Asset failures in manufacturing plants can significantly disrupt production schedules and reduce productivity. To prevent such downtime, predicting when machines are likely to break down is crucial. Eugenie's digital twins provide timely alerts and performance updates to help avoid such downtimes. Manufacturers can leverage Eugenie's insights to create an optimal asset reliability strategy.

Eugenie can optimize chemical manufacturing operations by predicting future events, prescribing preventive actions, and simulating efficient solutions. This can reduce operational, legal, and reputational damage while increasing financial stability, operational certainty, safety, and yield. We can solve common issues such as low yield, excessive process waste, unoptimized energy consumption, and unplanned downtimes.

Eugenie's workflow for digital twin involves creating a strategy for prediction, simulation, and recommendation systems for complex processes with multiple data points and control parameters. Our simulator shows plant reactions to different input scenarios and generate real-time operating conditions. Our dashboard highlights process parameters causing deviation, and our AI model maintains control over all KPIs to find the best input set with just one overarching change.