The New Data-Driven Newsvendor Problem with Service Level Constraint

Yuqi Ye () and Xufeng Yang ()
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Yuqi Ye: Beijing Jiaotong University
Xufeng Yang: Beijing Jiaotong University

A chapter in LISS 2020, 2021, pp 1009-1023 from Springer

Abstract: Abstract In today’s data-rich world, decision makers can employ not only demand observations but also external explanatory variables (i.e. features) to solve the newsvendor problem without traditional demand distribution assumption, which has been drawing increasing attention and has derived so-called new data-driven approaches. Still in its infancy, this paper proposes an improved new data-driven method based on Sample Average Approximation and the nonparametric machine learning technique to solve the newsvendor problem with target service level constraint that is faced by the front distribution center of e-commerce enterprises. Then numerical experiments based on the real dataset of a large e-commerce enterprise are conducted to compare the performances implemented by our approach and those implemented by other well-established methods. We found that our approach can get lower surplus inventory levels while realizing higher service levels especially when the target service level is higher than 80%, which provides practical guidance for the inventory decision of the e-commerce enterprise’s front distribution center under the big data environment.

Keywords: Inventory; Newsvendor problem; Service level; New data-driven method; Nonparametric machine learning (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-33-4359-7_69

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DOI: 10.1007/978-981-33-4359-7_69

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