The Army is currently in the midst of a recapitalization (RECAP) program that calls for the rebuilding and selective upgrading of 17 systems. Because this program's plans for the scale, scope, and type of RECAP for each of these systems have been evolving over time, the program may benefit from additional information about the relationships between Army vehicle ages and operating costs and the practical implications of those relationships. In this study, we analyzed the effects of vehicle age and other factors (such as usage, initial odometer reading, and location) on repair costs and availability and embedded our results in a spreadsheet-based vehicle replacement model used to estimate optimal replacement or RECAP age for a specific model fleet. Several prior studies that looked at vehicle age-cost relationships used such fleet-level Army data as average fleet age and total operations and maintenance (O&M) spending for a fleet. Our study used vehicle-level data, which may provide a more complete picture of aging effects. We focused on the high-mobility multipurpose wheeled vehicle (HMMWV) because of the wide age range of HMMWVs in the Army fleet, the fact that the Army has placed a high priority on HMMWV RECAP, and the HMMWV's critical role in ongoing operations.
Improving Recapitalization Planning: Toward a Fleet Management Model for the High-Mobility Multipurpose Wheeled Vehicle
2008
93 pages
Report
No indication
English
Logistics Military Facilities & Supplies , Transportation , Business & Economics , Military vehicles , Decision support systems , Downtime , Replacement , Repair , Cost estimates , Management planning and control , Aging(Materials) , Trucks , Army equipment , Availability , Regression analysis , Optimization , Military modernization , Mathematical prediction , Odometers , Parts , Hmmwv(High-mobility multipurpose wheeled vehicles) , Recapitalization planning , Recapitalization age , Vehicle age , Vehicle usage , Vehicle location , Vehicle variant , Repair costs , Odometer readings , Annual mileage , Replacement costs , Optimal replacement age , Annual parts costs , Annual labor costs , Annual downtime , Predictor variables