Forecasting Models for Predicting Global Supply Chain Disruptions in Trade Economics

Global supply chain disruptions have evolved into a critical challenge for trade economics, and have caused them to reach across industries and economies around the globe. The ability to foresee these disruptions is crucial for policymakers, businesses, and supply chain managers who want to develop actionable strategies for stability. The current document focuses on analyzing the potential application of forecasting models to predict global supply chain disruptions and their efficacy and limitations. A comparison of statistical, machine learning, and hybrid models is performed, and the best methods for predicting disruptions arising from geopolitical events, pandemics, natural disasters, and other external factors are identified. The study considers real-world datasets and various scenario analyses to provide actionable insights. The key findings were obtained by integrating various sources of information, including trade volume fluctuations, transportation bottlenecks, and economic indicators, into predictive frameworks. It is thus a novel contribution to the field of study done by this research to build up an advanced forecasting model that can boost the resilience level and elasticity level of global supply chains, finally playing a key role in the sustainability of trade economics.

@article{Fu2025,
  title     = {Forecasting Models for Predicting Global Supply Chain Disruptions in Trade Economics},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Limei Fu},
  doi       = {10.14569/IJACSA.2025.0160289},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160289}
}