United States Air Force (USAF) aircraft parts forecasting techniques have remained archaic despite new advancements in data analysis. This approach resulted in a 57% accuracy rate in fiscal year 2016 for USAF managed items. Those errors combine for 5.5billionworthofinventorythatcouldhavebeenspentonothercriticalspareparts.Thisresearcheffortexploresadvancementsinconditionbasedmaintenance(CBM)anditsapplicationintherealmofforecasting.ItthenevaluatestheapplicabilityofCBMforecastmethodswithincurrentUSAFdatastructures.ThisstudyfoundlargegapsindataavailabilitythatwouldbenecessaryinarobustCBMsystem.ThePhysics−BasedModelwasusedtodemonstrateaCBMlikeforecastingapproachonB−1spareparts,andforecasterrorresultswerecomparedtoUSAFstatusquotechniques.ResultsshowedthePhysics−BasedModelunderperformedUSAFmethodsoverall,howeveritoutperformedUSAFmethodswhenforecastingpartswithasmoothorlumpydemandpattern.Finally,itwasdeterminedthatthePhysics−BasedModelcouldreduceforecastingerrorby2.4612.6 million worth of parts in those categories alone for the B-1 aircraft