Dynamic discounting program of supply chain finance based on a financial information matching platform

Abstract

The dynamic discounting (DD) program based on the financial information matching platform (FIMP) is an emerging but underexplored supply chain finance (SCF) model. This study explores the configuration and operation of the DD program, compares and analyzes the DD decision between the traditional model and the FIMP-based DD model, and reveals how the latter model improves the cash flow of participants. Compared with the traditional model, the FIMP-based DD model can improve the accuracy of the daily discount rate for small and medium-sized enterprises; it offers enterprises more capital sources and helps participants determine an optimal payment period. There are three major contributions of this study. First, it explores the mechanism of the platform in promoting the DD model. The DD program is not participated in by a single buyer and supplier, but by one buyer with multiple (N) suppliers based on an FIMP. Once the fundamental decision parameters can be met, several suppliers can enjoy early payment from a specific buyer, thereby improving liquidity of the benefitting participants. Second, the study offers important managerial insights. Suppliers can calculate the discount rate to motivate buyers’ early payment choices. Besides, early payments could be constituted from a mixed portfolio with buyer’s own capital and that from financial institutions. Third, the study explores key parameters. The discount rate is not only related to the cost of working capital but also to the financing costs. Based on the varying relationships between discount rate and capital costs, the present study makes a reasonable argument for the use of mixed funds and then calculates the optimal early payment period. Finally, this study conducts a sensitivity analysis with numerical examples, which finds that, by adopting the FIMP-based DD program, both buyers and suppliers enjoy a fairer procurement environment and more profitable multi-lateral relationships.