: From data to decision

Huber, Jakob ; Müller, Sebastian ; Fleischmann, Moritz ; Stuckenschmidt, Heiner

DOI: https://doi.org/10.1016/j.ejor.2019.04.043
URL: https://www.sciencedirect.com/science/article/abs/...
Additional URL: https://www.researchgate.net/publication/321938534...
Document Type: Article
Year of publication: 2019
The title of a journal, publication series: European Journal of Operational Research : EJOR
Volume: 278
Issue number: 3
Page range: 904-915
Place of publication: Amsterdam [u.a.]
Publishing house: Elsevier
ISSN: 0377-2217
Publication language: English
Institution: Business School > ABWL u. Logistik (Fleischmann 2009-)
School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
330 Economics
Keywords (English): inventory ; newsvendor ; retail ; machine learning ; quantile regression
Abstract: Retailers that offer perishable items are required to make ordering decisions for hundreds of products on a daily basis. This task is non-trivial because the risk of ordering too much or too little is associated with overstocking costs and unsatisfied customers. The well-known newsvendor model captures the essence of this trade-off. Traditionally, this newsvendor problem is solved based on a demand distribution assumption. However, in reality, the true demand distribution is hardly ever known to the decision maker. Instead, large datasets are available that enable the use of empirical distributions. In this paper, we investigate how to exploit this data for making better decisions. We identify three levels on which data can generate value, and we assess their potential. To this end, we present data-driven solution methods based on Machine Learning and Quantile Regression that do not require the assumption of a specific demand distribution. We provide an empirical evaluation of these methods with point-of-sales data for a large German bakery chain. We find that Machine Learning approaches substantially outperform traditional methods if the dataset is large enough. We also find that the benefit of improved forecasting dominates other potential benefits of data-driven solution methods.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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