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Data-driven inventory management under customer substitution
Müller, Sebastian
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Huber, Jakob
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Bubak, Ralph Alexander
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Fleischmann, Moritz
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Stuckenschmidt, Heiner
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1-s2.0-S0377221725010008-main.pdf
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DOI:
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https://doi.org/10.1016/j.ejor.2025.12.034
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URL:
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https://www.sciencedirect.com/science/article/pii/...
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URN:
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urn:nbn:de:bsz:180-madoc-717028
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Document Type:
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Article
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Year of publication Online:
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2025
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Date:
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25 December 2025
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The title of a journal, publication series:
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European Journal of Operational Research : EJOR
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Volume:
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tba
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Issue number:
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tba
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Page range:
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1-18
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Place of publication:
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Amsterdam [u.a.]
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Publishing house:
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Elsevier
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ISSN:
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0377-2217
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Publication language:
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English
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Institution:
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Business School > ABWL u. Logistik (Fleischmann 2009-)
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Pre-existing license:
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 Creative Commons Attribution 4.0 International (CC BY 4.0)
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Subject:
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330 Economics
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Keywords (English):
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inventory , multi-product , substitution , machine learning
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Abstract:
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Retailers selling perishable goods typically 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 product-specific demand distributions are usually unknown to the decision maker. Digital technologies have massively expanded the available data and computing power, which can 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 locations, and special days, and are able to take complex substitution effects into account. We evaluate our approach on two real-world datasets of a large German bakery chain and find that our data-driven approach outperforms the benchmark on the first dataset and performs competitively on the second dataset. We then analyze our approach in a controlled environment with synthetic data to pinpoint the factors that determine its performance. While saving computation time, our approach outperforms relevant benchmarks for a wide range of cost parameters and substitution levels when the demand distribution depends on exogenous data; it is competitive otherwise.
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 Search Authors in
BASE:
Müller, Sebastian
;
Huber, Jakob
;
Bubak, Ralph Alexander
;
Fleischmann, Moritz
;
Stuckenschmidt, Heiner
Google Scholar:
Müller, Sebastian
;
Huber, Jakob
;
Bubak, Ralph Alexander
;
Fleischmann, Moritz
;
Stuckenschmidt, Heiner
ORCID:
Müller, Sebastian ; Huber, Jakob ; Bubak, Ralph Alexander ; Fleischmann, Moritz ; Stuckenschmidt, Heiner  ORCID: 0000-0002-0209-3859
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