Predictive Risk Modeling and Optimization for Resilient Semiconductor and Defense Manufacturing Supply Chains: A Systematic Review
Abstract
Background: Semiconductor and defense manufacturing supply chains face unprecedented disruptions from geopolitical tensions, natural disasters, and global pandemics. These critical industries require robust predictive risk modeling and optimization approaches to ensure supply chain resilience and operational continuity. Despite growing research interest, a comprehensive synthesis of methodologies, findings, and gaps in this domain remains lacking. Methods: We conducted a systematic review following PRISMA 2020 guidelines. Six electronic databases were searched (Dimension AI, Google Scholar, ArXiv, PubMed) from inception to February 2026, yielding 960 initial records. After deduplication (273 unique papers) and systematic screening with predefined inclusion/exclusion criteria, 30 studies were included in the full synthesis. Data extraction focused on study characteristics, risk modeling approaches, optimization methods, resilience strategies, key findings, and limitations. Quality assessment examined methodological rigor, data sources, and validation approaches. Results: The 30 included studies employed diverse methodologies, including stochastic programming (n = 8), machine learning and AI-driven approaches (n = 7), simulation-based methods (n = 6), multi-objective optimization (n = 5), and network-based models (n = 4). Risk modeling approaches predominantly addressed demand uncertainty (73%), supply disruptions (67%), yield variability (43%), and geopolitical risks (27%).…
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 8 participants or records.
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