Optimization of Airframe Depot Readiness Assessment Model (ADRAM)

Abstract

The Naval Aviation Enterprise (NAE) is tasked with providing sufficient numbers of aircraft to meet readiness and operational surge requirements. This study seeks to improve readiness as defined by the number of mission-capable aircraft available for operational tasking. Our research is focused on the output of the Aviation Depot Readiness Availability Model (ADRAM) and seeks to inform Senior Leaders with regard to funding decisions about aircraft maintenence. Our hope is to reduce the risk of decreased aircraft readiness as well as explore the available trade space and implications of potential alternatives, revealing some practical outcomes. This project also seeks to optimize ADRAM’s ability to balance readiness with cost . The far-reaching implications of programmatic decisions concering maintaining aircraft airframes through the examination of “operational availability” is a complex problem that requires exploration. Through our examination of ADRAM output over numerous maintenance events, many depots, and many types, models, and series (T/M/S) of aircraft, we found that ADRAM does not produce the most efficient and effective set of maintenance events required to meet a specific readiness goal (or to implement a decrease in funding). There are two basic reasons for this observation: some of the model’s solution algorithms do not appear consistent with current practices, while other algorithms reflect current practices that are likely to be suboptimal. In particular, we observe that ADRAM assumes that the most expensive tasks are the first to be deferred. This does not necessarily minimize the impact of the delay on readiness. Also, ADRAM assumes that “level-loading” of maintenance tasks occurs across facilities. This may be consistent with current practices but does not necessarily allocate events to the most efficient facilities. Furthermore, ADRAM assumes “fair-sharing” of budget cuts across facilities. This does not necessarily defer events away from the least efficient facilities.