The personnel who operate remotely piloted aircraft (RPA) are crucial in the Air Force, but many in the RPA career field are dissatisfied, and it is difficult to train and retain enough pilots to meet the demand. To assist in building a healthy, sustainable RPA career field, RAND has developed a long-term RPA career field planning model. This report explains the model's main features, content, and data inputs and describes key technical aspects.
Building a Healthy MQ-1/9 RPA Pilot Community
Designing a Career Field Planning Tool
- Is a 90% 18X mix in the career field for the MQ-9 platform obtainable by 2026?
- What is the best way to ramp up personnel to bring the career field to a healthy, sustainable state where operations tempo, retention, recruitment, job satisfaction, career development, and training become comparable to those of other established, healthy career fields?
- What is the optimum mix of 18X, 11U/12U, and ALFA tour pilots to achieve long-term career field development, health, and sustainment for the MQ-9 platform?
- What is the desired end state for the career field that accounts for senior leaders' views on health and sustainability?
Remotely piloted aircraft (RPA) and the personnel that operate them are well understood to be crucial to mission success in today's Air Force, and demand for skilled pilots continues to grow rapidly. However, recent studies suggest that personnel in the RPA pilot career field are dissatisfied with aspects of the job and are experiencing stress as a result. Although a variety of workplace factors lead to the stress and dissatisfaction, a large portion of them relate to issues associated with career field planning.
These career field planning issues exist, in part, because of the newness and rapid growth of the RPA enterprise. The 18X RPA pilot force (those whose first and only rated job is as an RPA pilot) is only six years old, and plans for the future of the career field are still evolving. Moreover, as the rapid growth in demand for 18X pilots has outpaced the Air Force's ability to produce them, the Air Force is now struggling to train and retain enough personnel to meet the demand. Recognizing that a more thoughtful and stable plan for managing the career field is needed to ensure the future health of the force, Air Force leadership asked RAND to assist in building a long-term career field planning model that addresses those force health issues and the timeline required to build a healthy, sustainable career field. This report documents RAND's efforts to develop that model; explains its main features, underlying content, and data inputs; and describes its key technical aspects.
RAND Model Explores Issues Impacting Growth of the RPA Career Field
- Producing high levels of 18X RPA pilots in the short term without factoring in potential overages (to junior-level requirements) could quickly create a glut of inexperienced personnel.
- Producing moderate levels of 18X RPA pilots results in filling the desired end-state requirements in roughly the same time frame as high production, but results in a healthier experience-to-desired-end-state-requirements match under a low-loss scenario.
- Poor retention leads to unmet development and support requirements in the desired end state as the RPA pilots separate before they gain the experience required to fill these kinds of duties.
- The RPA career field currently has the capacity to grow far beyond the baseline desired end-state requirements fairly quickly and still achieve a large career field that mirrors the patterns among traditional pilots, provided that retention is closely monitored and provided that annual production numbers take into account the anticipated growth far in advance of the actual need.
Table of Contents
RPA Career Field Model Overview
Comparison of Two Production Tempos
The Impact of Growth in Desired End-State Requirements on Production and Inventory Levels
How the Model Can Be Used to Support RPA Policy Analysis
Interview Participants, Themes, and Questions
Senior Leader and Subject-Matter Expert Perspectives
Complete Details on the Model Inputs and Assumptions
RPA Model Formulation
Optimal Mix of 18X, 11U/12U, and ALFA Tours
Alternative Assignment Patterns and Desired End-State Requirements