Application of AI for Global Transportation Planning

The United States Transportation Command and Military Surface Deployment and Distribution Command have large stores of transportation data in legacy transportation systems. How can AI help the Joint deployment and distribution enterprise apply new technology to existing data sets and processes to increase its decision advantage?

• Andresen, Maj. Jonathan S., "How US Military Forces Have Utilized Artificial Intelligence and Machine Learning to Improve Operations and Mission Effectiveness." AFGC thesis, 2023.
  • This paper explains that AI can ingest and analyze massive amounts of existing data—such as maps and real-time traffic patterns—to produce recommended options for troop and equipment movements. It highlights historical success, noting that AI applications like the Dynamic Analysis and Replanning Tool (DART) utilized intelligent agents to aid decision-makers at the United States Transportation Command. By employing automated reasoning capabilities on these data sets, the military was able to reduce the timeline for creating in-theater deployment plans from four days to just hours, demonstrating a massive leap in decision-making speed and efficiency.
• Begeman, Lt. Col. Jeremy, "Redefining Routes: An Inside Look at the Air Mobility Command Channel Mission," AFGC thesis, 2024.
  • This research examines how USTRANSCOM's Air Mobility Command can overcome rigid scheduling and asset underutilization by adopting commercial cargo practices. It points out that current operations suffer because AMC lacks the proper data tools to determine how operational factors impact payloads. By adopting advanced technologies like predictive analytics and real-time data management, AMC can gain real-time insights into demand fluctuations. This allows for dynamic route optimization and improved load factors, directly empowering the enterprise with the flexibility and decision advantage needed to respond efficiently to emergent global demands.
• Bell, Jared E., "An Artificial Intelligence-Enabled Joint Force Equals Successful Outcomes for American Influence," AFGC thesis, 2023.
  • This study emphasizes the concept of "Smart Logistics," which relies on AI to successfully apply intelligent, lean, and agile supply chains within the military and commercial sectors. The author details how these cooperative logistics networks can utilize machine learning and deep learning to enhance interlinked organizations through modern information and communication technologies. By applying AI to logistical data, the deployment and distribution enterprise can optimize travel routes, reduce fuel consumption, and effectively dispatch vehicles to increase overall competitive and decision advantage.
• Bowers, Adriana M., "One Step Ahead: A US Focus to Advance Logistics Footprint," AFGC thesis, 2024.
  • Highlighting USTRANSCOM as the DoD's focal point for global force power projection, this paper explores how multi-domain technology platforms can process enterprise data into actionable intelligence. It notes that the DoD uses platforms like Advana to fuse intelligence data sets with logistics information, making data broadly visible, understandable, and secure across the enterprise. By utilizing AI-enabled decision-support tools to illuminate logistics processes, requirements, and resources, the Joint Force Commander can rapidly synthesize and visualize the environment, creating an effective decision-making process that outpaces adversary calculus during Joint All Domain Operations.
• Buroker, Capt. Larry D., "A Survey of Digital Twin Technology and Possible Applications for the Air Force," SOS AUAR, 2021.
  • Addressing the extreme stress placed on USTRANSCOM's mobility and logistics forces, this paper explores how AI can optimize the military's incredibly massive and complex distribution network. The author proposes applying digital twin technology—an ultra-realistic virtual representation of a physical system—paired with machine learning software to manage incoming logistical data like transportation control numbers. By applying predictive analytics and machine learning to existing supply chain data, this AI-driven approach can automatically optimize routing, packaging, and warehousing, while providing decision-makers with a reliable tool to assess risks and rapidly reroute distribution networks in response to sudden environmental changes.
• Carroll, Lt. Col. Daniel R., "On Disruptive Technologies for Improving Aircraft Maintenance and Readiness," AF Fellows (Argonne National Lab), 2025. 
  • This paper addresses the organizational structures of the United States Transportation Command (USTRANSCOM) and its service components, including the Surface Deployment and Distribution Command (SDDC), which operate the Defense Transportation System (DTS) to provide global mobility to the joint force. To increase decision advantage and modernize legacy processes, the author highlights the Joint All-Domain Command and Control (JADC2) concept, which seeks to introduce artificial intelligence (AI) and expand cloud networking to break down communication barriers across services. For the global mobility enterprise, leveraging AI and better communications through the JADC2 network will directly aid efficiency, interoperability, and decision-making when commanders must balance competing global transportation requirements in dynamic environments.
• Cobian, Maj. Leonardo J., "Securing Our Artificial Intelligence: The Implications of the United States Losing the 5G War to China," AFGC thesis, 2020.
  • This research highlights that military logistics and transportation encompass a wide variety of functions that generate vast amounts of data, making it a perfect domain for AI application. The paper explains that deep learning algorithms can be applied to historical and existing data sets of previous supply requests to accurately anticipate a unit's requirements for upcoming operations. By allowing AI to analyze this legacy data, the joint logistics enterprise can proactively make decisions to position and deliver supplies ahead of schedule, acting as a lifeline that fundamentally improves the speed and efficiency of military operations.
• Griffith, Lt. Col. Timothy, "Strategic Readiness: An Assessment Methodology," AF Fellows (Kennedy), 2021.
  • Answers the question by proposing an AI-powered strategic readiness dashboard to assist U.S. Transportation Command and other combatant commands in planning deployment logistics. The paper highlights that evaluating Time-Phased Force Deployment Data (TPFDD) against unit readiness to meet operational plan (OPLAN) requirements is currently a massive, latent data undertaking. By applying artificial intelligence to synthesize these existing, disparate readiness and transportation data sets, the joint enterprise can provide senior defense leaders with visualized, real-time assessments that clearly demonstrate how their daily operational and strategic decisions impact global force management and deployment timelines.
• Hagardt, Maj. Benjamin, "Artificial Intelligence and Agile Combat Employment," AFGC thesis, 2022, 
  • Directly addresses the U.S. Transportation Command's (TRANSCOM) need for artificial intelligence to manage complex global deployment and distribution challenges. The paper explains that by integrating machine learning (ML) models with legacy Time-Phased Force Deployment Data (TPFDD), AI can rapidly assess massive logistical variables—such as cargo dimensions, aircraft weight capacities, threat profiles, and weather restrictions—to determine the most efficient transportation methods for personnel and equipment. If disruptions occur in a contested environment, the AI can automatically process these changes and reflow transportation load plans down to the exact aircraft, providing senior leaders with decision superiority by turning raw logistics data into immediate, actionable strategic movements.
• Herrill, Steven, "Addressing the Tyranny of Distance and Capacity in Air Mobility Operations in INDOPACOM," AFGC thesis, 2025.
  • Focusing on Air Mobility Command (AMC)—the air component of USTRANSCOM—this paper discusses how AI is necessary to help planners deconflict competing global lift requirements when space and capacity are constrained. It highlights emerging software solutions like DEFCON AI's ARTIV and the Next Generation Information Technology for Mobility Readiness (NITMRE) program, which utilize AI and natural language processors to synthesize vast amounts of data. By processing this information asynchronously and in real-time, these tools provide planners, aircrews, and executors with optimized courses of action, significantly easing their cognitive burden and enabling more informed, rapid decision-making.
• Leatherman, Capt. J., "Key Logistical and Infrastructure Challenges in Support of the Defense of Taiwan," SOS AUAR 2021.
  • Discusses how emerging Business Intelligence and cloud-based tracking technologies can be applied to Defense Logistics Agency (DLA) systems to improve joint distribution decisions. The paper highlights existing legacy capabilities like the Army's In-Transit Visibility (ITV), which uses Radio Frequency and Automatic Identification Technology to track equipment shipments, and suggests that integrating these systems into a secure, real-time database would allow Combatant Commanders to view available transportation and supply levels instantly. By utilizing these technologies to rapidly access data and track assets, the logistics enterprise can accelerate decision-making, automatically reroute packages to distributed troops, and maintain supply chain visibility even after hostilities commence.
• Shriver, Christopher L, "Enhancing Military Lethality: Adopting Commercial Cargo Practices to Boost Air Force Strategic Airlift Efficiency," AFGC thesis, 2025.
  • This paper addresses strategic airlift efficiency by detailing how AI-powered systems can analyze vast historical datasets—including the DoD's database of item dimensions, past load plans, and aircraft performance metrics. It notes that integrating AI into military load planning allows systems to predict optimal cargo configurations in real time, which is especially critical for unconventional and oversized military equipment. By combining this with AI-driven dynamic route scheduling that adjusts to weather, airspace restrictions, and maintenance needs, the Air Force can optimize its resources and achieve a data-driven decision advantage that maximizes aircraft utilization and speeds up deployment.
• Strabley, Maj. Joseph M., "A Contested Horizon: Conducting Logistics against a Near Peer Adversary," AFGC thesis, 2023.
  • Explores how the military's broader deployment and distribution enterprise can leverage AI to modernize its legacy supply chain systems for a decision advantage. The study recommends migrating supply chain management software to secure, third-party cloud-based services to consolidate data and provide personnel with a real-time, accurate picture of on-hand inventory. Once this data is accessible, the enterprise can apply AI algorithms—modeled after commercial logistics giants like Walmart—to predict supply and demand trends, run simulations to reroute shipments around damaged infrastructure, and establish trigger points that autonomously order and distribute critical assets before a shortage occurs.
• Wolff, Col. Jason B., "Digital Logistics under Attack," AF Fellows (Brookings), 2023.
  • This research argues for a single DoD enterprise resource planning (ERP) system—suggesting the U.S. Transportation Command as a leading candidate to manage it—that integrates AI and Machine Learning (ML) to process historical supply chain data. The paper explains that AI and ML can conduct predictive analytics to forecast future trends, monitor real-time inventory levels, and automatically identify anomalies or supply chain vulnerabilities. By automating logistics planning and demand forecasting based on these metrics, the technology provides real-time decision support to supply chain personnel, reducing human error and optimizing global logistics processes