GAO Report Recommends Improved Staffing and Support for Air Force RPA Programs
The criticality of and demand for Remotely Piloted Aircraft (RPA) capabilities within the Air Force is best illustrated by two statistics: in 2000, the Air Force flew less than 10,000 combat flying hours and almost no combat lines using RPAs. By 2019, these numbers had multiplied to 4 million combat flying hours and 60 RPA combat lines (In Air Force terminology, a ‘combat line’ is “is the measure of the capability to provide near-continuous 24-hour flight presence of an RPA over a specific region on Earth, to include time flying to and from a specific target area.”) The proliferation of the RPA program has generated new dependencies and important issues concerning training, staffing, and operator wellbeing.
In a report published on June 25, 2020, the Government Accountability Office (GAO) released its findings of a review conducted in relation to current challenges facing the Air Force RPA community. The report came in the wake of several prior reviews into the subject and, on this occasion, specifically addressed three key issues: the extent to which the Air Force (1) met its overall RPA pilot and sensor operator staffing targets and tracked its progress in implementing its combat-to-dwell policy; (2) identified and met its RPA pilot and sensor operator instructor staffing levels at its RPA formal training unit; and (3) addressed quality of life issues affecting its RPA workforce. The study involved a review of several key metrics including staffing patterns and targets, and retention incentives and goals, in addition to consideration of topics such as training, quality of life, health and wellness, and other aspects of RPA pilot and sensor operator life. The report and these recommendations provide insight into some of the key issues currently facing the Air Force RPA program – and provide indications of future requirements in training, retention, and operator wellbeing. Specifically, the report provides insight into specific challenges plaguing the RPA community and offers opportunities for the DIB to work jointly with the Air Force to address them.
In summary, GAO found that the Air Force did not have enough pilots and sensor operators – as well as instructors for these roles – to meet existing staffing targets. Despite increases in the authorized number of RPA-related positions and the offering of financial incentives for retention, the Air Force had managed to meet accession targets only once between 2015 and 2019. Several reasons were attributed as causes for these retention issues:
Senior leaders at one RPA base that we visited told us that not having dwell time as a break from constant combat operations negatively impacts RPA personnel resiliency and retention. They said that to get a break from combat operations, RPA personnel turn to the Air National Guard or separate. They noted that people join the Air Force to see and do things, not to be exposed to constant combat operations in less than appealing locations. Further, according to RPA officials, personnel stated in exit interviews that they wanted more temporary duty opportunities, deployments, exercises, and other opportunities for better career development.
Other officials also pointed to the lack of leadership opportunities among RPA pilots. One noted that there were ‘hundreds of [RPA] pilots’ at Creech Air Force Base, but only one wing commander. Similar issues plagued the retention of sensor operators – with the prime being “the perception among sensor operators that private contractors pay more than the Air Force”. This, in the opinions of several officials, led to dissatisfaction and resulted in the Air Force becoming a pipeline to contractors.
Despite these issues, the review found that the Air Force had no reliable metric off tracking “its overall progress in accessing and retaining enough RP personnel to implements its combat-to-dwell policy, which is intended to balance RPA units’ time spent in combat with non-combat activities.” Without such a metric, there was no to evaluate the progress of the Air Force in meeting its targets to fully implement combat-to-dwell by 2024 – and to understand what adjustments were needed to meet these targets. According to the GAO report: “Absent such action, a key component of the Air Force’s workforce will not be well-positioned to meet its mission for the nation”.
Within this context, GAO recommended “that the Air Force establish a comprehensive metric (or set of metrics) to track the progress of its efforts to access and retain enough RPA personnel needed to implement its combat-to-dwell policy”. In addition, the GAO report also recommended updating the number of required RPA instructor positions as, despite changes to training length (49 days to 83 days), no corresponding adjustments to instructor requirements were made.
The GAO review also considered progress made by the Air Force on various initiatives aimed at improving the quality of life for RPA operators and found that, despite progress, several gaps in execution and coverage existed. For instance, GAO flagged unsatisfactory progress on issues like crew rotational frequency, base childcare facilities, base chaplain facilities, and support for families. These concerns must be seen in context of what GAO called were ‘long-standing issues’ affecting the RPA Community. In previous reports, GAO identified these to include: (1) long hours and low manning; (2) frequently changing shift work and shift changes; (3) geographically undesirable locations; (4) limited base resources and rural settings; and (5) human-machine interface difficulties such as poor ergonomics and temperature control of work stations. These factors only serve to exacerbate concerns relating to retention discussed above.
While several of these challenges will require concerted and systemic reform from within the Air Force, others present opportunities for the development of innovative software and hardware solutions – particularly those relating to operator wellbeing with reference to ergonomic and related concerns relating to the human-machine interface.