<p> The United States Department of Defense (DoD) loses billions of dollars annually on cancelled or failed acquisition programs. Several DoD acquisition studies, Office of Management and Budget Studies, Government Accountability Office Reports, as well as other studies highlight the disturbing fact, of a plethora of programs that fail to meet full operational requirement capabilities, and therefore, are eventually cancelled. In these cases, the DoD loses billions of investment dollars without any return. Scholars, program managers, and systems engineers posit that there are a host of factors that influence whether a program is cancelled or allowed to continue. They include, but are not limited to political pressures, cost overruns, schedule overruns, and performance shortfalls. </p><p> The research here aims to add to the body of knowledge of systems engineering, program management, and the factors that influence acquisition program terminations within the United States Department of Defense (DoD). Specifically, this research surveyed the United States DoD acquisition program managers, defense industry program managers, and defense industry consultants, to evaluate and analyze the key program factors that influence DoD acquisition program terminations. The research also conducted a comparison of different attributes that would lead to project failure amongst various groups. This research used relatively important weight calculations and a chi-squared distribution analysis in order to compare the differences between DoD acquisition program managers, defense industry program managers, and defense industry consultants, with regards to the factors that lead to DoD acquisition program terminations. This research aims to further answer several interrelated research questions, in order to identify the factors that have the greatest influence on program and project cancellation from the expert&rsquo;s perspective, and capture any significant differences between DoD program managers, DoD industry personnel, and DoD consultants. The research questions include the following: </p><p> 1) Are there any statistically significant differences between what DoD program managers, DoD industry personnel, and DoD consultants personnel think influence program cancellation? 2) Are there statistically significant differences of the various DoD acquisition program factors between what DoD program managers, DoD industry personnel, and DoD consultants personnel think influence program cancellation? </p><p> An exhaustive literature review identified 11 critical factors that were associated with program management for examination. For this study, the examination and methodology used were the Relative Importance Weight technique, to analyze the attributes and factors. RIW methodology consisted of conducting a survey to identify and evaluate the relative importance of the signi?cant factors influencing program termination. Respondents of this survey included the following groups: 1) DoD program and project managers, 2) DoD Industry personnel, and 3) DoD consultants. The outcomes of this research serve three primary purposes: 1) identify the Relative Importance Weight of DoD acquisition program factors that influence program termination, 2) fulfill a system&rsquo;s engineering and program management&rsquo;s knowledge gap, by understanding and identifying the most critical factors within the unique DoD acquisition program management system, and 3) serve as a spring board for future research for DoD program management. The results of this research indicate that a statistically significant difference does not exist between the three groups with relative importance of 11 program management factors.</p>

<p> For decades, the United States (U.S.) Department of Defense (DoD) has developed, deployed, and operated hundreds of different types of systems as components of systems-of-systems. Achieving and maintaining joint systems-of-systems interoperability as new systems are added is a constant problem. The Army, Navy, U.S. Marine Corps, and Air Force each develop requirements, budgets, and acquire, field and operate systems that function as part of joint systems-of-systems. Technology and threats are rapidly evolving. These globally deployed systems and systems-of-systems employed by combatant commanders must be continuously modernized or risk becoming obsolete, resulting in potential mission failure and loss of life. </p><p> Using a wireless telecommunications interface standard case study, this research developed a unique method of planning systems-of-systems modernization using a system dynamics (SD) approach. This approach: a) accounts for key factors that influence the dynamic behavior of systems-of-systems modernization, impacting the ability to modernize systems-of-systems, and b) enables what-if analysis, and decision-making support of systems-of-systems modernization planning options. This research used a mixed-methods approach to demonstrate that the SD model is measurably superior to past practice. Quantitative statistical analysis was performed on 20 years&rsquo; (2001&ndash;2020) of data. A qualitative, scenario-based approach was used to develop an SD model. The results demonstrate that engineers, managers, and senior decision makers in the DoD can realize statistically significant gains by using an SD model to develop and explore systems-of-systems modernization planning options. This research&rsquo;s original contribution to knowledge is the development and validation of an SD model for planning systems-of-systems modernization using a mixed-methods research approach.</p><p>

<p> The United States economic and national security sectors remain vulnerable to shortages of critical materials due to the risks posed by disruptions in globally-dispersed supply networks. Numerous methods over the past 10 years have been proposed to identify, assess, and evaluate risks in critical material supply chains. This praxis provides a method to quantify the impact of supply disruptions and inform the application of risk mitigation measures for a critical material supply chain from mineral deposits to final platform. It proposes a Bayesian network modeling method not yet applied to the problem in publicly available studies and fits with an assessment methodology proposed by the National Science and Technology Center (NSTC). Results from this study provide indicative answers to how supply disruptions propagate through a selected critical material supply network, which nodes are vulnerable to supply disruptions, and whether mitigating actions can reduce the impact of supply disruptions. The approach here demonstrates that a Bayesian network model can be one of the tools in a criticality assessment methodology.</p><p>

<p> In an effort to control cost and schedule growth, the US Department of Defense mandates that defense acquisition programs perform Technology Readiness Acquisitions (TRAs) during the acquisition cycle. Technology maturity is widely believed to correlate with cost and schedule risk in complex development programs, with the Technology Readiness Level (TRL) the metric currently used for assessing technology maturity. However, while a schedule-related correlation has been demonstrated, no research has shown a statistically significant correlation between a system&rsquo;s overall technology maturity and cost growth. This study demonstrates that an acceptable system level metric can be constructed with the available TRLs that aids in controlling cost growth. This work validates this metric as a useful tool for program managers and system engineering professionals. Utilizing published data on US Department of Defense acquisition programs, this study defines a System Maturity Level (SML) metric that can be computed from existing TRLs and is a statistically significant predictor of cost risk. A System Maturity Level cost-risk curve is also introduced in order to help engineering managers make cost-risk decisions.</p><p>

<p> The majority of steel manufacturers within the United States have employed electric arc furnaces (EAFs), which melt raw materials, for the production of steel. The raw material used in EAFs is referred to as steel, or ferrous, scrap. There are many different grades of ferrous scrap, which are classified by the following attributes: size, density, and chemistry weight percentages of residual elements. Methods currently applied to assess the price of ferrous scrap employ qualitative measures. Furthermore, the largest unknown when procuring ferrous scrap is the chemistry weight percentages of residual elements. This attribute is the most critical to the steel manufacturer as it affects the quality of steel. The goal of the research conducted for this praxis was to create a pricing model that predicted the value-in-use price of ferrous scrap per supplier using a quantitative purchasing structure. The solution to this problem was attained through a pricing model. As this research was applied, the pricing models developed during this research are readily available for use within the steel industry. Results from the pricing models displayed a significant improvement in predicting the value-in-use ferrous scrap price over the current procurement process used by the steel manufacturer. </p><p>

<p>Systems engineering measurements provide the decision maker a method to effectively manage uncertainty throughout the systems engineering lifecycle including entrance into key project milestones. Premature transition through project milestones or decision gates is likely to lead to cost and schedule overruns. Risks to a project can be monitored by measuring systems engineering measures in the development of systems. This dissertation proposes a framework for implementing systems engineering measures for the development of systems based on a set of leading indicators. The dissertation also defines additional methods to identify predictive measures. Finally, the dissertation provides the results of surveying systems engineering professionals to capture an industry perspective on systems engineering measurement and the extent to which the industry uses predictive measures and techniques. The relationship between a set of leading indicators and project performance was evaluated. This helps to identify the strength of the relationship between leading indicators and project performance. The results of the study indicate that the use of systems engineering measurement on a project increases the likelihood of better project performance. By monitoring systems engineering as a leading indicator, overall project management and performance is improved.