Asset management databases are widely employed to help close the maintenance/sustainment gap and to more proactively identify potential risks. However, current data models often use population averages to make predictions, which can overlook individual asset performance over its lifespan. Recent research at the Air Force Institute of Technology investigated using stepwise asset condition forecast models to develop better predictions
