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Managing default risk under trade credit: Who should implement Big-Data analytics in supply chains?

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  • Tsao, Yu-Chung

Abstract

This paper considers a supplier–retailer channel in which providing trade credit to customers incurs default risk. Big-data analytics (BD-A) could be used to mitigate default risk. The aim is to identify the party that should implement BD-A in the supply chain. Our results indicate that when the retailer (supplier) is dominant in determining the credit period, the retailer (supplier) prefers to implement BD-A unilaterally if the optimal BD-A effort is higher than a threshold. The credit period, quantities ordered, and BD-A effort increase when BD-A effort cost is shared.

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  • Tsao, Yu-Chung, 2017. "Managing default risk under trade credit: Who should implement Big-Data analytics in supply chains?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 276-293.
    • Handle: RePEc:eee:transe:v:106:y:2017:i:c:p:276-293
    DOI: 10.1016/j.tre.2017.08.013
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    5. Chen, Xi & Wong, Tse Chiu, 2021. "Application of social media data in supply chain management : A systematic review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 499-523, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    6. Xu, Song & Fang, Lei, 2020. "Partial credit guarantee and trade credit in an emission-dependent supply chain with capital constraint," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    7. Zhen, Xueping & Shi, Dan & Li, Yongjian & Zhang, Chu, 2020. "Manufacturer’s financing strategy in a dual-channel supply chain: Third-party platform, bank, and retailer credit financing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    8. Johari, Maryam & Hosseini-Motlagh, Seyyed-Mahdi, 2022. "Evolutionary behaviors regarding pricing and payment-convenience strategies with uncertain risk," European Journal of Operational Research, Elsevier, vol. 297(2), pages 600-614.
    9. Bi, Gongbing & Wang, Pingfan & Wang, Dujuan & Yin, Yunqiang, 2021. "Optimal credit period and ordering policy with credit-dependent demand under two-level trade credit," International Journal of Production Economics, Elsevier, vol. 242(C).
    10. Johari, Maryam & Hosseini-Motlagh, Seyyed-Mahdi & Nematollahi, Mohammadreza & Goh, Mark & Ignatius, Joshua, 2018. "Bi-level credit period coordination for periodic review inventory system with price-credit dependent demand under time value of money," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 270-291.
    11. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
    12. Lou, Yaqi & Feng, Lipan & He, Shuguang & He, Zhen & Zhao, Xiukun, 2020. "Logistics service outsourcing choices in a retailer-led supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    13. Schoenherr, Tobias, 2023. "Supply chain management professionals’ proficiency in big data analytics: Antecedents and impact on performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    14. Dariusz Nowak & Sławomir Gorczynski, 2020. "A Qualitative Approach to Trade Credit in Business Organisations," Business Management, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 1 Year 20, pages 25-41.

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