Autonomous driving, manufacturing, and connected vehicles
This application cluster focuses on using data-driven models to optimize how automotive products are designed, built, validated, operated, and sold end‑to‑end. It spans factory quality inspection, cost-aware manufacturing error prediction, predictive vehicle maintenance, resilient production and logistics planning, and dealer inventory optimization, all tied to the lifecycle of vehicles and mobility services. In parallel, it includes safety‑critical driving functions such as autonomous driving, ADAS, and test/validation automation that ensure vehicles operate safely and efficiently in the real world. It matters because automotive companies face thin margins, high capital intensity, strict safety and regulatory requirements, and growing product complexity (software‑defined vehicles, electrification, autonomy). Optimizing operations across manufacturing, fleets, and retail networks—while improving on‑road safety and performance—is a major lever for profitability and competitive differentiation. Advanced analytics and learning‑based systems enable continuous improvement under uncertainty, turning data from factories, vehicles, and markets into better decisions and more resilient operations.
This application area focuses on selecting the most effective therapy regimen for an individual patient based on their unique clinical, molecular, and functional data, rather than relying on population‑level protocols. It encompasses both predicting disease risk and progression, and—critically—matching each patient to the drugs or combinations most likely to work for them while minimizing toxicity. In functional precision medicine, this can include testing many therapies directly on patient‑derived cells and using computational models to interpret the results. It matters because traditional one‑size‑fits‑all treatment approaches lead to trial‑and‑error care, delayed or missed diagnoses, unnecessary side effects, and poor outcomes for complex, rare, or relapsed conditions like pediatric cancers. By integrating large‑scale clinical records, omics data, imaging, and ex vivo drug response profiles, advanced analytics can quickly surface optimal, personalized treatment options at scale, improving survival rates, reducing adverse events, and shortening time to effective care.