Modern Organizations must be high performing to sustain and grow their operations. This is particularly true for healthcare organizations as they have complex and sensitive operations. One way to improve organizational performance is to adopt Quality Management Systems (QMSs). QMSs organize and improve the effectiveness of all processes to meet stakeholder requirements and achieve organizational performance improvements in alignment with strategic goals. However, the value of QMSs is dependent on successful implementation, which is reportedly quite challenging. This doctoral research examines the factors that affect the successful implementation of QMSs in healthcare. First, a systematic analysis of the published literature revealed that this area is at a relatively early to moderate stage of maturity with many significant opportunities to advance the research area. After that Thematic Analysis of the factors studied in the literature identified ten success factors and one implementation outcome. The ten emergent factors interrelationships and effect on the outcomes was investigated using multiple linear regression and correlation analysis. The result revealed three Critical Success Factors (CSFs), Implementation Culture, Structure, and Management Training. The research found that the Implementation culture requires understanding the working environment with all stakeholders to recognize quality as a routine rooted in all the processes. A systematic embedding of quality within the structure of the organization includes reviewing processes and measure their quality. Management training entitles that change toward QMS requires proper knowledge and expertise; otherwise, it will lead to failure. Hence, decision-makers' ability to understanding the QMS is crucial for success. The study also revealed that a full understanding of the interaction among emergent factors is essential to fully improve the chances of QMSs' implementation success in healthcare, making the potential benefits of these systems more broadly accessible to support this critical industry.

Project manufacturing is a production manufacturing mode that develops and produces large and complex systems that is supplemented by project management techniques for planning and execution. Project manufacturing is better understood by examining it along with three other key areas: manufacturing, project management, and team management. Critical success factors for each of these areas have been examined in the literature but have not been examined in a real world setting in the aerospace and defense industry. This research examines the association between perspectives of project teams and senior management on critical success factors along the four identified dimensions of success in the literature: efficiency, impact on consumer, impact on team, and preparation for the future. These associations are evaluated using Spearman rank correlation method that utilizes survey data obtained from ten project manufacturing teams at an aerospace and defense company along the identified four dimensions of success stated. This research also utilizes regression method to identify significant predictors of overall project success in the aerospace and defense industry for project manufacturing teams. The main focus of this research was achieved by surveying project team members to indicate their level of agreement with the identification of critical success factors. Performance scores for each dimension of success were also gathered from senior management in order to conduct the appropriate statistical analysis of the two sets of paired scores. This research demonstrates that there are both considerable agreements and disagreements between project team members and senior management on project success on different factors. This research also provides some recommendations to bridge the gap between these two groups.

The Aerospace and Defense industry has shifted into a global competitive market that is prioritizing innovative advancements in technological capabilities. Corporations are now having to further develop customer focused strategies based in adding value while reducing costs. Large manufacturing corporations often embrace continuous improvement methodologies, such as Lean Six Sigma, for process improvement. Many organizations have received minimal benefit from the methodology which may link back to leadership and culture. This research examined which styles of leadership are most effective when trying to gain the most value from Lean Six Sigma within manufacturing. The research study surveyed 112 black belt practitioners from one large Aerospace and Defense organization with multiple manufacturing locations in the United States. The study analyzed the relationship between laissez-faire, transactional, and transformation leadership styles and the Lean Six Sigma critical success factors of top management, project selection, and training. The results found that both transactional and transformational leadership styles had a positive correlation while the laissez-faire leadership style had a negative correlation. The results also found that laissez-faire, transactional, and transformational leadership did not predict the success of LSS implementation These findings demonstrate black belt practitioners with transactional and transformational leadership styles positively influence the benefits derived from Lean Six Sigma implementation.

Blockchain technology is a new digital technology that has been disrupting the way businesses are performing. It is a decentralized and distributed ledger that enables transactions of any form of value. As blockchain technology provides visibility, transparency, and security through the multi-agent system, the supply chain sector is one of its critical and promising applications. In a highly dynamic environment, the supply chain's efficiency needs to be measured from a blockchain perspective. As the main benefit of blockchain technology is the visibility and real-time access to data, blockchain technology's preeminent affected areas within the supply chain are the responsiveness to the customer and the inventory efficiency among the supply chain partners. This research developed a dynamic model to measure supply chain efficiency from the blockchain perspective. The developed model is based on system dynamics methodology to model a typical three-tier supply chain; manufacturer, distributor, and retailer. It consists of three components; chain system, backlog system, and supply chain efficiency evaluation system. First, an introduction to the supply chain and blockchain technology is provided. Second, a literature review of supply chain and blockchain technology is presented. From the literature analysis, a research gap is identified with the research questions and objectives. Third, the methodology proposed in this research to answer the research problems is discussed with a literature review about applying system dynamics methodology within the supply chain, and technology adoption is provided. Forth, an illustration of the proposed model is given. Fifth, the dynamic analysis of the supply chain's performance according to various scenarios is evaluated. Finally, a conclusion with the future work is provided.

This dissertation seeks to design an optimization algorithm, based on naturalistic flight data, with emphasis on safety to perform a benefits' analysis when sequencing and scheduling aircraft at the runway. The viability of creating a decision-support tool to aid air traffic controllers in sequencing and optimizing airport operations is evaluated through the benefits' analysis. Air traffic control is a complex and critical system that ensures the safe and efficient movement of aircraft within the airspace. This is particularly true in the immediate vicinity of an airport. Unlike in en-route or terminal area airspace where aircraft usually traverse well established routes and procedures, near the airport after completing a standard arrival procedure, the routes to the final approach are only partially defined. With safety being the foremost priority, the local tower controllers monitor and maintain separation between aircraft to prevent collisions and ensure the overall safety of the airspace. This involves constant surveillance, coordination, and decision-making to manage the dynamic movement of aircraft, changing weather conditions, and potential hazards. All the while, the controllers make decisions regarding tromboning or vectoring based on various factors, including traffic volume, airspace restrictions, weather conditions, operational efficiency, and safety considerations to ensure a safe traffic sequencing of aircraft at the runway. A novel framework is presented for modeling, characterizing, and clustering aircraft trajectories by extracting traffic control decisions of air traffic controllers. A hidden Markov model was developed and applied to transform trajectories from a sequence of temporal spatial position reports to a series of control actions. The edit distance is utilized for quantifying the dissimilarity of two variable-length trajectory strings, followed by the application of k-medoids algorithm to cluster the arrival flows. Next, a repeatable process for detecting and labeling outlier trajectories within a cluster is introduced. Through application on a set of historical trajectories at Ronald Reagan Washington National Airport (DCA), it is demonstrated that the proposed clustering framework overcomes the deficiency of the classical approach and successfully captures the arrival flows of trajectories, that undergo similar control actions. Leveraging on the set of arrival flows, statistical and machine learning models of air traffic controllers are created and evaluated when ordering aircraft to land at the runway. The potential inefficiencies are identified at DCA when sequencing aircraft. As such, there is a potential performance gap, and it appears that there is room for additional sequence optimization. With the goal of overcoming the potential inefficiencies at DCA, a mixed-integer zero-one formulation is designed for a single runway that takes into consideration safety constraints by means of separation constraints between aircraft imposed at each metering point from the entry to the airspace until landing. With the objective of maximizing runway throughput and minimizing the traversed distance, the model sequences and schedules arrivals and departures and generates safe and conflict-free arrival trajectories to actualize that scheduling. The output of the optimization shows that the model successfully recovers approximately 52% of the performance gap between the actual distance traversed and idealized (cluster centroids) distance traversed by all arrival aircraft. Moreover, each arrival aircraft, on average, traverses 2.12 nautical miles shorter than its historical trajectory and thus saving approximately 10 US gallons of jet fuel. By showcasing the potential benefits of the optimization, this dissertation takes a step towards achieving the long-term vision of developing a decision-support tool to assist air traffic controllers in optimally sequencing and scheduling aircraft. To fully leverage the potential benefits of optimization, further development and refinement of the algorithm are necessary to align it with real-world operational demands. As future work, the research would be expanded to integrate uncertainties like weather conditions, wind directions, etc. into the optimization.

Business incubators and entrepreneurship centers are essential tool of economic and social development. This mix method research examined and described the effectiveness of business incubators and entrepreneurship centers as supporting mechanism for economic and social development. The research investigated the impact of business incubators and entrepreneurship centers on mainly combined revenue, regional per capita GDP, and regional unemployment rate in 40 states. The quantitative data was collected by InBIA and UCF in a period of 42 months. 206 of business incubators and entrepreneurship centers responded to a lengthy survey that contains 123 questions. Moreover, 20 semi-structured interviews were conducted with business incubator managers to collect the qualitative data that compliment the quantitative data. The research was design into three main phases: conceptual phase, operational phase, and conclusion. Eisenhart (1989) building theories from case study research approach was applied to this research. The quantitative data was analyzed by using SmartPLS4 which adopted multiple liner regression. QDA Miner Lite software was used to analyze the qualitative data. The result showed that there are significant impact of business incubators and entrepreneurship centers on incubated companies combined revenue and regional per capita GDP. Whereas the effect of business incubators and entrepreneurship centers was not proofed.

Academic literature is limited on how to coordinate the compliance and operational risk functions in organisations. The functional overlap between these two functions in financial institutions, such as banks, may result in oversight gaps or unintentional duplication. This paper describes a study on the overlap between these two functions in the second line of risk and control defence. A number of documents were analysed including relevant Basel Committee documents; South African banking legislation and regulations; integrated annual results, risk and capital reports of the four largest South African (the Big Four) banks; as well as internal operational risk and compliance documents of one of the Big Four banks. Based on this study, regulatory and practice based guidelines are proposed, which may be used to improve the efficiency of the compliance and operational risk functions in banks.

Academic literature is limited on how to coordinate the compliance and operational risk functions in organisations. The functional overlap between these two functions in financial institutions, such as banks, may result in oversight gaps or unintentional duplication. This paper describes a study on the overlap between these two functions in the second line of risk and control defence. A number of documents were analysed including relevant Basel Committee documents; South African banking legislation and regulations; integrated annual results, risk and capital reports of the four largest South African (the Big Four) banks; as well as internal operational risk and compliance documents of one of the Big Four banks. Based on this study, regulatory and practice based guidelines are proposed, which may be used to improve the efficiency of the compliance and operational risk functions in banks.

Thesis (M.Com. (Risk Management))--North-West University, Potchefstroom Campus, 2009.

The Basel Committee was established as the Committee on Banking Regulations and Supervisory Practices.There was a strong recognition within the Committee of the overriding need for a multinational accord to strengthen the stability of the international banking system and to remove a source of competitive inequality arising from differences in national capital requirements. A capital measurement system commonly referred to as the Basel Capital Accord (or the 1988 Accord) was released to banks in July 1988. In 2000, the Committee issued Consultative Document (CP2) designed to incorporate operational risk. In June 2004, the Committee published the document ¡§International Convergence of Capital Measurement and Capital Standards, a Revised Framework¡¨ (widely known as Basel II). This study investigated 3 banks including large-sized, middle-sized and small-sized in Taiwan. And Archival Research and Interviews were used to analyze the regulatory treatment of Operational Risk in the New Basel Capital Accord. In conclusion, this study recommends some appropriate measures to bankers in accordance with the New Basel Capital Accord. Furthermore, several suggestions are also proposed to bankers and the supervisors.