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cover image INFORMS Journal on Applied Analytics

Volume 56, Issue 1

January-February 2026

Pages 1-112, ii

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Accepted:November 07, 2025
Published Online:January 30, 2026

Cite as

Amitabh Sinha, Jeremy Agte, Russell Allgor, Cristiana L. Lara, Ashish Agiwal, Semih Atakan, Lourdes Campo, Tolga Cezik, Daniel Chen, Qi Chen, Jesse Fischer, Eitan Gor, Nader Kabbani, Ryan Kennedy, Kaushik Krishnan, Yuan Li, Shahbaaz Mubeen Mamadapur, Tanmay Mathur, Nick McCabe, David Mildebrath, Eric Powell, Andrea Qualizza, Denton Schroeder, Nityansh Seth, Xiaoyan Si, Kaushik Sinha, Darren Stegner, Jun Xiao, Ling Zhang, Shanshan Zhang, Jikai Zou (2026) Regionalize and Scale: Amazon’s Fulfillment Network Design for Faster and Cheaper Delivery. INFORMS Journal on Applied Analytics 56(1):23-41.

https://doi.org/10.1287/inte.2025.0295

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Acknowledgments

This paper describes the scientific research and modeling of the regionalized network, leveraging several tools. However, it builds on substantial prior research at Amazon, as well as mathematical modeling tools that have been built by many scientists and developers over the years. More importantly, translating the research into impact was the result of countless hours spent by operations managers, engineers, and associates on network configuration changes, software updates, building operations changes, troubleshooting, rapid problem solving, and creative thinking. The authors are immensely humbled by, and grateful to, the numerous people who enabled this success. The authors are also thankful for the leadership support they received, which included direction, encouragement, disambiguation, and unwavering trust in their mathematical modeling. Lastly, the authors are thankful to the Amazon internal review and support teams who helped them prepare this paper (including Legal, Public Relations, and Investor Relations), as well as their Edelman coaches for their feedback and support.

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Regionalize and Scale: Amazon’s Fulfillment Network Design for Faster and Cheaper Delivery

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