A custom RUL workflow automates the conversion of high-frequency sensor data into probabilistic failure forecasts, directly addressing the multi-million dollar cost of unplanned downtime. By implementing feature engineering pipelines and deploying survival or regression models that quantify uncertainty, you shift from calendar-based to condition-based maintenance. The operational upside comes from extending mean time between failures (MTBF) by 20-40% and enabling just-in-time parts procurement, which reduces inventory carrying costs by 15-25% while preventing production stoppages.




