Data-driven Inventory Management under Customer Substitution

29 Pages Posted: 2 Jul 2020

See all articles by Sebastian Müller

Sebastian Müller

University of Mannheim, Business School

Jakob Huber

University of Mannheim - Data and Web Science Group

Moritz Fleischmann

University of Mannheim, Business School

Heiner Stuckenschmidt

University of Mannheim - Data and Web Science Group

Date Written: June 10, 2020

Abstract

Most retailers that sell perishable goods offer multiple products in a product category (e.g., fresh food or fashion). Managing the inventories of these products is especially challenging due to frequent stock-outs and resulting substitution effects within the category. Furthermore, the true demand distributions of products are usually unknown to the decision maker. New digital technologies have enormously expanded the availability of data, storage capacity, and computing power and may thereby help improve inventory decisions. In this paper, we present a novel solution approach for the multi-product newsvendor problem. Our method is based on modern machine learning techniques that leverage large available datasets (e.g., data on historical sales, weather, store location, and special days) and are able to take complex substitution effects into account. We empirically evaluate our approach on two real-world datasets of a large German bakery chain. We find that our data-driven approach outperforms the model-based benchmark on the first dataset and performs competitively on the second dataset.

Keywords: inventory, multi-product, substitution, machine learning

Suggested Citation

Müller, Sebastian and Huber, Jakob and Fleischmann, Moritz and Stuckenschmidt, Heiner, Data-driven Inventory Management under Customer Substitution (June 10, 2020). Available at SSRN: https://ssrn.com/abstract=3624026

Sebastian Müller (Contact Author)

University of Mannheim, Business School ( email )

University of Mannheim, Business School
P.O. Box 10 34 62
Mannheim, 68131
Germany

Jakob Huber

University of Mannheim - Data and Web Science Group ( email )

L 5, 2 - 2. OG
68161 Mannheim
Germany

Moritz Fleischmann

University of Mannheim, Business School ( email )

University of Mannheim
P.O. Box 10 34 62
Mannheim, 68131
Germany

Heiner Stuckenschmidt

University of Mannheim - Data and Web Science Group ( email )

L 5, 2 - 2. OG
68161 Mannheim
Germany

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