兰德-Building-a-Healthy-MQ_131页_2mb

Summary of "Building a Healthy MQ-1/9 RPA Pilot Community"

Core Content

This report, commissioned by the U.S. Air Force, outlines the development of a long-term career field planning model for MQ-1/9 RPA pilots. The goal is to ensure a healthy and sustainable RPA pilot community by addressing career field planning issues that contribute to job dissatisfaction and stress among personnel. The model is designed to project the short-term and long-term consequences of planning decisions on the RPA force, using linear programming to determine the optimal production and crossflow levels.

Main Objectives

• To define a healthy and sustainable RPA pilot career field by incorporating desired end-state requirements.
• To develop a model that factors in retention, assignment patterns, and production levels to align the current and future RPA pilot inventory with these requirements.
• To explore different production and retention scenarios and their implications on the long-term health of the RPA community.

Key Viewpoints

• RPA pilots are crucial to mission success, but recent studies show dissatisfaction and stress, largely due to career field planning issues.
• The RPA pilot community is new and rapidly growing, with the 18X RPA pilot force only six years old. This has led to challenges in training and retention.
• The Air Force's Culture and Process Improvement Program (CPIP) has initiated 140 actions to improve RPA operations and quality of life, but a more structured and long-term planning model is needed.
• The model is not limited to current manpower needs but aims to achieve an ideal future state that includes combat-to-dwell and crew-to-line ratios, as well as developmental and leadership opportunities similar to traditional pilot communities.

Model Overview

• The model is based on linear programming and uses desired end-state requirements to guide production and crossflow decisions.
• It incorporates real-world constraints such as retention patterns, assignment norms, and production capacity.
• The model includes 17 types of duties that RPA pilots are expected to fulfill, grouped into line flying, training, leadership, development and support, and other development and support.
• The model also considers crossflow levels, using historical data from other rated communities to set benchmarks for future RPA pilot recruitment.

Scenarios Explored

1. ASAP (As Soon As Possible) Scenario:

   • Focuses on rapid production to meet desired end-state requirements quickly.
   • Under low losses, most requirements are met by FY 2021, with ALFA tour pilots filling shortages in early years.
   • Development and support positions are filled by FY 2030.
   • However, this high production tempo leads to a large inventory of unassigned pilots, who lack the experience for senior roles.
   • Higher losses delay meeting requirements until FY 2040 and maintain a high inventory imbalance.

2. Balanced Scenario:

   • Takes a long-term, holistic approach to career field planning.
   • Achieves the desired end-state requirements within a year of the ASAP scenario.
   • Lower annual production is possible if retention policies are effective.
   • Poor retention can lead to unfilled development and support duties, as inexperienced pilots cannot be assigned to these roles.
   • This scenario is more sustainable and aligns better with the Air Force's long-term vision.

Impact of Growth in Desired End-State Requirements

• Increased operational demands and manpower needs will require a larger RPA pilot community in the long term.
• The model explores three growth scenarios:
  • Increased combat-to-dwell ratios
  • Increased crew-to-line ratios
  • Increased number of active duty combat lines
• Despite higher personnel needs, the timeline to meet requirements remains relatively short (by 2031 in all scenarios).
• Retention monitoring and anticipating growth are critical to achieving a healthy and sustainable RPA pilot community.
• If 18X retention is better than 11U, the RPA community could support 10–20 more lines by 2031.
• Poor 18X retention necessitates higher production and fails to meet traditional pilot benchmarks.

Model Use in Policy Analysis

• The model is designed to support policy decisions by providing insights into production tempo, crossflow policies, retention incentives, and assignment patterns.
• It helps in understanding how current and future staffing levels impact force health and sustainability.
• The model can be adjusted in the future based on updated priorities and data from the Air Force.

Key Technical Aspects

• The model uses historical data and expert input to define retention patterns and assignment norms.
• It includes weighted objectives to prioritize meeting desired end-state requirements while minimizing excess production and crossflows.
• Constraints are included to reflect real-world limitations and policy preferences.

Conclusion

• A balanced approach to career field planning is more sustainable and effective in meeting long-term RPA pilot needs.
• Retention policies are crucial in ensuring that the RPA community can support senior roles and developmental assignments.
• The model serves as a tool for policy analysis and force planning, helping the Air Force to manage growth and stress in the RPA pilot community.

Appendices

• Appendix A includes interview participants and themes discussed with Air Force leaders.
• Appendix B provides senior leader and SME perspectives on the RPA career field.
• Appendix C details model inputs and assumptions.
• Appendix D explains the RPA model formulation.
• Appendix E explores the optimal mix of 18X, 11U/12U, and ALFA tour pilots.
• Appendix F discusses alternative assignment patterns and desired end-state requirements.
• References provide a list of sources used in the study.