Abstract
This study investigates trade credit and early payment financing in a three-party supply chain consisting of a manufacturer, a capital-constrained distributor, and a retail platform. The manufacturer or the platform provides the financing service to the distributor. Two different leadership structures are investigated, namely, platform and manufacturer leadership Stackelberg game, where the platform or manufacturer first makes the decision, respectively. Under trade credit financing, the manufacturer and the whole supply chain face loss when the commission rate increases. However, under buyer financing, they benefit from the high commission rate. Under platform leadership, the distributor, the manufacturer, and the supply chain perform better with trade credit if and only if the commission rate is small or the production cost is high, while the platform always prefers buyer financing. Under manufacturer leadership, the distributor, manufacturer, and supply chain perform better with trade credit under low production cost and commission rate. The platform prefers trade credit when production cost is in the intermediate range. By further analyzing the case that both financing channels are available and the distributor decides which one to choose, we find that the financing services competition hurts only the platform’s profits. And under certain conditions, trade credit and early payment can achieve the same performance for every supply chain member. These findings enhance our understanding of the supply chain risk allocation efficiency of trade credit and early payment financing under different leadership structures. Neither risk allocation form outperforms the other, and the relative efficiency depends on supply chain characteristics.
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Acknowledgments
This research is supported by National Key R&D Program of China under Grant No. 2019YFB1404901, the Key R&D Program of Zhejiang Province under Grant No. 2021C01104 and National Natural Science Foundation of China under Grant Nos. 72192823 and 71821002. The authors sincerely thank the editors and anonymous referees for their constructive comments to significantly improve the paper.
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Weihua Zhou is now a professor at the School of Management, Zhejiang University, China. He received his BS degree in information and electronic engineering (1999), MS degree in applied mathematics (2002) at Zhejiang University, China, and PhD degree in industrial engineering and logistics management (2007) at Hong Kong University of Science and Technology, China. His research interests are supply chain modeling and optimization, interface of operations and finance. His work has been published in Management Science, Operations Research, etc.
Yulin Hu is a PhD candidate at the School of Management, Zhejiang University, China. She received her BS degree (2018) and MS degree (2021) at the School of Economics and Management, Southeast University, China. Her main research interests include digital economy, platform economy and supply chain financing. She has published papers in journals such as International Journal of Production Research.
Tiantian Lin is now an algorithm engineer at Cardinal Operation, Shanghai, China. She received her BS degree in logistics management (2013), and PhD degree in management science and engineering (2019) at Zhejiang University, China. Her research interests are supply chain finance and inventory optimization. Her work has been published in Production and Operations Management.
Zhigang Ding is now an associate professor at the Business School of ShaoxingUniversity, China. He received his BS degree in electronics information engineering (2002) at PLA Artillery Academy, China, MS degree in management science and engineering (2005) at Hangzhou Dianzi University, China, and PhD degree in electronic business (2015) at Donghua University, China. His research interests are low carbon supply chain management and supply chain finance. He has published several papers in Chinese core journals such as Soft Science and China Soft Science, etc.
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Zhou, W., Hu, Y., Lin, T. et al. Trade Credit and Buyer Financing. J. Syst. Sci. Syst. Eng. 31, 288–312 (2022). https://doi.org/10.1007/s11518-022-5530-z
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DOI: https://doi.org/10.1007/s11518-022-5530-z