Corporate Strategy for Secure Semiconductor Supply Chains: ML-Driven Risk and Market Intelligence

The semiconductor industry is the foundation of modern digital economies, yet its supply chains remain highly vulnerable to systemic risks, geopolitical tensions, and global market fluctuations. This study examines how corporate strategy, when combined with machine learning (ML) driven risk modeling and market intelligence, can enhance the security and resilience of semiconductor supply chains. Using a mixed-method design, the research analyzed corporate strategy dimensions such as governance, operational resilience, sustainability, and financial adaptability alongside supply chain security parameters covering physical, digital, and systemic risks. Machine learning models, including Gradient Boosting, Random Forest, and LSTM, were applied to multi-source datasets to predict disruptions and evaluate performance metrics, while market intelligence indicators captured emerging demand trends, innovation signals, and trade risks. The findings reveal that operational resilience and financial adaptability exert the greatest impact on supply continuity, while systemic vulnerabilities remain critical due to interdependencies across global networks. Gradient Boosting emerged as the most effective predictive model, offering superior accuracy and reliability. Market intelligence further emphasized the accelerating demand for AI/IoT and automotive semiconductors, as well as the disruptive impact of tariff policies. Overall, the study highlights that secure semiconductor supply chains depend on the strategic integration of corporate decision-making with predictive analytics and intelligence-driven insights, enabling firms to achieve both resilience and competitiveness in a volatile global environment.