<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> Despite being a mandated requirement, the U.S. Government Accountability Office found a lack of guidance across the Department of Defense for conducting analysis of alternatives which contributed to significant cost, schedule, and performance problems for Defense acquisition programs. In 2008 ninety-six major weapon system programs were reviewed and findings showed cost growths of $296 billion, average program delays of twenty-two months, and the delivery of fewer systems with reduced capabilities. Without specific guidance and criteria for how analysis of alternatives should be conducted the Department of Defense will continue to struggle to make informed trade-offs and start executable programs. This praxis presents a decision support system that enables decision makers to analyze cost, schedule, and performance ratings for multi criteria decision analysis problems. The decision support system provides interactive visualization tools that allow decision makers to execute sensitivity and uncertainty analyses, analyze the decision problem from multiple stakeholder-specific viewpoints, and synthesize results in a meaningful way. Although the primary motivation of this praxis is to fill the gap identified by the U.S. Government Accountability Office, the decision support system presented in this praxis can be modified and applied across multiple domains. </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.

<p> The intent of the current study was to address the gap in knowledge regarding factors influencing Canadian power engineers&rsquo; decision to pursue advanced certification in British Columbia and Alberta. The purpose of the study was to explore potential relationships between advancement intention and these decision factors. The dependent variable was the intent to advance. The six independent variables are comprised of the following: (a) time commitment, (b) educational support, (c) locus of control (LOC), (d) time elapsed since previous certification, (e) responsibility, and (f) peer appraisal. The research methodology was a quantitative correlational design, followed with linear and logistic regression. An original survey was developed for the study and pilot tested for validity and reliability. Revealed in the results of the study were positive and significant relationships between the dependent variable (DV) of advancement intention and three of the six independent variables (IVs) tested through correlational analysis. The relationships were time commitment, responsibility, and elapsed time. The three remaining independent variables that did not exhibit significant relationships with the DV were educational support, locus of control, and peer appraisal. Comprehension of the influential factors regarding the intention of Canadian power engineers&rsquo; to pursue advanced certification may assist industry and academia with insight into the barriers and enablers to higher certification, and the correlation of decision factors with advancement intention.</p><p>

<p> Design for Six Sigma (DFSS) is a methodical quality design strategy for deploying new products, services, and processes in all aspects of business functions; its goal is to improve customer satisfaction, sales, and revenue.</p><p> This project presents the theory and application of the DFSS Define, Measure, Analyze, Design, and Verify (DMADV) model as applied to the design and development of an environmental compliance process. This model addresses recent European Union (EU) regulations known as REACH (Registration, Evaluation, Authorisation, and Restriction of Chemicals).</p><p> The project presents the methodical use of applicable quality tools and concepts by a project team at an original components manufacturer (OCM) for each stage of the DMADV model. The final presentation has resulted in a process that consistently meets customer requirements, targeted release dates, and process capabilities as defined by the project charter.</p>

<p> The need to identify the factors that influence the overall safety environment and compliance with safety procedures within airline operations is substantial. This study examines the relationships between job satisfaction, the overall perception of the safety culture, and compliance with safety rules and regulations of airline employees working in flight operations. A survey questionnaire administered via the internet gathered responses which were converted to numerical values for quantitative analysis. The results were grouped to provide indications of overall average levels in each of the three categories, satisfaction, perceptions, and compliance. Correlations between data in the three sets were tested for statistical significance using two-sample t-tests assuming equal variances. Strong statistical significance was found between job satisfaction and compliance with safety rules and between perceptions of the safety environment and safety compliance. The relationship between job satisfaction and safety perceptions did not show strong statistical significance.</p>

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.