Research on the big data information sharing in closed-loop supply chain with triple-channel recycling

Abstract

Based on big data techniques to improve recycling efficiency and uncertain market information on whether manufacturers share, we construct a closed-loop supply chain where a manufacturer, a retailer, and a third-party collector compete for recycling at the same time. From the perspectives of manufacturer monopoly information market (Model-M), manufacturer and retailer share information (Model-MR), manufacturer and third-party collector share information (Model-MT), and supply chain tripartite shared information (Model-MRT), we build four types of Stackelberg game models dominated by the manufacturer to analyze the optimal strategies of the manufacturer in the four models and conduct numerical analysis to verify the effectiveness of the models. Research shows that as competition intensifies, the negative impact of big data technology costs on manufacturer decision-making and profitability diminishes. Furthermore, when the competitive intensity of recycling is wild, the optimal decision for the manufacturer is to share information only with the retailer. While competition is intense, the optimal strategy for the manufacturer is information monopoly. However, it is not always optimal for the manufacturer to share information with the third-party collector.