Capt Nicholas Riggs
EWS StudentCaptain Riggs was born on 21 October 1992 in Camp Pendleton, CA. He attended Murray State University in Murray, Kentucky. Upon graduation, he attended Officer Candidate School. He received his commission on 12 August 2017.
After the Basic School, Second Lieutenant Riggs was assigned to the Marine Corps Aviation Supply School aboard Naval Air Station Whiting Field, FL. He attained the Military Occupational Specialty of 6602 Aviation Supply Officer.
After completing Marine Corps Aviation Supply School in 2018, Second Lieutenant Riggs was assigned to Marine Aviation Logistics Squadron 39, Camp Pendleton, CA. During this tour, he served as the Operations Management Division Officer-in-Charge and Detachment Commander. He deployed to Kuwait and Saudi Arabia in support of Special Purpose Marine Air-Ground Task Force 21.2. He was promoted to Captain on 1 February 2022.
In 2022, Captain Riggs was assigned as the Executive Officer/Operations Officer at Marine Corps Recruiting Station Montgomery, Alabama. Captain Riggs is currently a student at the Expeditionary Warfare School in Quantico, VA.
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
