This project presents a next-generation risk management framework designed to proactively identify and mitigate long-term risks in the cell therapy supply chain. This framework leverages Natural Language Processing (NLP) to interpret large volumes of unstructured data from the media to create two data-driven scenarios. The model is trained on 13,377 news articles per country within the sponsor’s company network from 2011 to 2024, combined with structured economic and security indicators, to forecast political risk in 2027. On the basis of the publicly available country annual risk index, our model identifies Japan as facing the most significant increase in political risk, with its index rising from 10.8/100 in 2024 to 16.2 in 2027. Two scenarios are then generated to translate risk signals into operational impact. Through simulation of local geopolitical disruption, Scenario A reveals that existing mitigation strategies are insufficient: market share would decline by 20%. Building on the disruption analysis in Scenario A, Scenario B further explores the structural vulnerabilities in the digital aspect by simulating three strategic future "worst to best" orders: a reactive, compliance-driven path; a fragmented and vulnerable system; and a secure and connected supply chain. These simulations reflect different levels of digital maturity, governance, and risk readiness. Scenario B demonstrates that transitioning to a fully integrated, secure digital infrastructure could reduce inventory holding costs by up to 25%, improve service levels by 40%, and cut recovery time by more than 50%, establishing a measurable pathway to both operational efficiency and digital resilience. Together, these scenarios emphasize the importance of proactive leadership, integrated technology, and strategic planning to build a resilient supply chain.