Causal AI in Predictive Maintenance
Reducing unplanned equipment downtime is a top priority for manufacturing and heavy industries, especially given its high cost—estimated at $50 billion annually. Traditional predictive maintenance methods, reliant on historical data, often fall short. Our latest use case explores a game-changing solution: causal digital twins.
This advanced technology unravels the complex cause-and-effect dynamics impacting equipment, enabling more precise and timely maintenance schedules. With heightened model transparency and expert-driven insights, causal digital twins offer a robust, cost-efficient approach to predictive maintenance. Discover how this technology can elevate your maintenance strategies and give you a competitive edge.