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Top1. Introduction
Inter-enterprise cooperation has become more frequent with the development of global economic integration. There are many drawbacks in the traditional supply chain organization and management structure, such as information asymmetry, high production costs, and complex interaction processes, which have caused serious obstacles to the sustainable development of enterprises. In response to these shortcomings, enterprise collaboration is put forward and quickly applied in various industries. Enterprise collaboration can effectively improve production efficiency and reduce transaction costs, which is an effective way to enhance enterprises' core competitiveness and comprehensive strength. As an extremely complex logistics resource integration and allocation link, the supply chain is oriented to distributed and independent stakeholders. Its management risk is high. Improving supply chain resilience and realizing enterprise collaboration optimization have become the focus of current enterprise development. With the advancement of multivariate statistical data analysis and artificial intelligence theory, partial least squares and artificial neural networks have developed significantly and have been widely applied in various industries, such as stoichiometry, economic management, social sciences, and other fields, playing their unique value. In enterprise collaboration optimization, the PLS-ANN Method can provide a more accurate and detailed analysis of supply chain management data and provide strong support for interaction, cooperation, and decision-making among enterprises (Tang & Zhang, 2022) (Li et al., 2022).
With the development of industrial structure, the optimization of enterprise collaboration has increasingly become the focus of many scholars. Brodeur (2022) proposed a collaborative method model under the background of digital transformation, and took two manufacturing SMEs (small and medium-sized enterprises) in North America as the research object, providing a new perspective for the optimization of their business demand adjustment, project portfolio creation and other collaborative models. Ge (2020) explored the role of intelligent manufacturing in the enterprise structure by combining the concepts of network physical systems, big data, and the Internet of Things, and showed that intelligent manufacturing was of great significance to optimizing the enterprise’s business level and cross-enterprise global collaboration. From the perspective of enterprise knowledge collaboration, Jian (2020) suggested a multi-criteria approach based on integration and a decision optimization model to choose partners for collaborative product invention. Finally, he optimized the scheduling algorithm based on a natural heuristic algorithm. He confirmed the model's viability and effectiveness in enterprise collaboration optimization, which could support on-demand services of enterprises and access a series of services and tools from around the world. For enterprise development, it could achieve collaborative optimization and flexible resource scheduling (Belay, 2021). At present, enterprise collaboration optimization has made good progress. However, under the influence of the open market environment, enterprise collaboration also needs to make appropriate improvements. The impact of supply chain risk on enterprise collaboration optimization is not well considered in current research.