Artificial intelligence (AI) has the potential to revolutionize the execution of sustainment during multi-domain operations (MDO). Emergent technologies such as AI, hypersonics, machine learning (ML), nanotechnology, and robotics are driving a fundamental change in the character of war. As these technologies mature and their military applications become clearer, their impact has the potential to revolutionize battlefields unlike anything since the integration of machine guns, tanks, and aviation which began the era of combined arms warfare. In an era of great power competition, Russia and China continue to explore methods of stymieing U.S. military power. China has the economy and technological base, such as an independent microelectronics industry and world-leading AI development process, enough to overtake current Russian system overmatch in the next 10-15 years and become a world-class military capable of power projection. Therefore, it is prudent to focus research and development efforts in AI across all warfighting functions (WfFs), to include sustainment, to ensure the U.S. military can compete and win across all domains.
AI indicates different concepts to different audiences. As Army efforts to operationalize AI crystalize, there is value in providing a common definition for what AI is to differentiate it from related concepts such as robotics, autonomous systems, and ML. Loosely defined as intelligence exhibited by machines, AI consists of extended technohuman cognitive systems capable of significant independent action.
AI relates closely to business intelligence, a set of techniques and tools for the transformation of data into meaningful and useful information for analysis purposes. Components for study within the AI field include:
- Automated perception using a range of modalities, such as vision, sonar, lidar, and haptics
- Robotic action, such as locomotion and manipulation
- Deep reasoning, such as planning, goal-oriented behavior, and projection
- Language technologies, such as language, speech, dialog, and social nets
- Big data, such as storage, processing, analytics and inference
- ML to include adaptation, reflection, and knowledge acquisition
Stood up in February 2019, the Army’s AI Task Force is pursuing pilot projects to explore the functionality of AI. Current efforts run the gamut of accelerating adjudication of security clearances to analyze imagery for use within the intelligence WfF. Future initiatives on the horizon will explore the use of AI to streamline the defense community’s rapid prototyping process. As such, there is a necessity to aggressively explore AI’s potential to operationalize sustainment for the multi-domain fight.
The sustainment perspective can further stratify AI as the use of computers to simulate human intelligence, specifically including learning—the acquisition and classification of information, and reasoning—and gaining insights into data. At the core of AI is the ability to recognize patterns across the volume, velocity, and variety of big data and find correlations among diverse data.
Man-machine interfaces, enabled by AI and high-speed data processing, improve human decision making in both speed and accuracy. AI and ML have the potential to vastly enhance the DoD’s logistics enterprise network. Predictive analytics, demand forecasting, production scheduling, anomaly detection, and supply chain/inventory optimization are just some of the ways these technologies can enhance logistics.
According to senior Army leadership, there are seventeen major gaps in organizational structure that the Army must close prior to 2028 to field MDO-capable forces in support of U.S. European Command (EUCOM) operational requirements. Of these gaps, three are related to the sustainment WfF. AI can provide efficiencies to close these gaps and mitigate personnel increases within the force. Implementation of advanced technologies, including AI, robotics, and autonomous systems not resident in the current force on a wide scale, will require particular attention to doctrine, organization, training, materiel, leadership and education, personnel, facilities, and policy (DOTMLPF-P) developments that integrate innovative leaders, skilled Soldiers, and trained teams for the best application of these technologies to MDO formations.
The first organizational gap within the sustainment WfF is the line haul and tactical distribution of fuel. In the MDO fight against great power competitors, simulations indicate that Army forces will lose operational momentum due to extended supply lines and lack of effective fuel distribution at the tactical level. Army sustainers continue to explore how AI can inform autonomous resupply by using technology to deliver materials autonomously or semi autonomously by ground, air, and watercraft. Since 2010, researchers have predicted several applications of AI in supply chain management that could help answer this shortfall. These include setting inventory safety levels, transportation network design, purchasing and supply management, and demand planning and forecasting. A potential technological solution for this gap involves the development of cognitive technology to track and predict supply chain disruptions for fuel based on gathering and correlating external data from disparate sources such as consumption reports, social media, newsfeeds, weather forecasts, and historical data.
Sustainers in the MDO environment of 2028 and beyond can leverage AI and intelligent automation to forecast the needs of the customer unit for future operations based on the pattern of supply requests or through pre-requested supply inquiries. Sustainers can create these patterns of supply requests via machine learning or process automation. This improved process would allow units to get their supplies on time and ahead of schedule. Current algorithms built into Global Combat Support System-Army automate the process of monitoring, forecasting, auditing, and managing future requests and fulfillment between customers and suppliers. Continued improvement of these algorithms, coupled with feedback from operational units, shows promise for future AI-enabled Army enterprise resourcing platforms (ERPs).
The second organizational gap within the sustainment WfF is the lack of adequate tactical mobility within the division footprint to enable sustainment, troop movement, and survivability. Autonomous mobility kits provide the capability to retrofit select ground systems for a designated manned lead vehicle to lead a line of unmanned follower vehicles which are remotely operated by a single operator in the lead vehicle. This capability offers the ground commander options in the employment of Soldiers and the execution of sustainment convoy operations. Aided by AI, this technology has the potential to improve Soldier safety and battlefield survivability by increasing crew situational awareness and cognition while reducing vehicle collisions and driver fatigue. A reduction in vehicle accidents will result in saved lives and reduced injuries, reductions in loss of materiel and cargo, and reduced missed opportunity costs.
The third organizational gap within the sustainment WfF is the lack of materiel management capability and higher echelon maintenance within the division footprint. AI can analyze operational data to support initiatives in additive manufacturing (AM), also known as 3D printing. AM produces parts from plastic and other durable materials by using 3D printers. It can improve the performance of Army weapons systems on the multi-domain battlefield by reducing distribution requirements for spare parts and replacements, increasing operational readiness, and improving materiel development.
AI also can help to inform and improve predictive maintenance systems on major Army equipment. Condition-based maintenance plus (CBM+) is a system that allows commanders to plan maintenance around their training and operational cycles to increase reliability and reduce the costs to sustain equipment. By anticipating component failures in real time, and maintaining accurate records of required and unscheduled maintenance, AI-informed CBM+ will keep Army combat systems operational for longer periods of time while reducing the number of human maintainers in an increasingly vulnerable deep maneuver area.
As the corporate sector and competitor nations explore AI and its accompanying suite of intelligent automation, it is unknown how the Army will apply the tools of AI to fight and win in MDO. The heightened level of risk in Army operations as part of a joint, interagency, and multinational force allows for zero margin for error. However as an emergent technology, the defense sustainment enterprise is still skeptical of potential compromise through espionage or cyberattacks. Properly supported by changes in organizational structure and championed by a culture shift, AI’s ability to rapidly make fact-based decisions is a novel development with several positive implications for Army sustainment. Planners and futurists expect AI and its accompanying technologies to create the sentient supply chain of the future; MDO-capable and able to feel, perceive, and react to the needs of the warfighter at an extraordinarily granular level.
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Col. Eric A. McCoy is director of the subsistence supply chain for Defense Logistics Agency Troop Support. He has a bachelor's degree from Morgan State University and master's degrees from Central Michigan University, Georgetown University, and the U.S. Army War College. He is a graduate of the Army Command and General Staff College, Combined Arms and Services Staff School, Combined Logistics Captains Career Course, and Ordnance Officer Basic Leadership Course
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This article was published in the July-September 2020 issue of Army Sustainment.
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