Accurately forecasting the demand for organizational clothing and individual equipment (OCIE) and other logistical needs is challenging due to changing operational tempos, new unit activations, deployments, and equipment life cycles. These inaccurate forecasts can lead to costly overstocking of some items or critical shortages of others, impacting overall service member readiness. In some cases, such as on the Korean Peninsula, the military must also account for stocking OCIE for partnered forces, including Korean Service Corps personnel and Korean Augmentation to the United States.
The analysis of large datasets can provide new insights into relationships between variables and potentially enable better predictions of the likelihood of processes and events. How can the military use tools like predictive analytics and machine learning (ML) to capture important trends, prepare for the future, and develop a predictive model to forecast OCIE and equipment demand more accurately? Once these predictive models are established, how can they be linked into the transportation system to ship stocks quickly to the point of need? Furthermore, how can the military incorporate large language models (LLMs) and user-interface-friendly systems into its operations to better accomplish its missions, and what are the associated risks and benefits?
Beyond supply forecasting, how can these emerging technologies, such as artificial intelligence (AI) and automation, be integrated into the broader operational workflows of various Air Force units, including installation and mission support operations? What are the specific challenges in effectively transitioning to AI-driven decision-making processes within these military maintenance and logistics environments? Ultimately, how can seamless integration and optimization of AI technologies be ensured without compromising operational efficiency or mission readiness, and how can potential challenges, such as data security and compatibility issues, be successfully addressed?
- Robertson, Maj. Jordan K., "Timing the Test: An Alternative Timeline for the Air Force Officer Qualifying Test for Accessions through the Air Force Reserve Officer Training Corps," AFGC thesis, 2025.
- This topic explores how the analysis of datasets can provide insights into relationships between variables to improve military selection and training. Robertson answers this by applying predictive analysis to evaluate the validity of the AFOQT, utilizing quantitative data to demonstrate that such aptitude tests only accurately predict performance for a limited three-and-a-half-year window. Because cadets currently take the test as freshmen or sophomores, the results incorrectly forecast their success as upperclassman cadets rather than predicting their actual post-commissioning performance at technical training. By leveraging these predictive insights, she demonstrates how the Department of the Air Force can delay the test's administration so that its optimal predictive window accurately aligns with technical training milestones, rather than providing a false indicator of early success.
- Carroll, Lt. Col. Daniel R., "On Disruptive Technologies for Improving Aircraft Maintenance and Readiness," AF Fellows Paper (Argonne National Laboratory), 2025, 73 pgs.
- Hagardt, Lt. Col. Benjamin, "Artificial Intelligence and Agile Combat Employment," GCPME thesis, subsequently published in Military Review (May-June 2024). Original paper.
- Kearney-Kurt, Christine, "Overcoming Strategic Blind Spots in Airlift Operations," AFGC thesis, 2025, 35 pgs.
- She notes that airlift planners, aircrews, maintainers, and logisticians currently use separate, siloed databases to track flight hours, parts, and equipment, which prevents them from adapting quickly during crises. To fix this, she recommends utilizing AI to merge these disparate systems into a single HADR Common Operating Picture (COP). By pre-loading flight rules, maintenance schedules, and security clearances, an AI-driven manager could instantly provide planners with real-time resource availability the moment a crisis occurs.
- Lawhon, Maj. Joshua, "Making Boxes Smart: Using AI and ML to Streamline AF Logistics and Readiness," GCPME thesis, 2024, 34 pgs.
- North, Matthew, "Can Machines Have Ethics?" AFGC thesis, 2025, 44 pgs.
- North notes that while AI struggles with ethical dilemmas, it excels at numerical and language analysis in logistics and acquisitions. By transforming raw supply chain data, AI can successfully predict shortages, optimize delivery routes, and improve predictive maintenance, ultimately reducing logistics costs by 15% and decreasing product defects. Furthermore, AI can rapidly scan massive, complex acquisition contracts to ensure the warfighter receives necessary tools faster and cheaper.
- Stevenson, Maj. Dane and Andrew Clayton, "Improve Aircraft Maintenance Sortie Production Rates with Extended Reality and Artificial Intelligence Assistance in Maintenance Processes," ACSC VR/AR RTF paper, 2022, 11 pgs.
- Agnes, Allen, "Revolutionizing DoD Wargaming through AI/ML: A Comprehensive Exploration," AF Fellows paper (CSIS), 2024, 3 pgs.
- Andresen, Maj. Jonathan S., "How US Military Forces Have Utilized Artificial Intelligence and Machine Learning to Improve Operations and Mission Effectiveness," GCPME thesis, 2023, 37 pgs.
- Dacanay, Capt. Charlie Mark, "How Can Artificial Intelligence (AI) Be Utilized in the Department of Defense (DoD) to Speed Up the Procurement Process?" SOS AUAR White paper and slides, 2021.
- Ewing, Capt. Nathaniel, "Generative Artificial Intelligence Application in the United States Air Force," SOS AUAR White Paper, 2023, 6 pgs.
- Kohl, Maj. Matthew B., "The Perfect Staffer: Using Machine Learning to Streamline Operational Staff's Planning Efforts," AFGC thesis, 2024, 40 pgs.
- Lane, Charles, "Optimizing USAF Asset Management: Utilizing AI to Model Air Force Facility Sustainment as a Constraint Optimization Problem," SOS AUAR White Paper and Slides, 2021.
- Moriarty, Capt. Richard and Capt. Patrick Snyder, "The Use of Artificial Intelligence and Less Lethal Force in Installation Security," SOS AUAR White Paper and Slide, 2021.
- Montoro, Lt. Col. Joseph G., "Swift Sustainment is Key to Effective Logistics," AWC Strategic Studies Paper, 2024, 28 pgs. Winner of the AWC Senior Leadership Innovation Award
- Schmitz, Samuel J., "Infrastructure Sustainment Warfare: Technology Integration to Keep Established Infrastructure in the Fight," AF Global College thesis, 2024, 44 pgs.
- Silverman, Lt. Col. Michael J., "Barriers to Artificial Intelligence Implementation in the Military Healthcare System," AWC Strategic Studies Paper, 2024, 40 pgs. Winner of the AWC Air Force Cyber Award
- Smith, Lt. Col. Christopher J., "Embracing the Inevitable: Integrating LLM into PME," AWC paper, 2025, 46 pgs.
- Twyford, Cameron S., "Bridging the Gap: Innovative Approaches to Air Force Real Property Sustainment Project Execution," AF Global College, 2025, 50 pgs.