4.7 Article

Trade Credit, Risk Sharing, and Inventory Financing Portfolios

期刊

MANAGEMENT SCIENCE
卷 64, 期 8, 页码 3667-3689

出版社

INFORMS
DOI: 10.1287/mnsc.2017.2799

关键词

trade credit; supply chain management; supply contract; inventory management; newsvendor; operations-finance interface; financial constraint; capital structure

资金

  1. London Business School
  2. University of Chicago Booth School of Business

向作者/读者索取更多资源

As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain coordination and inventory management. Using a model that explicitly captures the interaction of firms' operations decisions, financial constraints, and multiple financing channels (bank loans and trade credit), this paper attempts to better understand the risk-sharing role of trade credit-that is, how trade credit enhances supply chain efficiency by allowing the retailer to partially share the demand risk with the supplier. Within this role, in equilibrium, trade credit is an indispensable external source for inventory financing, even when the supplier is at a disadvantageous position in managing default relative to a bank. Specifically, the equilibrium trade credit contract is net terms when the retailer's financial status is relatively strong. Accordingly, trade credit is the only external source that the retailer uses to finance inventory. By contrast, if the retailer's cash level is low, the supplier offers two-part terms, inducing the retailer to finance inventory with a portfolio of trade credit and bank loans. Further, a deeper early-payment discount is offered when the supplier is relatively less efficient in recovering defaulted trade credit, or the retailer has stronger market power. Trade credit allows the supplier to take advantage of the retailer's financial weakness, yet it may also benefit both parties when the retailer's cash is reasonably high. Finally, using a sample of firm-level data on retailers, we empirically observe the inventory financing pattern that is consistent with what our model predicts.

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