<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.

<p> Resilience is explained as an entity&rsquo;s capacity to survive and recover from disruptions. Approaches to design resilient systems are in growing demand; however, there have been few demonstrations for measuring and quantifying systems resilience. This research presents a Resilience Analysis regarding Inertial Navigation System (INS) architectures. Specifically for the case study at hand, it is not clear which INS architecture to choose (tightly coupled or loosely coupled) for resilience. This research tests whether Robustness and Recovery measures of effectiveness (MOEs) can be used to determine which INS architecture is most resilient. </p><p> The methodology of this research included enhancing an INS Tool Kit with Resilience Analysis functions. The INS Tool Kit was used to collect resilience data for different INS configurations dealing with Global Positioning System (GPS) outages. Robustness and Recovery data were collected for 500 observations of five different INS configurations. Three configurations were loosely coupled and two were tightly coupled; therefore, 1500 observations of resilience data for loosely coupled INS were compared to 1000 observations for tightly coupled INS. Using data from this Resilience Analysis, a series of nonparametric Mann-Whitney tests showed there is a statistically significant difference between tightly coupled and loosely coupled INS architectures in terms of resilience. Based on these results, greater resilience to GPS outages can be added to the list of advantages for tightly coupled INS architectures. The conclusion of this research is that Robustness and Recovery measures can be used to determine and compare the resilience of different INS architectures. </p><p>

<p> There is a current need to study and understand the behaviors and characteristics of systems of systems. Studying a single system is relatively straightforward when compared to studying a system of systems. A system of systems has unique characteristics that distinguish it from a single system. The additional complexity in a system of systems leads to complicated models and advanced computer simulations. Although modeling and simulation are popular methods for researching a single system, there have been fewer attempts at modeling and simulating systems of systems. Agent-based modeling is an effective approach for researching systems of systems, but validation of agent-based models is difficult, especially if data are not available. Finally, communicating an agent-based model is more difficult than communicating an analytical model because analytical models use familiar mathematical notation. The purpose of this research is to increase the knowledge of system of systems engineering by developing, executing, and analyzing an agent-based model of team collaboration in a real-world, operational system of systems. This research has several goals. The first goal is to address a current need to increase the understanding of the behaviors and characteristics of systems of systems. More specifically, this research aims to model and explain how collaboration and integration in a real-world system of systems affect the achievability of the overall goal of the system of systems. There is an emphasis on the operations and integration of heterogeneous component systems of the collaborative system of systems. This includes understanding the behaviors, characteristics, and interactions among the component systems. The second goal is to develop and thoroughly document a new, repeatable agent-based model of the real-world system of systems. The final goal is to develop a useful tool for understanding and predicting the achievability of the overall goal of the system of systems. Specifically, this research explores team collaboration in a National Basketball Association offensive lineup. This lineup possesses the necessary characteristics to categorize it as a system of systems. Players are the individual, heterogeneous component systems that belong to and operate in the system of systems. This research introduces a new agent-based model and simulation to understand how the individual component systems affect the achievability of the system of systems goal. The NetLogo modeling platform provides an effective environment for executing the model. Data for initialization and validation come from the National Basketball Association. Results show that the overall goal of scoring is an emergent behavior of the collaborative system of systems. Top performing combinations of lineups and collaboration levels emerge. The heterogeneity and interactions of the component systems affect the achievability of the overall goal in different ways. Specific combinations of the collaboration levels and integration of individual component systems determine the scoring output. Observing the component systems individually offers no explanation for the achievability of the overall goal. Instead, it is necessary to view the component systems as a whole. Finally, the verified and validated agent-based model of the offensive lineup contributes to system of systems research, and it is an effective tool for understanding and exploring offensive lineups in the National Basketball Association.</p><p>

<p> Demand response (DR) is considered one of the most reliable and cost-effective solutions for smoothing the electric demand curve of systems under stress. DR programs encourage customers to make changes in power consumption habits in response to electricity price incentives. A well designed autonomous scheduling system for households that are part of the smart grid can result in numerous benefits to all the players in the electricity market. </p><p> Distribution intelligence can be used to anticipate and moderate electricity usage, resulting in lowered production costs. When using this communication network, each entity may send and receive local and global data in a timely fashion, enabling customers to monitor their own electricity usage. Within a smart home, the energy management system is connected to smart appliances, thermostats, and other devices via a home area network (HAN). The HAN balances the electricity demand within the household and prioritizes between appliances and electric devices to modulate electricity usage and to ultimately reduce costs. </p><p> With a collection of rich and timely data, players in the power system can make better decisions to improve reliability, to optimize energy usage, and to reduce energy costs for themselves and for the system. Advanced metering infrastructure (AMI) creates ample opportunities to effectively address peak demand periods using pricing incentives, such as in DR programs and time-of-use (ToU) pricing, which ultimately reduce utilities operating costs. Electricity usage is thus reduced during peak hours with appliances and devices operating at other times, ensuring that electricity production is more evenly distributed throughout the day. </p><p> This dissertation presents a smart home energy management system (SHEMS) using a limited memory algorithm for bound constrained problems known as L-BFGS-B, along with time-of-use (ToU) pricing to optimize appliance scheduling in a 24-hour period. The allocation of energy resources for each appliance is coordinated by a smart controllable load (SCL) device embedded in the household's smart meter. SCL guarantees automation of the proposed SHEMS and prevents manual participation of customers in demand response (DR) programs. The model is simulated on a population of 247 residential prosumers with solar photovoltaic (PV) systems based on 15-min interval electric load data from a residential community in Austin, TX. After clustering households based on their electricity profiles, the proposed optimization model is performed. Simulation results showed that the proposed autonomous scheduling system reduced cumulative energy consumption for customers across the different clusters. In addition, when households were grouped based on their respective category according to the ToU pricing scheme, the simulation reported a notable decrease in total energy consumption from 65.771 kWh to 44.295 kWh; as well as a reduction in the cumulative cost of energy from $6.550 to $4.393 per day. Simulation results confirmed that the proposed algorithm effectively improved the operational efficiency of the distribution system, reduced power congestion at key times, and decreased electricity costs for prosumers.</p><p>

<p> Based on a belief that innovation is increased by Holacratic Engineering Management practices distributing authority to engaged, autonomous, decision makers versus traditional corporate, hierarchical, and delegated decision making, this research examines the relationship between holacratic engineering management and company innovative performance. This proposed new, chaordic, systems engineering and engineering management process, inherently disruptive and arising out of the agile software and lean systems engineering disciplines, is explored using systems thinking and model-based systems engineering principles. This research effort examining Holacratic Engineering Management, an adoptive innovation of lean and agile engineering concepts as a convergence of Holacracy and Lean Enterprise System Engineering includes case studies measuring the effects of Holacratic Engineering Management and Lean Enterprise Systems Engineering on performance. Using soft systems methodology, multiple linear regression is performed on 18 companies that design, develop, and deliver prepackaged software. The theoretical model consists of five component values comprising the holacracy measurements. Companies embracing Holacratic Engineering Management have significantly improved innovation performance.</p><p>

<p> A product is recalled when it is deemed to pose a public safety hazard. As systems become more complex, identifying what factors influence recalls becomes increasingly important for engineering managers and systems engineers. With this in mind, this research endeavor highlights a novel safety framework that assists engineering teams in identifying recall risk factors early in the systems engineering process. The framework is applicable to complex systems, which usually have several stakeholders with requirements, and their needs are to be satisficed. Existing system engineering tools such as Failure Mode and Effects Analysis and Fault-tree Analysis are incorporated to identify risks. Five sequential activity phases are integral to the framework: Stakeholder Identification, Data Acquisition, Statistical Analysis, Safety Analysis, and Risk Review. The framework is especially useful in identifying and evaluating factors that could be associated with recalls prior to the next system design revision. For example, the framework can be applied by the engineering team during the redesign phase of a vehicle model (e.g. 2018 Toyota Camry), using historical data from the previous generation (e.g. 2011 Toyota Camry). Validation of the recall mitigation framework is highlighted through a case study involving the engineering of a new vehicle model in the automotive industry. Original Equipment Manufacturers (OEMs) of complex systems like cars issue recalls whenever the vehicle is perceived to have defects impacting public safety, whether due to airbag issues or excessive emissions. For over four years (2010&ndash;2013), the influence of recall factors for each automaker was analyzed. An additional, more qualitative, case study was performed of Lithium-Ion battery recalls, based on learnings from Sony&rsquo;s woes in 2006. These case studies further validate the framework. Utilizing the framework within a new project environment can greatly assist engineering managers and their teams in identifying recall risk factors early in the systems engineering lifecycle.</p><p>

<p> In a 2007 memorandum, John Young, Under Secretary of Defense at that time, mandated the use of &ldquo;competitive prototyping&rdquo; strategies prior to the System Design and Development phase (Young, 2007). Department of Defense Instruction 5000.02 also includes considerations for prototyping in the acquisition strategy (Department of Defense Instruction [DoDI] 5000.2, 2017). Young&rsquo;s memo (p. 1) listed five benefits of prototyping, which are expected to &ldquo;reduce technical risk, validate designs, validate cost estimates, evaluate manufacturing processes, and refine requirements.&rdquo; However, a process to assess whether, and to what extent, a prototype will be or has been successful in achieving these benefits is not currently in use by the Department of Defense or elsewhere. Because cost increases and schedule extension downsides are inherent in prototyping, such an assessment is critical to determine whether these benefits can be achieved or outweigh the drawbacks. This research proposes an approach for assessing a prototype&rsquo;s likelihood of success in achieving expected prototyping benefits based on identifying the factors yielding these benefits as well as their relative weights.</p><p>

<p> Integrating Techlog, Petrel, Eclipse, and COMSOL is a game changer and led to a better understanding of a very complex undercompacted and overpressurized sand in Block 276. Different reservoir simulation sensitivity runs on P1-sand indicated that putting a new well in block (2,34) under pure depletion will yield the highest incremental oil recovery of about 38%. The sensitivity runs included dumpfloods, waterfloods, and artificial lift. COMSOL has also shown that formation overlying the salt dome is hotter than other portions of the reservoir and that planning a new well on the western flank of the accumulation was the right decision. COMSOL has also shown that overpressurization is driven by undercompaction but that heat conduction from the dome and underlying diapirs affected pore pressure by 3 to 15%.</p><p>