In the aerospace supply chain, a complex, high-stakes ecosystem is at risk of multiple risk categories such as component shortage, cyber threats, and noncompliance with regulations. Traditional risk mitigation strategies are not enough. They are now offered as measures reactive to risks and static contingency plans. This paper investigates how AI-driven predictive risk modeling can break these limitations of the current risk management practices and allow risk management to change from reactionary to proactive across the aerospace supply chain. These models leverage the power of machine learning by poring over structured and unstructured data (telemetry data, supplier log files) and searching for patterns that predict future disruptions. Core technologies that can ingest and process data in real-time, like Apache Kafka and Apache Spark, support dynamic risk calculation. Combining with the domain expertise, they provide precision to the model and compliance framework (FAA, ITAR, AS9100) for legal compliance. The document also mentions some architectural shifts from monolith to microservice systems and the use of design patterns such as CQRS, the Strangler pattern, and ModelOps in the model deployment. Quantifiable benefits, as shown in a case study in a major aerospace OEM, include reduced downtime, decreased procurement times, and better prediction. Results suggest that stakeholders must be involved, ethical AI governance should be implemented, and iterative validation should be used to build trust and alignment in the system. Edge AI, blockchain, and quantum computing are moving in the right direction in the industry and predictive analytics. The guide is a strategic tool for converting their operation to systems with resilient and intelligent supply chains that the aerospace industry’s professionals aspire to embrace.

Predictive Modeling, Apache Kafka, Compliance, Telemetry, Microservices

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