The Data-Driven Censored Newsvendor Problem

Chamsi Hssaine
Corresponding Author
Chamsi Hssaine
[email protected]
https://orcid.org/0000-0003-3104-3575
Department of Data Sciences and Operations, University of Southern California, Marshall School of Business, Los Angeles, California 90089
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Sean R. Sinclair
Sean R. Sinclair
[email protected]
https://orcid.org/0000-0002-7011-8253
Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208
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Corresponding Author

Chamsi Hssaine

[email protected]

https://orcid.org/0000-0003-3104-3575

Department of Data Sciences and Operations, University of Southern California, Marshall School of Business, Los Angeles, California 90089

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,

Sean R. Sinclair

Sean R. Sinclair

[email protected]

https://orcid.org/0000-0002-7011-8253

Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208

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Published Online:13 Nov 2026https://doi.org/10.1287/mnsc.2024.09021

Supplemental Material

mnsc.2024.09021.sm1.pdf

The replication files for this article are available HERE.

cover image Management Science

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Received:December 18, 2024
Accepted:June 02, 2026
Published Online:November 13, 2026

Copyright © 2026, INFORMS

Cite as

Chamsi Hssaine, Sean R. Sinclair (2026) The Data-Driven Censored Newsvendor Problem. Management Science 0(0).

https://doi.org/10.1287/mnsc.2024.09021

Keywords

Acknowledgments

The authors thank the department editor, associate editor, and three anonymous referees whose comments and suggestions were tremendously helpful in improving the paper. The authors are also grateful to Vishal Gupta, Qi Luo, Will Ma, Omar Mouchtaki, Bahar Taskesen, and Linwei Xin for helpful comments on a preliminary version of this manuscript.