Supply chain information sharing and collaborative innovation based on social network analysis

In order to improve the completeness of supply chain information sharing and shorten the sharing time, a supply chain information sharing and collaborative innovation method based on social network analysis is proposed. Firstly, through dynamic programming and cost function optimisation, the optimal cluster of supply chain information is divided. Secondly, generate anonymous sequences of supply chain information through social network analysis and dynamic grouping strategies. Once again, establish a node reputation evaluation system that combines direct and indirect reputation to achieve secure sharing of supply chain information. Ultimately, leveraging social network analysis, the supply chain's information sharing mechanism is refined, fostering synergistic collaboration between resources and competencies. Additionally, network governance structures are crafted to enhance collaborative innovation within the supply chain. Empirical outcomes reveal that the proposed approach in this study achieves a 0.97 completeness rate for supply chain information sharing, concurrently reducing the duration required for information dissemination.