Human Factors and Predictive Resource Management in Naval Food Systems
Presentation Type
Poster Presentation
In Person or Zoom Presentation
In-Person
Campus
Daytona Beach
Status
Student
Faculty/Staff Department
Human Factors and Behavioral Neurobiology
Student Year and Major
Graduate HFS and Behavioral Neuro Biology
Presentation Description/Abstract
Algorithmic inference (Ai) and computational sustainability offer new opportunities to improve U.S. Navy food systems through predictive resource management informed by human factors engineering.
This study examines the historical impact of technological advancements to naval food provisioning from 1800 to 2026, tracing advancements from standardized rations and preserved provisions to refrigerated supply chains, digital inventory management, and nutritional planning. Using historical documentary analysis and comparative systems evaluation, the research examines selected naval regulations, archival food-service records, and contemporary operational assessments to distinguish authorized rations, menu availability, actual dietary consumption, and food waste.
This distinction identifies a fundamental limitation in historical provisioning records: food issued does not necessarily represent food consumed.
Building upon these findings, the study evaluates the potential application of machine-learning inference to naval resource management, including food-demand forecasting, inventory optimization, and waste reduction that may improve sustainable naval nutrition delivery.
The evaluation considers predictive accuracy, incomplete consumption data, automation bias, and maritime operational constraints. Integrating Human Factors Engineering, the proposed framework emphasizes nutritional adequacy, human oversight, and algorithmic accountability.
The analysis establishes criteria for assessing future AI-supported provisioning systems through resource efficiency, dietary consumption, and human-system performance, recognizing adequate nutrition as an essential contributor to naval operational readiness.
Keywords
Computational Sustainability; Artificial Intelligence; Human Factors Engineering; U.S. Navy Food Systems; Predictive Resource Management; Naval Nutrition; Machine Learning; Food Waste
Human Factors and Predictive Resource Management in Naval Food Systems
Algorithmic inference (Ai) and computational sustainability offer new opportunities to improve U.S. Navy food systems through predictive resource management informed by human factors engineering.
This study examines the historical impact of technological advancements to naval food provisioning from 1800 to 2026, tracing advancements from standardized rations and preserved provisions to refrigerated supply chains, digital inventory management, and nutritional planning. Using historical documentary analysis and comparative systems evaluation, the research examines selected naval regulations, archival food-service records, and contemporary operational assessments to distinguish authorized rations, menu availability, actual dietary consumption, and food waste.
This distinction identifies a fundamental limitation in historical provisioning records: food issued does not necessarily represent food consumed.
Building upon these findings, the study evaluates the potential application of machine-learning inference to naval resource management, including food-demand forecasting, inventory optimization, and waste reduction that may improve sustainable naval nutrition delivery.
The evaluation considers predictive accuracy, incomplete consumption data, automation bias, and maritime operational constraints. Integrating Human Factors Engineering, the proposed framework emphasizes nutritional adequacy, human oversight, and algorithmic accountability.
The analysis establishes criteria for assessing future AI-supported provisioning systems through resource efficiency, dietary consumption, and human-system performance, recognizing adequate nutrition as an essential contributor to naval operational readiness.