OTPTO: Joint Product Selection and Inventory Optimization in Fresh E-commerce Front-End Warehouses
Zheming Zhang, Yan Jiang, Qingshan Li, Ai Han

TL;DR
This paper introduces OTPTO, a joint optimization approach for product selection and inventory management in fresh e-commerce warehouses, improving order fulfillment rates and customer satisfaction.
Contribution
It proposes a novel multi-task OTPTO framework that jointly optimizes product selection and inventory, outperforming traditional predict-then-optimize methods in fresh e-commerce settings.
Findings
Increases full order fulfillment rate by 4.34%
Reduces gap to optimal fulfillment rate by 5.27%
Demonstrates robustness on JD.com's 7Fresh platform
Abstract
In China's competitive fresh e-commerce market, optimizing operational strategies, especially inventory management in front-end warehouses, is key to enhance customer satisfaction and to gain a competitive edge. Front-end warehouses are placed in residential areas to ensure the timely delivery of fresh goods and are usually in small size. This brings the challenge of deciding which goods to stock and in what quantities, taking into account capacity constraints. To address this issue, traditional predict-then-optimize (PTO) methods that predict sales and then decide on inventory often don't align prediction with inventory goals, as well as fail to prioritize consumer satisfaction. This paper proposes a multi-task Optimize-then-Predict-then-Optimize (OTPTO) approach that jointly optimizes product selection and inventory management, aiming to increase consumer satisfaction by maximizing…
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Taxonomy
TopicsSupply Chain and Inventory Management · Vehicle Routing Optimization Methods · Optimization and Packing Problems
MethodsALIGN
