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Part of a larger RAND Project Air Force study on capability-based programming, this report introduces a revealed preference methodology to estimate the value to the United States Air Force of expediting F-15 fighter jet programmed depot maintenance (PDM). Such a valuation estimate would be useful in depot-level cost-benefit analysis. The authors rely on the fact that the Air Force has chosen to pay for intermittent PDM on F-15s to assert that F-15s must have enough value after PDM visits to justify PDM costs. Air Force expenditure data suggest that a typical fiscal year 2005 PDM visit cost about $3.2 million. Using the aircraft valuation curves consistent with PDM being worthwhile, the authors find that expediting an F-15's last PDM visit by a month must be worth at least $60,000. However, using a plausible annual aircraft valuation decline rate, they find that expediting an old F-15's last PDM visit by a month would be worth around $75,000, while expediting a new F-15's first PDM visit by a month would be worth more than $180,000. This report also explores various robustness enhancements. Consideration of aging aircraft issues, for instance, tends to increase the estimated value of expedited PDM.
This monograph describes a model for evaluating the combined capacity of organic (U.S. Air Force owned and operated) and contractor maintenance assets to meet aircraft programmed depot maintenance (PDM) workloads. The PDM Capacity Assessment Tool (PDMCAT) forecasts the average number of aircraft that will be in PDM status each year over several decades, based on the initial number of aircraft in PDM status, the physical capacity of the facility or facilities (number of docks available for conducting PDM work), the PDM induction policy (the period allowed between the completion of one PDM and the start of the next), and the minimum hands-on flow time (the minimum time it would take a facility to complete a PDM if only one aircraft were in PDM status). While not directly part of the model, the derived induction data can be used to estimate both near- and long-term obligation authority requirements for different induction policies, labor rates, and workload forecasts. To illustrate the model's operations and capabilities, we applied the model to evaluate the U.S. Air Force's current capacity for supporting KC-135 PDM and examined several options for improving both near- and long-term availability. In the process, we discovered that, while future annual fleet costs increase and availability decreases with age and workload, they do so rather less rapidly because the aircraft induction rates (the number of aircraft inducted each year) decrease as the PDM flow time increases. This leads to a less-drastic cost and availability forecast than usual.
This technical report describes the F-15 programmed depot maintenance (PDM) process as performed at the Warner Robins Air Logistics Center (WR-ALC) in FYs 2004-2006. The average WR-ALC F-15 PDM visit runs behind schedule and lasts about four months. Also, PDM can wait a long time for parts; aircraft move through PDM steps out of sequence, with missing parts catching up with the aircraft when they become available, or cannibalize other aircraft.
Using a case study based on the Army's new Stryker Brigade Combat Team, the authors explore how the Army might improve its ability to contribute to prompt, global power projection, that is, strategically responsive early-entry forces for time-critical events. The authors examine options to reach a dual goal: to initiate deployment of the right force capabilities, and then get those capabilities where they need to be as quickly as possible.
This study used the Poisson regression technique, bootstrap estimates, and the Kaplan-Meier estimates for survivor curves to determine the impact of Programmed Depot Maintenance (PDM) on weapon system availability. More specifically, these techniques estimated the effect in the number failures per year due to PDM, but the bootstrap technique also estimated the effect in the amount of downtime experienced by a weapon system due to PDM. Although the Poisson regression model did not pass the rigors of statistical testing, the Poisson regression suggested differences in the number of failures per year between PDM and no-PDM weapon systems. The results of the bootstrap estimates for the number of failures per year and amount of downtime per year showed that the no-PDM weapon systems experienced approximately 1.3 failures per year while remaining unserviceable approximately 120 days per year, and PDM weapon systems experienced approximately .3 failures per year while remaining unserviceable approximately 30 days per year. PDM reduced the number of failures per year and drastically reduced weapon system downtime per year. PDM increased weapon system availability from approximately 67 percent to approximately 92 percent.