Capt Changa Ngwenya

USMC and the use of Artificial Intelligence Predictive Demand Planning in a Contested Information Environment

This paper argues that we must integrate artificial intelligence into predictive demand planning if we are to remain competitive in contested informat…This paper argues that we must integrate artificial intelligence into predictive demand planning if we are to remain competitive in contested information environments, particularly against adversaries already leveraging AI-enabled logistics. Commerci…This paper argues that we must integrate artificial intelligence into predictive demand planning if we are to remain competitive in contested information environments, particularly against adversaries already leveraging AI-enabled logistics. Commercial firms such as Amazon and FedEx demonstrate how AI-driven forecasting, simulation modeling, and large language models can optimize inventory placement, anticipate demand,a nd accelerate decision-mak…This paper argues that we must integrate artificial intelligence into predictive demand planning if we are to remain competitive in contested information environments, particularly against adversaries already leveraging AI-enabled logistics. Commercial firms such as Amazon and FedEx demonstrate how AI-driven forecasting, simulation modeling, and large language models can optimize inventory placement, anticipate demand,a nd accelerate decision-making. By contrast, we continue to rely on fragmented data systems, static planning methods, and limited interoperability, which slow throughput and increase risk in high-tempo, and dispersed operations. Without secure integration of predictive analytics int our command-and-control systems, we risk delayed resupply, maintenance shortfalls, and exploitable logistical vulnerabilities - especially in theaters like INDOPACOM where distance, dispersion, and adversary anti-access capabilities compound sustainment challenges. To address this gap, we propose three complementary courses of action: AI-enbaled predictive logistics for maintenance and demand forecasting, AI-optimized contested distribution routing, and AI-prioritized additve manufacturing. Predictive models would allow us to anticipate equipment failures and supply requirements before they occur, reducing downtime and emergency resupply. AI-driven routing tools would enhance survivability by optimizing movement across contested domains based on ISR patterns, weather, and platform availability. Finally, additive manufacturing, guided by AI to prioritize mission critical parts, would reduce our reliance on vulnerable supply lines. Together, these solutions provide a phased, practical path to transforming our logistics enterprise from a reactive support function into a resilient, data-driven combat advantage. Show MoreClick the title to see all detailsShow More

Capt Changa NgwenyaEWS Student, USMC

Capt Nicholas RiggsEWS Student

Capt Tim WingertLogistics Officer

Capt Dominick TranfagliaStudent, Expeditionary Warfare School, United States Marine Corps, Marine Corps University

Capt Changa NgwenyaEWS Student, USMC

Capt Nicholas RiggsEWS Student

Capt Tim WingertLogistics Officer

Capt Dominick TranfagliaStudent, Expeditionary Warfare School, United States Marine Corps, Marine Corps University