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Data-driven optimization models for inventory and financing decisions in online retailing platforms

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Abstract

With data-driven optimization, this study investigates the sellers’ inventory replenishment and financial decisions, and lenders’ interest rate decisions in online retailing platforms. Moreover, we focus on the annual large-scale promotion, which requires massive capital in a short period. While scholars studying the data-driven inventory replenishment problem hardly consider capital-constrained sellers, these problems are important because the seller’s capital level can significantly influence the order quantity and generate different effects on inventory management. Hence, we propose two novel data-driven game-theoretic approaches (including separated and integrated methods) using machine learning and deep learning methods to optimize inventory replenishment and financial decisions for the sellers who obtain financial support from the online platform. Moreover, we propose a data-driven game-theoretic model for the online platform to optimize their interest rate considering the market potential. We explore the real retailing transaction data containing 199,390 weekly sales records. We find that the seller and lender can benefit when the seller chooses integrated machine learning and quantile regression method. However, we find that only a low capital level can motivate the seller to choose to borrow from the lender. Interestingly, our results also suggest that the lender has the motivation to build a data-driven system that helps sellers optimize inventory decisions. Our work identifies the optimal interest rate and inventory decision under the data-driven method. We propose data-driven decision support tools by evaluating the values of both the lender’s and the seller’s profit and provide new management insights on joint inventory and financing decisions.

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Acknowledgements

This work was supported by the National Science Foundation of China (Grant No. 71620107002, 71821001 and 71971095).

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Authors and Affiliations

  1. School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China

    Bingnan Yang & **anhao Xu

  2. AIM Institute, Emlyon Business School, 23 avenue Guy de Collogue, 69130, Ecully Cedex, France

    Yeming Gong

  3. ESCP Business School, 11 av. de la République, 75011, Paris, France

    Yacine Rekik

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  1. Bingnan Yang
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  2. **anhao Xu
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  3. Yeming Gong
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  4. Yacine Rekik
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Correspondence to Yacine Rekik.

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Appendix A: The historical transaction data of the seller

Appendix A: The historical transaction data of the seller

See Fig.11.

Fig. 11
figure 11

Demand of seller

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Yang, B., Xu, X., Gong, Y. et al. Data-driven optimization models for inventory and financing decisions in online retailing platforms. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05234-4

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