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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References
Abbasi, B., Babaei, T., Hosseinifard, Z., Smith-Miles, K., & Dehghani, M. (2020). Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management. Computers and Operations Research, 119(104), 941.
Alan, Y., & Gaur, V. (2018). Operational investment and capital structure under asset-based lending. Manufacturing and Service Operations Management, 20(4), 637–654.
Arunraj, N. S., & Ahrens, D. (2015). A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting. International Journal of Production Economics, 170, 321–335.
Ban, G. Y., & Rudin, C. (2019). The big data newsvendor: Practical insights from machine learning. Operations Research, 67(1), 90–108.
Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press.
Buzacott, J. A., & Zhang, R. Q. (2004). Inventory management with asset-based financing. Management Science, 50(9), 1274–1292.
Cai, G., Chen, X., & Xiao, Z. (2014). The roles of bank and trade credits: Theoretical analysis and empirical evidence. Production and Operations Management, 23(4), 583–598.
Cao, Y., & Shen, Z. J. M. (2019). Quantile forecasting and data-driven inventory management under nonstationary demand. Operations Research Letters, 47(6), 465–472.
Chen, X. (2015). A model of trade credit in a capital-constrained distribution channel. International Journal of Production Economics, 159, 347–357.
Cheng, Y., Wu, D. D., Olson, D. L., & Dolgui, A. (2021). Financing the newsvendor with preferential credit. International Journal of Production Research: Bank versus Manufacturer, 59(14), 4228–4247.
Christmann, A., & Steinwart, I. (2008). Consistency of kernel-based quantile regression. Applied Stochastic Models in Business and Industry, 24(2), 171–183.
Dada, M., & Hu, Q. (2008). Financing newsvendor inventory. Operations Research Letters, 36(5), 569–573.
Dong, G., Liang, L., Wei, L., Xie, J., & Yang, G. (2021). Optimization model of trade credit and asset-based securitization financing in carbon emission reduction supply chain. Annals of Operations Research, 1–50.
Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Gao, D., Zhao, X., & Geng, W. (2014). A delay-in-payment contract for pareto improvement of a supply chain with stochastic demand. Omega, 49, 60–68.
Huber, J., & Stuckenschmidt, H. (2020). Intraday shelf replenishment decision support for perishable goods. International Journal of Production Economics, 231, 107828.
Huber, J., Müller, S., Fleischmann, M., & Stuckenschmidt, H. (2019). A data-driven newsvendor problem: From data to decision. European Journal of Operational Research, 278(3), 904–915.
Jin, W., Zhang, Q., & Luo, J. (2019). Non-collaborative and collaborative financing in a bilateral supply chain with capital constraints. Omega, 88, 210–222.
Jing, B., & Seidmann, A. (2014). Finance sourcing in a supply chain. Decision Support Systems, 58, 15–20.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.
Koenker, R., & Bassett, Jr G. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 33–50.
Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives, 15(4), 143–156.
Kouvelis, P., & Zhao, W. (2016). Supply chain contract design under financial constraints and bankruptcy costs. Management Science, 62(8), 2341–2357.
Lai, G., Debo, L. G., & Sycara, K. (2009). Sharing inventory risk in supply chain: The implication of financial constraint. Omega, 37(4), 811–825.
Lai, Z., Lou, G., Zhang, T., & Fan, T. (2021). Financing and coordination strategies for a manufacturer with limited operating and green innovation capital: bank credit financing versus supplier green investment. Annals of Operations Research, 1–35
Oroojlooyjadid, A., Snyder, L. V., & Takáč, M. (2020). Applying deep learning to the newsvendor problem. IISE Transactions, 52(4), 444–463.
Raghavan, N. S., & Mishra, V. K. (2011). Short-term financing in a cash-constrained supply chain. International Journal of Production Economics, 134(2), 407–412.
Saghafian, S., & Tomlin, B. (2016). The newsvendor under demand ambiguity: Combining data with moment and tail information. Operations Research, 64(1), 167–185.
Tao, Y., Yang, R., Zhuo, X., Wang, F., & Yang, X. (2022). Financing the capital-constrained online retailer with risk aversion: Coordinating strategy analysis. Annals of Operations Research, 1–29
Xu, X., & Birge, J.R. (2004). Joint production and financing decisions: Modeling and analysis. Available at SSRN 652562
Yan, N., Sun, B., Zhang, H., & Liu, C. (2016). A partial credit guarantee contract in a capital-constrained supply chain: Financing equilibrium and coordinating strategy. International Journal of Production Economics, 173, 122–133.
Yan, X., & Wang, Y. (2014). A newsvendor model with capital constraint and demand forecast update. International Journal of Production Research, 52(17), 5021–5040.
Yang, M. (2013). Research on supply chain finance pricing problem under random demand and permissible delay in payment. Procedia Computer Science, 17, 245–257.
Yang, S. A., & Birge, J. R. (2018). Trade credit, risk sharing, and inventory financing portfolios. Management Science, 64(8), 3667–3689.
Zhang, Y., & Gao, J. (2017). Assessing the performance of deep learning algorithms for newsvendor problem. In International conference on neural information processing, pp. 912–921, Springer.
Zhao, D., Zhang, B., & Wang, Z. (2017). A bank credit model with capital-constrained newsvendor under two ordering opportunities. Journal of Systems Science and Information, 4(5), 408–418.
Zheng, M., Shu, Y., & Wu, K. (2015). On optimal emergency orders with updated demand forecast and limited supply. International Journal of Production Research, 53(12), 3692–3719.
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This work was supported by the National Science Foundation of China (Grant No. 71620107002, 71821001 and 71971095).
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Appendix A: The historical transaction data of the 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 339, 741–764 (2024). https://doi.org/10.1007/s10479-023-05234-4
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DOI: https://doi.org/10.1007/s10479-023-05234-4