Methodological Approaches to the Application of Predictive Analytics for Risk Minimization in Global Supply Chains

Citation: Priyam Das, "Methodological Approaches to the Application of Predictive Analytics for Risk Minimization in Global Supply Chains", Universal Library of Innovative Research and Studies, Volume 03, Issue 02.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article is dedicated to the analysis of methodological approaches to the application of predictive analytics for risk minimization in global supply chains. The relevance of the study is determined by the increasing volatility of international logistics networks, the growing complexity of supplier ecosystems, and the need for anticipatory mechanisms capable of detecting disruption signals before operational failures emerge. The scientific novelty lies in the analytical interpretation of predictive analytics as an integrated methodological framework combining machine learning, probabilistic modeling, simulation techniques, and distributed data architectures in supply chain risk governance. The work describes structural transformations in risk detection mechanisms, studies the interaction between predictive monitoring and decision support systems, and examines how predictive models reshape the temporal horizon of supply chain management. Special attention is paid to deep learning forecasting models, IoT-driven predictive infrastructures, federated machine learning architectures, and causal machine learning approaches for disruption mitigation planning. The work sets the goal of systematizing methodological approaches to predictive risk identification and evaluating their operational implications in global supply chain management. To solve this task, methods of comparative analysis, synthesis of scientific sources, structural interpretation, and analytical generalization were applied. The conclusion substantiates that predictive analytics forms the technological foundation of anticipatory supply chain governance. The article will be useful for researchers in supply chain analytics, logistics management specialists, and experts developing digital risk management systems.

Keywords: Disruption Prediction, Predictive Analytics, Supply Chain Risk Management, Machine Learning Forecasting, Supply Chain Resilience.

https://doi.org/10.70315/uloap.ulirs.2026.0302014