The Nation’s capability for maintaining and improving infrastructure systems and assuring continued critical infrastructure systems’ services has received special attention in the United States of America, largely due to recent disasters with significant impacts. A large number of research and policy studies have been conducted to develop methods to improve protection of critical infrastructure. One approach is to reduce the vulnerability of places and infrastructure systems through mitigation strategies that increase system resilience or resistance to the stresses imposed by disasters. Improving resiliency requires a system of systems approach because of its complexity. Critical infrastructure not only responds to the needs of society for the smooth daily continuation of activities, but also provides the basis on which society exists and relies. To address this complex problem a decision support system to develop critical infrastructure resilience strategies is needed. One such decision support system analyzes the problem using system dynamics. The Critical Infrastructure Resilience Decision Support System (CIR-DSS) developed in this research recognizes the impact of disasters including damage and disruption to critical infrastructure and loss of life. CIR-DSS development involves: (a) understanding the operations and management of critical infrastructure, (b) development of a framework to capture these processes, (c) development of the model framework, (d) development of the model, (e) development of the model’s interface, and (f) the communication of the model results including risk and a cost benefit analysis of alternative strategies. A case study is used to test and validate the approach of the CIR-DSS framework. The CIR-DSS development takes advantage of existing software such as Geographic Information Systems (GIS), Hazards U.S. Multi-Hazard (HAZUS-MH), a tool to assess the impacts of natural hazards, and Structural Thinking, Experiential earning Laboratory with Animation (STELLA), a tool to build Systems Dynamics models. The case study used to test and validate the CIR-DSS approach is based on a real disaster that occurred in Sussex County, Delaware in 2006. The case study demonstrates: (1) the wide range of data and resources required in supporting decision making, (2) how the concepts can be integrated into a decision support systems and (3) the insights gained in using system dynamics to structure this CIR-DSS complex problem.
Posts Tagged ‘Engineering’:
Post-project risk perception and systems management reaction
The objective of this research is to identify whether risk management in projects has any role in risk management in systems. Projects, systems, and risk management are three integral concepts in the management of various enterprises and agencies. Risk management is a common concept in systems and project processes. To avoid failures or crisis during their life cycles, projects and systems managers practice risk management. Projects and systems have well defined life cycles during which the risk is defined, controlled, and managed. Risk management is conducted in each phase of projects and systems. Projects are initiated to close certain operational gaps or to expand the capabilities of the system for better management and operation. The outputs of these projects are to be integrated into larger systems. This research investigates if the risk initiating events during these projects could cause a failure or crises in the system.
Economic efficiency of US 2007 heavy-duty diesel emission standards: A lifecycle-based approach
A new method of evaluating vehicle emission standards is developed and applied to US 2007 heavy-duty diesel emission standards. The method is closely related to lifecycle analyses because it relies on the calculation of lifecycle costs of a single vehicle meeting the new standards, as well as the lifecycle costs of a vehicle compliant with previous standards. This allows the calculation of a per-vehicle net benefit, which is then, along with forecasted vehicle sales, used to estimate the total net benefit of the standards imposed over some period of years. There are multiple advantages to the approach developed here relative to that used by the EPA. Primarily, it allows a comparison of benefits and costs that occur across different periods of time, it relies on marginal damage estimates from the peer-reviewed literature, and it is easily adaptable to different emission standards. In contrast to the result of the EPA analysis, it is found that the net benefit of the standards is negative.
Improving investment performance of venture financing utilizing Bayesian fundamentals
Entrepreneurship is an acknowledged driver of economic growth and job creation. And job creation is a hot topic that is currently in the minds of politicians, economists, regional development officials, the media, and, of course, the general public. Formal programs and strategies designed to stimulate job growth are a constant source of intense debate. But very little discussion has been offered regarding what specific types of jobs do we want to create. In a perfect world, we want to create high-paying jobs that have long-term sustainability and are in companies that people want to work. Research from this dissertation indicates that these “highly desirable” jobs reside mainly inside technology firms. So clearly a strategy for economic growth and job creation must include a focus on increasing the number of technology firms, their initiations, and their funding sources. Historically, technology firms have been funded by wealthy families and now more recently by professional venture capitalists within the private equity industry. How-ever, the performance of venture investors in technology start-ups has been a sore point in the entrepreneurial community for many years. Whether you take the perspective that the “home-run” success rate versus just survival) is 1 out of 10 or that the failure rate is nearly 60 percent, clearly there is room for improvement. And the recent trend in U.S. venture-capital returns has not been very positive of late. According to a recent Wall Street Journal article, the average return for venture capital funds fell to 14 percent for the ten years ended June 30, 2009, down from 34 percent for the ten years ended June 30, 2008, largely because the venture returns generated in the first half of 1999 dropped out of the calculation, according to research firm Cambridge Associates LLC. While this substantial drop in performance is directly linked to the state of the public markets and the dearth of initial public offerings, there is now an ongoing debate that the fundamental business model used by venture funds may be structurally broken. As a result, venture capitalists are resorting to investing in later-stage companies and dabbling in other investment opportunities, leaving a tremendous gap in seed- and early-stage funding, which is at the birthing of technology start-ups. This does not bode well for increasing the number of technology firms into the foreseeable future. Given this depressing backdrop, no one remedy can provide a complete solution that addresses both the input side of finding more and better investable deals and the output side of more high-return exits. However, this dissertation will show that if venture funds would go back to working on improving the basic “blocking and tackling” of finding more and better inputs into the venture investment process, improvements in investment returns would be a natural follow-on. This result re-confirms earlier research that showed that, by implementing a consistent, actuarially-based methodology, venture investors will over the long-run make better investment decisions and hence higher returns, which in turn should create more lucrative exit opportunities. So one of the keys to improving venture fund returns is to make better investment decisions during the venture investment process. By applying Bayesian methods into the venture investment process, this dissertation has developed a new and novel methodology to help the venture investment decision move beyond the current ambiguous and non-repeatable processes based on gut hunches and emotions. This methodology still utilizes subjective information, but within a statistical framework that can easily map into a venture investors current decision-making processes. Furthermore, this dissertation extends previous research and shows that if a venture fund can reduce its rate of “false positive” investments i.e., investing in bad deals), that a decrease in the false positive error rate, of say 50 percent, can produce a material improvement in investment prediction accuracy of 75 percent. If we assume that capital invested in “BAD” companies firms that do not even return the original capital invested) are re-allocated to “GREAT” companies firms that return 20 times the original capital invested), this can lead to an IRR increase of 38.27 percent and improvement in Cash Return of 46.55 percent over five years, assuming a model portfolio). Given that that average venture fund size is now over 100 million dollars, the additional returns can be substantial. Operationally this means that a venture fund must start tracking and monitoring its investment statistics, much like a batting average, over time and when making investment selections, it should adopt a new paradigm of removing from the bottom instead of selecting from the top to make its final investment selections. So the significance and uniqueness of this dissertation are as follows: A) Establish the basis for the importance of technology entrepreneurship. i) Demonstrate that technology firms outperform non-technology firms in growth rates. ii) Uniqueness and Significance: Numerous research on economic impact of entrepreneurship, but no research comparing the economic impact research comparing the economic impact of technology versus non-technology entrepreneurship.； Results could impact national policy regulating the venture capital industry. B) Develop a new methodology to help venture capitalists improve their success rate of investments in technology entrepreneurship based on Bayesian methods. i) Uniqueness and Significance: Since 1946, venture investment methodologies have been generally subjective and ad hoc so very few research on analytical methodologies Zacharakis and Meyer, 2000).； Bayesian methodology applied to many areas of study, but not to venture finance.； If the success rate can be improved, this would lead to a dramatic impact on the economy. In summary, if venture funds can improve on their investment returns, then they will be more apt to invest more money in more seed and early-stage companies, resulting in more venture initiation of technology start-ups, which will then result in faster economic growth and more high-paying longer term jobs in companies that people want to work. This is the ultimate contribution of this dissertation.
Risk management for enterprise resource planning system implementations in project-based firms
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.
An Integrated Framework for Measuring Project Quality
The success or failure) of any project is measured in terms of its ability or inability) to provide the project deliverables at acceptable quality levels within the cost and schedule constraints of the project. While qualitative measures of project cost and schedule performance namely the Cost Performance Index CPI), and the Schedule Performance Index SPI)) are widely accepted and used, no similar measure currently exists for measuring project quality. This research developed an easy-to-use methodology enabling practitioners to develop and use a Quality Performance Index QPI) that is suited to the unique circumstances of their project. The QPI enables practitioners to measure and monitor project quality throughout the project life cycle. It can be tracked and compared to the more traditional CPI, and SPI to compare performance against expectations, identify trends, and determine when and where corrective action is needed.
Competitive Positioning of Ports based on Total Landed Costs of Supply Chains
Nowadays ports play a critic role in the supply chains of contemporary companies and global commerce. Since the ports operational effectiveness is critical on the development of competitive supply chains, their contribution to regional economies is essential. With the globalization of markets, the traffic of containers flowing through the different ports has increased significantly in the last decades. In order to attract additional container traffic and improve their comparative advantages over the competition, ports serving same hinterlands explore ways to improve their operations to become more attractive to shippers. This research explores the hypothesis that lowering the variability of the service time observed in the handling of containers, a port reduces the total logistics costs of their customers, increase its competiveness and that of their customers. This thesis proposes a methodology that allows the quantification of the variability existing in the services of a port derived from factors like inefficient internal operations, vessel congestion or external disruptions scenarios. It focuses on assessing the impact of this variability on the users logistic costs. The methodology also allows a port to define competitive strategies that take into account its variability and that of competing ports. These competitive strategies are also translated into specific parameters that can be used to design and adjust internal operations. The methodology includes 1) a definition of a proper economic model to measure the logistic impact of ports variability, 2) a network analysis approach to the defined problem and 3) a systematic procedure to determine competitive service time parameters for a port. After the methodology is developed, a case study is presented where it is applied to the Port of Guaymas. This is done by finding service time parameters for this port that yield lower logistic costs than the observed in other competing ports.
Designing Deliberately | Transportation Through The Lens of Slow Design
Human mobility is the root of many of our most costly and debilitating societal problems—from urban sprawl to the rising cost of health care.Though the automotive industry acknowledges the need for sustainable mobility, that need remains unfilled in any meaningful way because transportation design remains steeped in a culture of styling and planned obsolescence. Slow Design is a response to the decadent excesses of contemporary product design. With roots in the Slow Food and Arts and Crafts movements, it is a methodology that replaces sales with human well-being as its foundational premise. Applied to the problem of mobility, Slow Design could provide the catalyst that steers the ship of transportation onto a more sustainable course. This paper documents the application of Slow Design to create of an automotive alternative for intracity transportation—the Trimtab 3X3, a vehicle designed to provide convenient, healthful mobility and perhaps change the course of the transportation paradigm.
Developpement de trois elements d’une methodologie de gestion du risque de portefeuilles de projets
Risk management plays an important role in managing a portfolio of projects. The reason for having a portfolio of projects is the maximization of the portfolio value following a selection of projects under an accepted level of risk. The concepts of value maximization and risk-benefit balancing are fundamental concepts in the theory of portfolio management. This is the importance of risk management which maintains an acceptable level of risk and maximizes the portfolio value searching and increasing opportunities. The research conducted has had as result the development of three elements of a risk management methodology which is specifically designed for the management of project portfolios. The three developed elements consider decisions taken during the processes of portfolio management； they also consider portfolio characteristics from a strategic standpoint. They highlight the research of opportunities as well as their maximization. Specifically, the developed elements are: a structure for identifying risks and opportunities, a set of key strategic performance indicators, and a framework for building and using a matrix to monitor risks and opportunities. These elements can be easily integrated within generic methodologies or accepted standards of risk management. The elements do not invalidate existing concepts in the literature. In opposite, they complement existing theory of risk management adding concepts found in the literature and considered as a need by other researchers. Research has revealed the existing relationships between portfolio objectives and projects which form a network of interdependences. These interdependences are built from objectives, benefits and projects； once these components are extended on a time scale, they form flows of “projects-profit- objectives.” This way of exhibiting those concepts is very useful because it allows for searching the impact on the achievement of objectives. At the same time, they allow for setting the manoeuvre margin to react and minimizing or maximizing consequences. Action plans are put in place since the first detection of materialized risks or opportunities. It provides an anticipatory characteristic which helps adapt the portfolio to changing conditions in which the portfolio progresses. The research has explored several concepts, such as benefits management, key performance indicators, critical success factors, and the complexity of a project portfolio. It has also explored current guides and methodologies in order to establish a framework within which the developed elements would be integrated. In addition, a theoretical framework was set for each component in order to provide a solid theoretical basis on which further development of the element could be established. There are still areas of opportunity for continuing the development of a risk management methodology specifically designed for the management of a portfolio. The processes of identifying, analyzing, evaluating, and controlling risks need techniques and tools that consider the characteristics of this area. It will facilitate the achievement of portfolio strategic objectives or even exceeding them. By this means, it supports the role of risk management by helping maximize the strategic value of the portfolio.
Strategic Outsourcing in A Supply Chain
Nowadays outsourcing is a prevailing trend in industry, which allows brand name companies to wholly concentrate on their core competences, as well as introducing some risks when outsourcing its production and procurement activities. In this thesis, we study two issues on outsourcing management with information asymmetries. We begin with the procurement outsourcing decisions. A brand name company may outsource the procurement activities along with production to a contract manufacturer in pursuit of a low cost, while such an approach may incur uncertainty on the quality of the materials and the final products. We consider a supply chain consisting of one brand name company, one contract manufacturer and a pool of material suppliers with distinct quality levels. The prices obtained from the suppliers depend on the bargaining power of the buyer, which is private information. We derive the optimal contracts under various scenarios to address whether the brand name company should outsource the procurement function and evaluate the value of procurement outsourcing strategy in offsetting supply chain risks. We also propose a quality management scheme as a means of fraud prevention in procurement outsourcing. In the second problem, we study dynamic outsourcing mechanism design. We examine a multi-period game in which a brand name company outsources the production to a contract manufacturer. The unit production cost in each period consists of two parts: a random shock that is only observed by the contract manufacturer, and a deterministic cost representing the learning effect, which decreases in the accumulated production volume. Our analysis reveals that the order quantity in each period is not only determined by the cost in the current period, but also by the past orders. Thus we derive the optimal quantity-based contracts in each period, and compare the decisions in different scenarios and analyzed the impact of the learning rate in dynamic mechanism design.