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Anticipatory shipping versus emergency shipment: Data-driven optimal inventory models for online retailers

Xinxin Ren, Yeming Gong (), Yacine Rekik and Xianhao Xu
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Xinxin Ren: HUST - Huazhong University of Science and Technology [Wuhan]
Yeming Gong: EM - EMLyon Business School
Yacine Rekik: ESCP Europe - Ecole Supérieure de Commerce de Paris
Xianhao Xu: HUST - Huazhong University of Science and Technology [Wuhan]

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Abstract: The inventory levels of pickup points play an important role for the same-day or next-day pickup and delivery services. The previous inventory optimisation research usually makes an assumption about demand distribution, does not use the real dataset or consider shipping strategies for this problem. In this study, we introduce a new strategy, mixture of anticipatory and emergency shipping, and propose forecasting-optimisation integrated approach to optimise multi-items' inventories in each pickup point based on big data analysis. We explore a real dataset including 23,808,261 records with 54 pickup points and 4018 items. We first cluster the dataset based on the distances between pickup points and the warehouse, then, implement the forecasting-optimisation integrated algorithms to select the more profitable strategy for each group. The result indicates that compared with the original algorithms, our proposed approach can effectively increase the profits, particularly, the novel algorithm, Long Short-Term Memory networks – Quantile Regression, performs better. Additionally, we find that the 100% anticipatory shipping is not necessarily superior to emergency shipment, when the pickup point is farther from the warehouse, the advantage of emergency shipment is more significant. However, the mixture of anticipatory and emergency shipping can contribute to higher profits for online retailers.

Keywords: emergency shipment; forecasting; inventory management; data-driven decision; deep learning; Anticipatory shipping (search for similar items in EconPapers)
Date: 2024-04-02
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Citations: View citations in EconPapers (1)

Published in International Journal of Production Research, 2024, 62 (7), 2548-2565 p. ⟨10.1080/00207543.2023.2219343⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04325715

DOI: 10.1080/00207543.2023.2219343

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