A Unified Multi-Hazard Data Ingestion and Risk Inference Engine for Global Supply Network Stress Testing
Keywords:
Global supply chains; risk prediction; data engineering; artificial intelligence; machine learning.AI-Driven Supply Chain Analytics;Predictive Risk Modeling;Data Pipeline Automation;Machine Learning for Supply Chain Resilience;Real-Time Supply Chain Monitoring;Big Data Integration for Logistics;Intelligent Risk Forecasting;Distributed Data Engineering Architecture;Supply Chain Disruption Prediction;AI-Powered Decision Support Systems.Abstract
Supply chain risk prediction supports decisions on investment, protection, and recovery against disruptions. However, the prediction of such risks at a global scale remains underdeveloped. Artificial-intelligence-enhanced data engineering frameworks can contribute by defining relevant risks, specifying architectures and components for risk-analytics drivers, and identifying metrics for predictive performance. Informed by these contributions, AI-enhanced data-engineering frameworks offer a step towards meeting the prediction challenge.
A supply chain risk prediction framework elaborates general-purpose prediction pipelines using real-time data streams and multimodal data fusion for demand, supply, and logistics risks. Data pipelines for crisis risk signals incorporate geopolitical, economic, and weather-related predictors. Global pipelines remain sensitive to prediction quality for individual geographies. Hence, prediction performance metrics encompass accuracy, degree of calibration, area under the receiver operating characteristic and precision-recall curves, and risk-relevant decision-analytic metrics. Guidelines for robust, generalizable, and calibrated predictions cover stress testing, cross-domain evaluation, domain-adaptation concerns, and uncertainty quantification
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