Contract Design for Enhancing Green Food Material Production Effort with Asymmetric Supply Cost Information

• Main Authors: Xiaoying Li, Qinghua Zhu
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Sustainability
• Subjects: green supply chain; information asymmetry; contract design; cost sharing contract; food supply chain
• Online Access: https://www.mdpi.com/2071-1050/12/5/2119

In order to improve green performance and achieve sustainability goals, food companies see the need to adopt green supply chain management. However, ensuring a green supply is a tough task since food companies do not always have full information of their suppliers’ efforts in improving their green performance. This information asymmetry issue will lead the food producers to make poor decisions and cause a profit loss. Therefore, to fill this research gap, this study investigates a two-stage supply chain, which consists of one dominated food producer and a food supplier who has private knowledge of its green food material producing (GFMP) cost. To figure out how green performance is the major parameter that influences the decision-making of supply chain members under information asymmetry, this study first expands demand functions for both a food supplier and a producer, considering their influence on the green degree of the food products and associated consumer acceptance. It is found that under certain conditions, information sharing will improve the supplier’s green performance and increase the food producer’s profit. This study then presents the prerequisite of green cost information sharing by the food supplier. Furthermore, a newly designed menu of contracts, which combine the wholesale price contract and cost sharing contract, is proposed for the asymmetric information case to incentivize the food supplier to disclose the green effort information and improve the environmental and economic performance of the food supply chain. Numerical experiments are conducted through a case analysis to illustrate and validate the proposed models.