Research on Timeliness Prediction and Reliability Analysis of Cross-border Logistics

In the context of the deep integration of global supply chains, the accurate prediction of cross-border logistics timeliness and the systematic analysis of reliability have become core issues for optimizing supply chain resilience and efficiency. This study first abstracts the cross-border logistics system as a complex network with scale-free characteristics, analyzing its topological structure, multimodal coordination mechanisms, and the transmission pathways of external uncertainties, thereby clarifying the systemic causes of timeliness fluctuations. Subsequently, it constructs an engineering framework that integrates machine learning time-series prediction algorithms with multi-source heterogeneous data features, focusing on model adaptability, interpretability, and the propagation mechanisms of prediction errors. On this basis, a multi-dimensional dynamic reliability assessment system is proposed, which identifies vulnerable nodes in the network from the perspective of supply chain resilience, ultimately forming an integrated decision-support framework that links timeliness prediction with reliability assessment. This research aims to provide a systematic theoretical methodology for quantifying the performance of cross-border logistics systems, enabling proactive risk management and optimizing operational decision-making.