Integrating AI-Driven Predictive Analytics and Smart Contracts for Data-Driven Supply Chain Risk Management - Pujiati, Tri; Kamil, Mustofa; Silawati, Nur; Ikhsan, Ramiro Santiago (ADI Journal on Recent Innovation, 2025)
Global supply chains face increasing uncertainty, while traditional risk management often lacks adaptability. This study investigates how AI driven predictive analytics and smart contracts enhance resilience, using mixed methods with case studies and big data analysis. A mixed method approach was employed, combining big data analytics from supply chain networks with machine learning models for predictive forecasting, supported by case studies from multinational manufacturing and logistics companies as well as secondary data from industry reports. The findings reveal that AI driven predictive models significantly improve demand forecasting accuracy, identify potential disruptions earlier, and enhance supplier risk assessment compared to conventional approaches, while integrating data from IoT enabled devices provides real time visibility across logistics operations. Overall, AI powered predictive analytics demonstrates substantial potential in transforming risk management within global supply chains by enabling proactive strategies and resilience, allowing organizations to reduce vulnerabilities, optimize performance, and strengthen competitiveness in dynamic markets, with future research suggested to explore the integration of blockchain for transparency and ethical governance in supply chain ecosystems.