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OPTIMIZING PROCUREMENT: THE EFFECT OF GPC LIMIT INCREASES ON ACQUISITION SPEED AND OVERSIGHT
No full textTo support streamlining governmental purchase and cost savings, this project explores how increasing Government Purchase Card (GPC) spending limits can enhance procurement practices within the Department of Defense (DoD) while maintaining rigorous oversight and accountability. The study examines the potential benefits associated with raising the Micro-Purchase Threshold (MPT) to $25,000, focusing on improvements to acquisition speed, administrative burden reduction, and responsive procurement processes. To ensure proper checks and balances in this high-limit GPC environment, the research addresses comprehensive oversight mechanisms, such as data analytics tools, robust auditing protocols, and supportive leadership. At the same time, the project acknowledges some of the key risks that may arise from the elevated GPC limit, to include heightened fraud potential, and compliance challenges. The research provides some recommended mitigation measures to safeguard against these risks. Finally, by collating policy analysis, stakeholder perspectives, best practices in financial oversight, and historical contracting data, the project provides insights aimed to support decision-makers as they consider policy adjustments to optimize DoD procurement efficiency, accountability, and transparency.Distribution Statement A. Approved for public release: Distribution is unlimited.Lieutenant Commander, United States Navy ReserveLieutenant Commander, United States NavyLieutenant, United States Nav
NON-LETHAL RESPONSES TO CHINESE GRAY ZONE TACTICS: EVIDENCE FROM SOUTHEAST ASIA
Get PDFFor over a decade, China has used maritime gray zone actions to assert control over parts of the South China Sea claimed by four Southeast Asian countries. Numerous authors have examined China’s coercive actions; however, fewer have analyzed Southeast Asian responses and almost none have explored China’s reactions. This gap in literature leads to the question: Have Southeast Asian countries successfully responded to China’s maritime gray zone aggression? If so, how can the success be replicated? To answer these questions, this thesis analyzed China’s actions, Southeast Asian responses to those actions, and China’s reaction to these responses in 19 case studies of maritime gray zone incidents. These case studies include 11 incidents between China and Malaysia as well as eight between China and Vietnam. This thesis finds that neither Malaysia nor Vietnam responded effectively. Both countries typically responded with small vessels that could not maintain a continuous presence during these incidents, let alone forcefully challenge China’s actions. To respond more effectively, Southeast Asian countries need to maintain a constant maritime law enforcement presence. They should consider using unmanned surface vessels for this role and use existing maritime assets to provide a forceful response when China initiates an incident.Distribution Statement A. Approved for public release: Distribution is unlimited.Lieutenant, United States NavyNPS Naval Research ProgramThis project was funded in part by the NPS Naval Research Program
FIRSTNET: IMPROVING INTEROPERABILITY AMONG SERVICE INSTALLATIONS AND EMERGENCY SERVICE PERSONNEL
Get PDFThis thesis investigates the integration of FirstNet within military installations, with a specific and in-depth focus on the United States Military Academy at West Point. Employing a case study approach, the research incorporates semi-structured interviews with key stakeholders and detailed document analysis to examine the strategies, challenges, and outcomes associated with FirstNet implementation. Key findings reveal improved interoperability and enhanced communication reliability among both military and civilian first responders, demonstrating the model's substantial potential for broader application across various military environments. While challenges such as infrastructure limitations and initial user unfamiliarity were noted, the overarching benefits of FirstNet suggest its adoption can substantially enhance emergency response capabilities and overall communication effectiveness across military installations. The insights gathered from this study are intended to inform policy decisions and guide future technological advancements in public safety communications within the military sector.Distribution Statement A. Approved for public release: Distribution is unlimited.Captain, United States Marine CorpsCaptain, United States Marine Corp
Faces of NPS: Bonnie Johnson, PhD
No full textFaces of NPS features interviews spotlighting the students, faculty, staff and alumni of our Nation's premier defense education and research institution
ANALYZING AND PREDICTING ARMY COMBAT FITNESS TEST PERFORMANCE: A STATISTICAL AND MACHINE LEARNING APPROACH
Get PDFThe United States Army emphasizes physical fitness as critical to operational readiness and transitioned from the Army Physical Fitness Test (APFT) to the Army Combat Fitness Test to reflect modern combat demands better and minimize injuries. This thesis applies machine learning techniques—including Logistic Regression (LR), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Random Forests (RF), and Artificial Neural Networks (ANN)—to predict ACFT outcomes using raw ACFT scores alongside demographic and body composition data. The analysis evaluates two feature subsets. One subset combines detailed ACFT event scores and body composition data, while the other relies solely on demographic and anthropometric data. We found that artificial neural networks achieve the highest predictive accuracy, underscoring their effectiveness in capturing complex, nonlinear relationships. ACFT event scores significantly improve prediction accuracy by approximately ten percentage points over demographic factors alone. Key predictors include 2-mile run time for ACFT-specific data and body mass index (BMI) among demographic and anthropometric variables. These insights can guide future Army fitness assessments by prioritizing critical predictors, optimizing testing procedures, and improving resource allocation.Distribution Statement A. Approved for public release: Distribution is unlimited.Captain, United States Arm
PORTFOLIO MANAGEMENT COMPETENCY STANDARDS: DEPARTMENT OF DEFENSE VERSUS PROJECT MANAGEMENT INSTITUTE
No full textIncludes Supplementary MaterialDepartment of Defense (DoD) acquisition programs and professionals have been under scrutiny for years. Direction has been provided, over time, to adopt civilian program management practices within DoD. The Project Management Institute, Inc. (PMI) sets and manages civilian program management standards and certification. This study assesses DoD alignment and/or progress in adopting PMI standards. This study is primarily focused on portfolio management competency standards; however, the research requires that some foundational information, prior study results, and discussion of deltas between DoD concepts or documentation and practical application be addressed. This study is a follow-on or update to a 2021 Naval Postgraduate School thesis on the same topic (Gap Analysis of Department of Defense Program Management Competency Standards in Preparation for the Shift to Portfolio Management in Defense Acquisitions). In both this study and previous studies, gap analysis methodology (both qualitative and quantitative approaches) was applied. The research from 2021 found a 41% alignment with industry standards. This study found an increased alignment, closer to 60%. This study reinforces recommendations from the 2021 study and makes an overall recommendation that would result in complete alignment between DoD standards and PMI, the industry standard for portfolio management.Distribution Statement A. Approved for public release: Distribution is unlimited.Civilian, Department of the Nav
Modeling Future Demands for Child Development Center (CDC) Funding
No full textNPS NRP Executive SummaryThe U.S. Marine Corps' Childcare Development Centers (CDCs) are essential to the well-being and operational readiness of military personnel with young families. These centers provide critical early childhood care for infants and children up to age five, enabling service members to fulfill their duties with the assurance that their dependents are receiving safe, high-quality care. However, as the number of young dependents continues to grow, the capacity of CDCs across many installations has become increasingly strained. This has resulted in lengthy waitlists, reduced access to childcare, and growing frustration among service members and their families. The lack of timely childcare availability has broader implications for the Marine Corps. Families facing excessive wait times often experience stress and logistical difficulties, which can in turn diminish service member morale, degrade job performance, and negatively impact retention rates. These systemic issues pose a risk to force readiness and long-term personnel sustainability. This research initiative aims to proactively address this challenge by developing a robust, data-driven modeling framework that supports informed strategic decision-making and future resource allocation efforts. The study designed a suite of reusable, extensible, and replicable predictive models to simulate the performance of the CDC system under various conditions. The goal is to enable leadership to forecast demand, anticipate resource shortfalls, and make informed funding allocations during the year of execution. The research methodology incorporated uncertainty-based intelligence and decision analysis, leveraging advanced quantitative tools to evaluate performance metrics within an environment of incomplete information. Key techniques included Monte Carlo stochastic risk simulations, Poisson queuing models to represent child arrival and service rates, and predictive analytics to assess system behavior over time. An analytical tool was developed that analysts can utilize to determine their individual installation’s allocation to their CDCs based on the demand and maximum required wait times.Approved for public release. Distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)N1 - Manpower, Personnel, Training & Educatio
EABO Connector Optimization Model Reformulation and Improvement
Get PDFNPS NRP Executive SummaryThe United States Marine Corps requires time-efficient, accurate modeling tools to inform decision makers on how to meet logistical requirements while conducting expeditionary advanced base operations. This concept relies heavily on small, independent forces that can conduct maritime operations, command and control, and littoral combat operations within contested environments. The Marine Corps currently utilizes a heuristic model known as the Strategic Marine Analytical Solving Heuristic (SMASH) as well as the Path and Route Mixed Integer Program (PRE-MIP) optimization model, which is a reformulation of the previously used Path Enumeration Mixed Integer Program (PE-MIP), to configure solutions while analyzing logistic networks. The PRE-MIP model works by selecting a path for each piece of cargo to transit the network, and a route for each vessel to take. While each of these models produces solutions, the SMASH model produces unreliable solutions due to its heuristic nature, and the PRE-MIP model cannot reach an optimal solution within a reasonable amount of time for large instances. This work aims to reduce computation time by first solving the linear program relaxation of PRE-MIP, then discarding those paths and routes that were not heavily utilized in the linear program. This results in a filtered path and route instance, which is much easier to solve as a mixed integer program.Approved for public release. Distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)HQMC Installations & Logistics (I&L
AI-Driven Competency Analysis for Enhancing Outcome-Based Education Programs
No full textThe integration of Artificial Intelligence (AI) into education offers unprecedented opportunities to enhance Outcome-Based Education (OBE), especially within military educational and training institutions responsible for developing competencies that meet the operational and leadership demands of highly contested environments. This research focuses on critical warfighter competencies, using the Naval War College (NWC) Professional Military Education (PME) program as a case study to investigate AI-driven solutions for classifying competencies and detecting their presence within the program. Specifically, the study addresses two core research questions: Can AI solutions effectively classify competencies for an OBE program? Can an AI-assisted tool accurately identify the existence of specific competencies within education programs? A systematic methodology was employed, involving subject matter experts (SMEs) for data collection, dataset labeling, and validation to assess various machine learning (ML) models. Recognizing the issue of class imbalance often present in textual data—such as expected learning outcomes (LOs) in our case study—we applied several techniques to enhance dataset balance. These included manual oversampling, text augmentation, and the Synthetic Minority Oversampling Technique (SMOTE), resulting in more representative data samples. Natural Language Processing (NLP) techniques were applied, followed by training ML models such as H2O AutoML, the Tree-based Pipeline Optimization Tool (TPOT), and Transformers. These models achieved test set accuracies consistently above 70%, peaking at 76.73% using the H2O platform. The findings demonstrate that appropriate model selection, fine-tuning, and dataset balancing significantly improve classification accuracy. Validations conducted by SMEs confirmed that AI complements human judgment by identifying gaps and overlaps in the curriculum, thereby enhancing decision-making for competency alignment. This research highlights the critical role of AI in supporting data-driven, competency-oriented education systems capable of adapting to evolving warfighter demands. Future work will explore expanding predictive modeling to recommend targeted curriculum updates and further refine warfighter competencies, promoting more responsive educational strategies.Approved for public release; distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Naval Postgraduate School, Naval Research ProgramOPNAV N711, Navy Education Strategy and Policy Branc
An evaluation of perceptually-enabled task guidance to enhance training and operational effectiveness for the U.S. Marine Corps
Get PDFI Marine Expeditionary Force (I MEF) recognized the lack of virtual, augmented, and mixed reality (VR/AR/XR) applications within the Marine Corps. They sponsored research to identify methods of developing VR/AR/XR use cases involving methods of hand/arm tracking that would assist with the completion of tasks. This report provides options for the usage of VR/AR/XR systems grounded in Marine Corps’ Project Tripoli. The method of research identifies various VR/AR/XR systems that assist in understanding capabilities in commercially developed systems. Laboratory site visits were conducted to learn about the VR/AR/XR systems, which then led to the discovery of a concept called Perceptually Enabled Task Guidance (PTG) developed by the Defense Advanced Research Projects Agency (DARPA). PTG is a potential artificial intelligence solution, when incorporated with VR/AR/XR system, would enable computer vision (CV) technology to identify actions taken by the user and provide detailed user instructions to complete a task. I MEF intends to utilize research findings to see and understand what systems are available, what capabilities these systems have, and how these systems can improve training for the Marine Corps. We expect to find that VR/AR/XR supported by artificial intelligence can support multiple military use cases across a wide range of communities.Approved for public release; distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)I Marine Expeditionary Forc