Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item DATA-DRIVEN RISK MODELING FOR INFRASTRUCTURE PROJECTS USING ARTIFICIAL INTELLIGENCE TECHNIQUES(2023) Erfani, Abdolmajid; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Managing project risk is a key part of the successful implementation of any large project and is widely recognized as a best practice for public agencies to deliver infrastructures. The conventional method of identifying and evaluating project risks involves getting input from subject matter experts at risk workshops in the early phases of a project. As a project moves through its life cycle, these identified risks and their assessments evolve. Some risks are realized to become issues, some are mitigated, and some are retired as no longer important. Despite the value provided by conventional expert-based approaches, several challenges remain due to the time-consuming and expensive processes involved. Moreover, limited is known about how risks evolve from ex-ante to ex-post over time. How well does the project team identify and evaluate risks in the initial phase compared to what happens during project execution? Using historical data and artificial intelligence techniques, this study addressed these limitations by introducing a data-driven framework to identify risks automatically and to examine the quality of early risk registers and risk assessments. Risk registers from more than 70 U.S. major transportation projects form the input dataset. Firstly, the study reports a high degree of similarity between risk registers for different projects in the entire document of the risk register, and the probability and consequence of each risk item, suggesting that it is feasible to develop a common risk register. Secondly, the developed data-driven model for identifying common risks has a recall of over 66% and an F1 score of 0.59 for new projects, i.e., knowledge and experience of similar previous projects can help identify more than 66% of risks at the start. Thirdly, approximately 65% of ex-ante identified risks actually occur in projects and are mitigated, while more than 35% do not occur and are retired. The categorization of risk management styles illustrates that identifying risks early on is important, but it is not sufficient to achieve successful project delivery. During project execution, a project team demonstrating positive doer behavior (by actively monitoring and identifying risks) performed better. Finally, this study proposes using a data-driven approach to unify and summarize existing risk documents to create a comprehensive risk breakdown structure (RBS). Study results suggest that acquired knowledge from previous projects helps project teams and public agencies identify risks more effectively than starting from scratch using solely expert judgments.Item Value Engineering Decisioneering: A Risk Management Tool in the Project Management Office - Case Study of Electricity Distribution Companies in Nigeria(2019) Quadri, Habeeb Adekunle; Baecher, Gregory B; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Project Management Office is in its early adopter stage worldwide most especially in developing economies. The Wall Street crash in the 1930s led to the emergence of corpo-rate governance to contain agency problems which resonates with the recent global eco-nomic recession induced by recklessness in decision making, which now requires uncon-ventional but methodical engineering approach to business decisions. The unprecedented proliferation of the PMO in the last decade is an indication of its con-tinued growth and relevance as Center of Excellence in achieving business goals. Value Engineering is a function-oriented problem-solving methodology the PMO can integrate as a risk management strategy in decision-making to optimize scarce resources for opera-tional excellence and long-term growth. The objective of this research is to propose the adoption of VE as a risk management tool in the PMO and provide a framework to facilitate its adoption and application as a risk management tool in the PMO. This will result in the discovery of a new, consistent and repeatable decision making process capable of enhancing the PMO efficiency and the overall corporate performance. This research case-studied the PMO of Abuja Electricity Distribution Company and Kano Electricity Distribution Company in Nigeria. Electricity industry in Nigeria enjoyed an excruciating monopoly for over a century, bad leadership and inept project governance led to its privatization in 2013. The quests by the new investors to discover new tools and strategies to achieve higher returns on investment heralded the establishment of the PMO to leverage its capabilities to execute change management and build leadership compe-tencies. The research revealed the major drivers propelling and constraints hindering the success-ful adoption and application of VE in the PMO. However, the research discovered that prudent integration of VE as a risk management tool in the PMO will competitively repo-sition the electricity distribution companies, ceteris paribus. This research contributed to the body of knowledge in the theoretical underpinnings of the PMO and risk management literature. However, testing and validating the effective-ness of VE methodology as a risk management tool in the PMO overtime, become the objective of further research by the researcher or other researchers.Item The Impact of Executives with Supply Chain Management and Operations Management Experience on Recall Performance and Risk Management(2017) Paraskevas, John-Patrick; Grimm, Curtis; Corsi, Thomas; Business and Management: Logistics, Business & Public Policy; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation investigates the impact of the growing presence of executives with supply chain management and operations management (SCOM) experience on the top management team. My dissertation focuses on two major strategic areas in which an executive with SCOM experience may influence firm strategy and firm performance. The first area I have chosen to study is a firm's propensity to engage in product recalls along with their responsiveness to the quality glitches that lead to recall. The second area of study is risk and resilience within a firm’s supply chain. Essay 1 explores the impact of an executive with SCOM experience on product recall propensity and firm responsiveness. We utilize a unique dataset collected from multiple sources on executives’ backgrounds and product recalls, and we find that firms having top management executives with SCOM backgrounds have fewer recalls and faster recall responsiveness. The findings also indicate that the shortened speed to recall is enhanced when a firm engages in a proactive recall strategy. The second essay studies the impact of top executives with SCOM experience as well as top executives with finance experience. We then propose original hypotheses regarding the impact of these two forms of experience on the firm’s supply chain risk profile. We utilize a dataset of manufacturing locations over a three-year period. Our findings indicate that firms with SCOM experience on their top management teams have lower levels of location risk and higher levels of resilience at their production locations. On the other hand our findings indicate that firms with top management teams with finance experience are more likely to take on location risk at their production locations but are similar to firms with SCOM on their top management team in that they also have high levels of resilience. Lastly we explore the impact of an SCOM executive when the firm uses offshore production. My findings for both essays contribute to upper echelons theory (UET) by proposing and testing novel hypotheses regarding the impact of the presence of executives with SCOM experience and finance experience on recall performance and supply chain risk management.Item Quantifying the Resilience of an Urban Traffic-Electric Power Coupled System(2016) Fotouhi, Hossein; Miller-Hooks, Elise; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Transportation system resilience has been the subject of several recent studies. To assess the resilience of a transportation network, however, it is essential to model its interactions with and reliance on other lifelines. In this work, a bi-level, mixed-integer, stochastic program is presented for quantifying the resilience of a coupled traffic-power network under a host of potential natural or anthropogenic hazard-impact scenarios. A two-layer network representation is employed that includes details of both systems. Interdependencies between the urban traffic and electric power distribution systems are captured through linking variables and logical constraints. The modeling approach was applied on a case study developed on a portion of the signalized traffic-power distribution system in southern Minneapolis. The results of the case study show the importance of explicitly considering interdependencies between critical infrastructures in transportation resilience estimation. The results also provide insights on lifeline performance from an alternative power perspective.Item Risk Analysis and Damage Assessment For Flood Prone Areas in Washington DC(2011) Lessani, Arian; Baecher, Gregory B; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis presents a loss estimation method regarding areas of District of Columbia susceptible to flooding, specifically the Southwest quadrant, the National Mall, and Federal Triangle. This thesis develops data for input to a flood model that considers parameters such as detailed digital elevation data, global warming potential, and storm surge for a category IV hurricane. The main goal of this study is to employ a standard method for estimating flooding damages in Washington by supplying combination of the mentioned parameters to the HAZUS-MH 2.0 program. The results of this research is useful for planning purposes, such as reducing natural hazard losses and preparing emergency response and recovery. It is predicted that in the projected storm surge flood more than 1500 buildings would be damaged and about ten thousand people would seek temporary refuge in public shelters. The estimate of total loss for flooding is approximately $1,300 million dollars.Item Risk Management for Enterprise Resource Planning System Implementations in Project-Based Firms(2010) ZENG, YAJUN; Skibniewski, Miroslaw J.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Enterprise Resource Planning (ERP) systems have been regarded as one of the most important information technology developments in the past decades. While ERP systems provide the potential to bring substantial benefits, their implementations are characterized with large capital outlay, long duration, and high risks of failure including implementation process failure and system usage failure. As a result, the adoption of ERP systems in project-based firms has been lagged behind lots of companies in many other industries. In order to ensure the success of ERP system implementations in project-based firms, sound risk management is the key. The overall objective of this research is to identify the risks in ERP system implementations within project-based firms and develop a new approach to analyze these risks and quantitatively assess their impacts on ERP system implementation failure. At first, the research describes ERP systems in conjunction with the nature and working practices of project-based firms and current status and issues related to ERP adoption in such firms, and thus analyzes the causes for their relatively low ERP adoption and states the research problems and objectives. Accordingly, a conceptual research framework is presented, and the procedures and research methods are outlined. Secondly, based on the risk factors regarding generic ERP projects in extant literature, the research comprehensively identifies the risk factors of ERP system implementation within project-based firms. These risk factors are classified into different categories, qualitatively described and analyzed, and used to establish a risk taxonomy. Thirdly, an approach is developed based on fault tree analysis to decompose ERP systems failure and assess the relationships between ERP component failures and system usage failure, both qualitatively and quantitatively. The principles and processes of this approach and related fault tree analysis methods and techniques are presented in the context of ERP projects. Fourthly, certain practical strategies are proposed to manage the risks of ERP system implementations. The proposed risk assessment approach and management strategies together with the comprehensive list of identified risk factors not only contribute to the body of knowledge of information system risk management, but also can be used as an effective tool by practitioners to actively analyze, assess, and manage the risks of ERP system implementations within project-based firms.Item PROJECT PERFORMANCE BASED OPTIMAL CAPITAL STRUCTURE FOR PRIVATELY FINANCED INFRASTRUCTURE PROJECTS(2004-11-18) Sundararajan, Satheesh Kumar; Tseng, Chung-Li; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Privately Financed Infrastructure (PFI) projects are characterized by huge and irreversible investments and are faced with various risks. Project performance risks, such as project completion time and costs, affect the project value significantly, particularly in project development phase. This is because a major part of the project investments are made during this phase. Due to high uncertainties in managing the project performance risks, the selection of optimal financial structure is a challenge to Project Company sponsors and Lenders. Conventional project performance measurement and valuation methods cannot capture the dynamics of risk variables and their impact on the project value. Without such dynamic performance information, the decision of capital structure may not only be suboptimal, but lead to erroneous results. This research proposes an uncertainty evolution model, with which the dynamics of the project performance risk variables can be predicted at any desired time over the project development phase. A dynamic capital structure model is proposed, that explicitly considers the performance risks and adjusts the capital structure dynamically to counter the impact of performance risks. Numerical results show that such a model can add a significant value to a PFI project. Two risk-sharing mechanisms are also incorporated in the capital structure for a PFI project: active project management (self-support) and government support. An active project management method called {\it dynamic crashing} is proposed. By dynamically controlling the project performance through dynamic crashing, we show that the project value can be improved and the chances of potential bankruptcies can be reduced. In addition, the significance of government support as a risk-sharing mechanism is also modeled, which may be viewed as another means to protect the Project Company against the potential bankruptcies and improves the project value. Numerical results are implemented to validate the models. Overall, this research contributes an integrated framework to capital structure decisions for projects with performance uncertainties.