In recent years, with the booming development of the B2C cross-border e-commerce industry, the supply chain of B2C cross-border e-commerce faces many potential risk factors, which must be prevented. To solve this problem, this paper studies the supply chain risk factors of B2C cross-border e-commerce and puts forward avoidance strategies. First, the fitness network model of a dual local world is constructed by combining the fitness model with the local world model. Then, the fitness network model of the dual local world is verified by the risk dynamics simulation model to fit the actual network situation. On this basis, the SIRS virus transmission model is constructed to simulate the risk transmission of the B2C cross-border e-commerce supply chain, and the evaluation index system of the supply chain risk immunity ability and external risk intensity are established according to the supply chain risk immunity ability and external risk intensity. AHP is used to calculate the factors that have the greatest influence on supply chain risk immunity and external risk intensity. The results show that: from the point of view of the enterprise’s own risk immunity, it is more necessary to ensure the stability of the company’s capital and to establish a perfect risk monitoring and coping mechanism to avoid risks. From the perspective of enterprise external risk intensity, the risk should be avoided from two aspects of transaction security and logistics.

Against the background of continuously rising energy carbon emissions, accelerated energy transformation in developed countries, and increased international attention to energy security, there is still a large amount of energy consumption in the manufacturing industry. Promoting the diffusion of green supply-chain management is becoming a powerful tool to support energy transformation and energy conservation and emission reduction in the manufacturing industry. Based on this, we first conducted a scientific metrological analysis of 4960 articles in relevant fields in the Web of Science database, presenting the research status of green supply-chain management diffusion in the context of energy transformation. Second, we identified factors that affect the implementation of green supply-chain management, and analyzed the diffusion path of green supply-chain management among enterprises. Finally, based on the energy situation, enterprise operation, and implementation of environmental protection laws and regulations in Shaanxi Province, China, we determined the current situation, obstacles, and development direction of green supply-chain management diffusion of enterprises in the context of energy transformation. The research found that: at this stage, there are still some deficiencies in the research on the mechanism of green supply-chain management in the internal communication of enterprises; in the future, the diffusion of green supply-chain management can be further developed around social performance and energy transformation technology; and we can help energy transformation by strengthening policy guidance and assisting enterprise reform.

To realize the efficient decomposition and allocation of collaborative production tasks and resources among multiple enterprises, a task decomposition and allocation model for collaborative production among multiple manufacturing enterprises is proposed in a big data environment. The model is designed for the efficient and fast processing of production information using big data technology. This study innovatively applies the 5S management method to conduct data preprocessing for a manufacturing service provider and design the operation process of data cleaning and conversion to improve the efficiency of data processing. A collaborative optimization model, based on a hierarchical model with seven levels and considering time, costs, and services, is established for the task of production to achieve a reasonable match between supply and demand. Finally, the correlation coefficients of manufacturing service providers are configured according to weight order, so that the weight order is symmetrical with that of the manufacturer. The model also engages all manufacturing service providers with different production capabilities in collaborative production. The model is proved to be scientific and effective by using a specific example. In cooperative production activities, the production tasks of small and medium-sized enterprises can be effectively allocated. It can also realize efficient cooperative production among multiple manufacturing enterprises.