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The United States Marine Corps’ counseling and development process requires modernization to remain on the leading edge of talent management and to keep pace with both the public sector and the private sector. The Commandant’s plan, titled Talent Management 2030, states that the Marine Corps will implement a 360-degree feedback program to bridge gaps and to retain the best, brightest, and most capable leaders. The purpose of our research is to analyze end-user perspectives to gauge support for 360-degree feedback in both developmental and evaluative capacities and offer recommendations for who should receive feedback and/or when feedback should be distributed. The sample population for our survey study is comprised of Marine Officers assigned to the Naval Postgraduate School. Most of the sample population consists of captains and majors between the ages of 26 and 35, with 6 to 15 years of military service. The results of our survey suggest that there is support for 360-degree feedback, particularly when utilized in a developmental capacity. Our analysis suggests that 360-degree feedback should be targeted toward junior or mid-career company grade officers and should relate to a significant occasion, such as advancement to a position with substantial oversight or influence. Our implementation and scaling recommendations consider civilian organization “best practices” and try to achieve meaningful results, enable growth, and avoid survey fatigue., Major, United States Marine Corps, Major, United States Marine Corps, Captain, United States Marine Corps, Approved for public release. Distribution is unlimited.

Through the successful application of data analysis tools, beginning-to-end analysis of enterprise supply and maintenance chains has resulted in the identification of valuable optimization opportunities. With complex logistics systems generating large amounts of structured and unstructured data, an enterprise’s ability to analyze and interpret mass amounts of disparate datasets is a source of great competitive advantage. The U.S. Navy operates a complex global logistics system, and its ability to optimize its operations is critical to ongoing success. In our research, we focus data analysis methods on critical weapon systems such as the CH-53E aircraft in order to identify unseen relationships in their sustainment efforts using reported maintenance records fielded by naval component commands and the multiple CH-53E subordinate and supporting organizations. Using available analytics tools, we improve our understanding of the relationships among total flight hours, maintenance actions, and percentage of equipment not in service to the aircraft’s Mission Capable rates. We find opportunities to improve the Navy’s understanding of its supply and maintenance practices in support of selected critical weapons systems through the holistic analysis of datasets at the organizational level. We identify pitfalls in weapon system sustainment, thus improving the effectiveness of naval aviation and the operational availability of its aircraft.

Student Thesis (NPS NRP Project Related), The U.S. Navy’s supply chain stretches globally, supporting the fleet in multiple theaters to enable sustained forward presence, security, and deterrence. However, supply chains are subject to disruptions that slow materiel movements throughout the network, and these disruptions may severely hinder the readiness of ships operating in distant theaters. A common culprit for peacetime supply chain disruptions is adverse weather, which is especially true in waters that are prone to major tropical storm systems. Other disruptions may include failure of equipment, accidents, and adversarial activity during active conflict situations. With these concerns in mind, this thesis formulates six optimization models to assist logistics planners in preparing for and responding to these uncertain contingencies. The models we present fall into both a proactive family, which plan for disruptions based on their likelihood before they occur, and a reactive family, which respond to the disruptions as they occur. To address the probabilistic risks of disruptions, these models utilize linear integer programming, chance constraints programming, and dynamic programming in different ways, seeking to demonstrate various methods for routing supplies through a network vulnerable to random disruptions. Lastly, we analyze results to determine the suitability of these models in several disruption scenarios., N41, N4 - Fleet Readiness & Logistics, http://archive.org/details/networkoptimizat1094564185, This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp, Chief of Naval Operations (CNO), Ensign, United States Navy, Approved for public release; distribution is unlimited.

The Marine Corps relies on advanced ground equipment that requires significant depot-level maintenance to extend its life cycle. However, Marine Corps budgets continue to be strained, and every dollar counts. Therefore, the Marine Corps Logistics Command has a vested interest in optimizing its budget to provide the best possible support to the warfighter. The Repair Optimization Materiel Evaluator (ROME) model is used to assist in creating repair plans. Using data-farming software designed specifically for this thesis and advanced design of experiments, the ROME model was run more than 2,000 times with specific, traceable, and repeatable changes made to the inputs each time. The outputs from these runs were analyzed to determine the model’s efficacy. Analysis determined that the model’s reliance on warfighting values and small penalty terms has created a bias toward allocating funds for high-density, low-cost items with low warfighting values at the expense of items with much higher warfighting values. Specifically, if monies were allocated only according to the ROME model, it would default to fixing thousands of M-16 rifles and rifle optics and very few, if any, large pieces of equipment such as tanks. Based on the findings from this thesis, it is recommended that ROME’s objective function be reformulated or calculation of warfighting values be changed. The new data farming capabilities will allow testing these modifications as well as enable advanced analysis with ROME.

A key enabler of military readiness includes civilian employees who work for the Department of Defense (DoD). To sustain military readiness, it is in the government’s interest to understand DoD civilian workforce attrition patterns and attrition factors. The intent of this research is to better understand DoD civilian employee personnel factors that might influence attrition. To meet this intent, we use survival analysis based on calendar year 2009 new hires with covariates found in a DoD civilian’s personnel record, as well as with covariates found in applicable employees’ prior military active component or reserve component records. In comparison of blue-collar and white-collar employees, we see there are very similar survival trends and retired military service members have the highest survivability. However, we do find that younger blue-collar males (29-years-old or less) have a higher survival probability than younger white-collar males, and blue-collar females have a higher survival probability than white-collar females. At the aggregate level, the probability of employee survivability increased among employees with families, higher salaries (greater than $50,000) and higher education (associate degree, bachelor’s degree, or master’s degree). Finally, employees who are male, who are between the ages of 35 and 54, or who work for the Navy, have an increased survivability., Office of People Analytics, http://archive.org/details/exploratorysurvi1094562312, Lieutenant Commander, United States Navy, Approved for public release; distribution is unlimited.

The U.S. Navy’s supply chain stretches globally, supporting the fleet in multiple theaters to enable sustained forward presence, security, and deterrence. However, supply chains are subject to disruptions that slow materiel movements throughout the network, and these disruptions may severely hinder the readiness of ships operating in distant theaters. A common culprit for peacetime supply chain disruptions is adverse weather, which is especially true in waters that are prone to major tropical storm systems. Other disruptions may include failure of equipment, accidents, and adversarial activity during active conflict situations. With these concerns in mind, this thesis formulates six optimization models to assist logistics planners in preparing for and responding to these uncertain contingencies. The models we present fall into both a proactive family, which plan for disruptions based on their likelihood before they occur, and a reactive family, which respond to the disruptions as they occur. To address the probabilistic risks of disruptions, these models utilize linear integer programming, chance constraints programming, and dynamic programming in different ways, seeking to demonstrate various methods for routing supplies through a network vulnerable to random disruptions. Lastly, we analyze results to determine the suitability of these models in several disruption scenarios.

The USMC Expeditionary Energy Office is funding the partial development of the Intelligent Power Optimization with Environmental Reactivity (IPOWER) simulation model to provide a tool to commanders and program managers for making energy-informed decisions. IPOWER allows leaders to predict power requirements for dismounted units and assess how equipment parameters affect power consumption. To facilitate testing and analysis with IPOWER, a software wrapper was built that enables users to automatically run efficient experiments. Using this capability, over 4,000 simulated missions were implemented across multiple input variables and applied to four scenarios that varied mission timeline (24- and 72-hour) and use of harvesters (solar panels). The results indicate that IPOWER produces intuitive results when harvesters are excluded. As duty cycle and average power increase, the total power consumed, total weight, and total number of batteries also increases. However, the inclusion of harvesters into scenarios highlights possible system-level interactions that are more complex to simulate. During scenarios where harvesters are used, power harvested does not always result in power being recharged to batteries. When mission length was extended to 72 hours, errors emerged and data anomalies resulted. In general, we show that IPOWER can be used for its intended purpose, but additional efforts are needed to simulate complexity when adding harvesters to scenarios.

The Personal Gear Issue (PGI) Division at each Expeditionary Support Unit is responsible for ordering, inventory management, and distribution of all personal gear issued. This work adapts an existing optimization model to determine appropriate ordering policies, specifically, the order point, s, and order quantity, Q, for each line item. The model seeks to minimize cost of inventory subject to target fill rate for each item, maximum number of orders per month, and other constraints. We consider realistic conditions such as current demand, 90 orders per month, and 99% fill rate. Under these conditions, the optimal s and Q values result in a significant reduction in the cost of inventory, from $8.2M (current PGI’s inventory value) down to $1.23M. In addition, our model shows, via sensitivity analysis, the effect that varying fill-rate targets, monthly order limit, and demand have on cost. For example, we observe a nonlinear increase in cost as a function of demand increase. We recommend that PGI Division revises its ordering policy to include formal optimization analysis and to periodically track demand in order to re-evaluate the optimal s and Q values., http://archive.org/details/expeditionarylog1094560392, Lieutenant Commander, United States Navy, Approved for public release; distribution is unlimited.

This thesis seeks to improve aircraft refueling at Naval Air Station (NAS) Oceana, VA, using aircraft waiting time for fuel as a measure of performance. We develop a computer-assisted discrete-event simulation to model refueling at NAS Oceana using airfield data from October 2011. Our study focuses on six factors: the total number of mobile refueling trucks, the rate of fuel flow from each truck, the quality of information sharing, the percentage of aircraft that refuel using hot pits (high-speed, in-ground refueling stations), and the normal operating band (both the upper limit and the lower limit) of jet fuel level that each truck driver maintains. We use experimental design and determine the efficiency of various decisions for reducing fuel wait time. We conclude with specific recommendations for NAS Oceana leadership., The original document contains color images.

This thesis seeks to improve aircraft refueling at Naval Air Station (NAS) Oceana, VA, using aircraft waiting time for fuel as a measure of performance. We develop a computer-assisted discrete-event simulation to model refueling at NAS Oceana using airfield data from October 2011. Our study focuses on six factors: the total number of mobile refueling trucks, the rate of fuel flow from each truck, the quality of information sharing, the percentage of aircraft that refuel using hot pits (high-speed, in-ground refueling stations), and the normal operating band (both the upper limit and the lower limit) of jet fuel level that each truck driver maintains. We use experimental design and determine the efficiency of various decisions for reducing fuel wait time. We conclude with specific recommendations for NAS Oceana leadership.