An evolutionary game model for indirect data sharing in manufacturing big data consortium

In the context of the explosive growth of manufacturing data, a manufacturing big data consortium is a collaborative organization aimed at promoting efficient decision-making and technological innovation. It is of vital significance to study how to effectively promote the data sharing among the members of manufacturing big data consortium. In this paper, we propose an evolutionary game model to investigate how to facilitate data sharing in the manufacturing big data consortium. Then, we analyze the synergistic revenue allocation method based on the indirect data sharing mode, and calculate the probability of the ideal event. We explore the influencing factors of data sharing in the manufacturing big data consortium by simulating Evolutionary Stable Strategies. Our findings reveal that the initial willingness of participants has a profound impact on their sharing behavior. Furthermore, the optimal interval revenue allocation coefficient can improve the probability of the manufacturing big data consortium to share data. Factors such as incentive revenue and social penalties positively influence the evolution towards active sharing strategies. In contrast, factors such as speculative revenue and data sharing costs inhibit the evolution towards active sharing strategies.