Optimizing the credit term decisions in supply chain finance. Issue 2 (March 2019) - Li, Haitao; Mai, Liuqing; Zhang, Wenlong; Tian, Xiangyu
Abstract: We study a credit term determination problem in the context of a supplier-buyer supply chain. The supplier's credit term decision is simultaneously made with its production and inventory decisions, and most importantly, it is impacted by the buyer's order quantity. We present a new game-theoretic framework to model this problem, which captures the interaction between the supplier's credit term decision and the buyer's order decision in a multi-period setting. An exact method based on nonlinear programming is implemented to obtain the optimal solutions. We apply our methodologies on a real world case. The computational results show that our approach significantly outperforms the heuristics with fixed credit terms, and either a short or a long credit term can be sub-optimal for the supplier in profitability. Our work offers the first data-driven model and solution approach that assists purchasing and supply managers to make optimal dynamic credit term decision in conjunction with production, ordering and inventory decisions in a game-theoretic setting. Highlights: A new game-theoretic framework is developed to optimize the credit term in a supplier-buyer supply chain. Bilevel programming is employed to model the leader-follower game (Stackelberg game). A real world case study shows the benefit of our optimal credit term solution comparing with two heuristic solutions. Sensitivity analysis is performed to obtain insights about key problem parameters' impact on the optimal objective value. Our study shows that either a short or a long credit term can be sub-optimal for the supplier in profitability.