Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2005. / Includes bibliographical references (leaves 84-94). / The USA healthcare industry has recently undergone significant pressure to become competitive and think innovatively due to its increased growth as a percentage of the GDP, which was as much as 14. 1% in 2001. Additionally hospitals are faced with an estimated nursing shortage of 600,000 by 2020, and with an aging patient base that demands better quality at a lower cost. Specifically, hospitals tie up as much as 35% of their budgets in inventory and in the required labour to manage it. Moreover, future improvements will necessarily require a solution beyond statistically sound inventory policies and software packages. The contribution of this thesis is to provide an analysis of "Long Term Value Added Partnerships" and their role in enabling innovative and trust based vendor - hospital inventory supply relationships as a suggestive solution for the healthcare industry. The study included two hospitals leading the way in such relationships with a leading healthcare vendor in the USA market. The conducted literature review helps understand the benefits and implications of attempting to establish long term value added partnerships in the healthcare industry. From describing the pressures and the traditional mindset of hospitals towards inventory practices, the study moves on to explain two inventory management methodologies widely used across different industries, and it finally provides an account of the drivers and potential pitfalls of strategic alliances which are information intensive in nature. / (cont.) The research framework is followed by a detailed description of the methodology used while conducting field observations, 47 interviews and data analysis of the visited hospitals. Subsequently the research findings are presented and supported by graphical representations of both the soft and hard data collected. Finally the thesis conclusion is given in the form of a list of recommendations to be adopted by both healthcare vendors and hospitals. / by Jorge Miguel dos Santos Fradinho Duarte Oliveira. / M.Eng.in Logistics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2007. / Includes bibliographical references (p. 259-269). / The university is increasingly seen as an engine of regional economic development. Since the 1980s the university's role has been framed in terms of its contribution to industrial innovation. The conventional wisdom views this contribution as occurring primarily through the technology transfer model. The university, in this way of thinking, must move closer to industry and the marketplace by translating research into deliverables for commercialization. This dissertation challenges the empirical validity of this view. Two case studies of industrial upgrading form the empirical core of this research: the machinery industry in Tampere, Finland and the NASCAR motorsports industry in Charlotte, North Carolina. In each case I analyze the university's role from the ground up using a conceptual framework that views the innovation as a social process that has a dual nature: analytic and interpretive. From an analytic perspective innovation is a problem-solving activity. From an interpretive perspective innovation is an ongoing conversation. I find that in neither case is the university's most important contribution to each industry's upgrading made through the technology transfer model. In Tampere, whose core innovation process is interpretive, the local university creates spaces for interaction and conversation that enable knowledge integration, provides interlocutors for exploratory conversations, and educates engineers. / (cont.) In Charlotte, whose innovation process is analytic, the local university plays essentially no role. NASCAR teams rely on business partners for technology transfer and attempts to make the university active in technology transfer for the industry have yet to succeed. The duality of innovation helps to explain the university's role in the Tampere case and its absence in the Charlotte case. I argue that the technology transfer model implicitly assumes that innovation is analytic and thus misses the interpretive side of innovation. The case study findings suggest three things. First, the university has a distinctive ability to make interpretive contributions to industrial innovation. Second, practices emphasized by the technology transfer model, such as patenting and technology commercialization, do not account for the university's interpretive role. Third and finally, too much emphasis on the technology transfer model may put at risk the university's interpretive capabilities and hence its most distinctive contribution to industrial innovation. / by Carlos Andrés Martínez Vela. / Ph.D.

Thesis: Ph. D., Massachusetts Institute of Technology, Engineering Systems Division, February 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 303-315). / Effectively balancing existing technology adoption and new technology development is critical for successfully managing carbon dioxide (CO2) emissions from the fossil-dominated electric power generation sector. The long infrastructure lifetimes of power plant investments mean that deployment decisions made today will influence carbon dioxide emissions long into the future. New technology development and R&D decisions can help reduce the overall costs of reducing emissions, but there are multiple technology investments to choose from, and returns to R&D are inherently uncertain. These features of the technology "deployment versus development" question create unique challenges for decision makers charged with managing cumulative carbon dioxide emissions from the electricity sector. Unfortunately, current quantitative decision support tools ultimately lack one or more of three overarching features jointly necessary to provide useful insights about an optimal balance between R&D program and power plant investments. They lack (1) resolution of the critical structure of the electricity sector, (2) an explicit endogenous representation of the effects of learning-by-searching technological change, and/or (3) an efficient decision-analytic framework to explore multiple technology investment options under uncertainty in the returns to R&D. This dissertation presents a new quantitative decision support framework that allows for the study of socially optimal R&D and capital investment decisions for the power generation sector. Through a novel integration of classical electricity generation investment planning methods, economic modeling of endogenous R&D-driven technological change, and emerging numerical stochastic optimization techniques, the new framework (1) explicitly accounts for the complementary roles that generating technologies play within the electric power system, (2) considers the characteristics of the uncertainty in the technology innovation process, and (3) identifies flexible, adaptive R&D investment strategies for multiple technologies for decision makers to consider. A series of numerical experiments with the new model reveal that (1) the optimal near-term R&D investment strategy under technological change uncertainty and adapting between decisions can be different than the optimal strategy assuming perfect foresight, and may be higher or lower; (2) the timing that a technology should be deployed to meet a specific carbon target dictates the direction and magnitude of the difference in these decisions; (3) increasing the level of uncertainty tends to increase near-term R&D investments; and (4) increasing right-skewness of the uncertainty (i.e., decreasing the likelihood of higher than average returns), reduces R&D spending throughout the planning horizon. / by Nidhi Rana Santen. / Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005. / Includes bibliographical references (p. 289-298). / This research develops a comprehensive approach to identify and deal with real options in" projects, that is, those real options (flexibility) that are integral parts of the technical design. It represents a first attempt to specify analytically the design parameters that provide good opportunities for flexibility for any specific engineering system. It proposes a two-stage integrated process: options identification followed by options analysis. Options identification includes a screening and a simulation model. Options analysis develops a stochastic mixed-integer programming model to value options. This approach decreases the complexity and size of the models at each stage and thus permits efficient computation even though traditionally fixed design parameters are allowed to vary stochastically. The options identification stage discovers the design elements most likely to provide worthwhile flexibility. As there are often too many possible options for systems designers to consider, they need a way to identify the most valuable options for further consideration, that is, a screening model. This is a simplified, conceptual, low-fidelity model for the system that conceptualizes its most important issues. As it can be easily run many times, it is used to test extensively designs under dynamic conditions for robustness and reliability; and to validate and improve the details of the preliminary design and set of possible options. The options valuation stage uses stochastic mixed integer programming to analyze how preliminary designs identified by the options identification stage should evolve over time as uncertainties get resolved. Complex interdependencies among options are specified in the constraints. / (cont.) This formulation enables designers to analyze complex and problem-specific interdependencies that have been beyond the reach of standard tools for options analysis, to develop explicit plans for the execution of projects according to the contingencies that arise. The framework developed is generally applicable to engineering systems. The dissertation explores two cases in river basin development and satellite communications. The framework successfully attacks these cases, and shows significant value of real options "in" projects, in the form of increased expected net benefit and/or lowered downside risk. / by Tao Wang. / Ph.D.

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 117-130). / Cyberspace is the most pervasive and rapidly adopted communication media and the most disruptive until date. It is now indispensable for almost every facet of modern society and touches, practically, everyone by providing a powerful platform for interaction and innovation. Given the widespread availability of tools to operate in this environment, a growing array of actors are trying to benefit as they seek to control critical decision points in the real world and cyberspace. It is imperative to understand what cyberspace "is made of' - over and above the Internet and answer the question "who gets what, when, and how?" The intent of this research initiative is to contribute to the generation, management and sharing of knowledge to enhance understandings of the emerging area of cyber-international relations as a complex, flexible and adaptive domain of interactions. The first contribution of this thesis is the development of a multi-dimensional Cyber System for Strategic Decisions (CSSD) framework. This framework enables a holistic identification of the elements of a system, which are structured as set of nested and hierarchical relationships. It facilitated in mapping the entities that comprise different domains of cyberspace and the dependencies within and across those entities. The second contribution of this thesis is the development of the foundations for an internally consistent and articulate representation of cyber-international relations in terms of actors- individuals and group of individuals, layers of the Internet and the context of cyber engagement that form the basis of the CSSD framework. This approach can be applied to diverse domains to build scenarios and model different facets of both the real world and cyberspace according to the practical needs. The instruments and intensity of engagement and the extent of time of engagement are the two dependencies that map the interactions among the different entities. The third contribution of this thesis is the development of a robust, comprehensive, and coherent test use-case based on "Intellectual Property Rights (IPR)" domain. The CSSD framework is then adapted to test its applicability to the use-case. IPR has been selected as the test use-case because it provided both the legal understanding and legislative efforts at international level, in as collaborative, effective and uniform manner as possible, to protect the rights of intellectual property owners and to avoid future conflicts. / by Gaurav Agarwal. / S.M.in Engineering and Management

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (page 99). / A methodology was proposed to measure sales cannibalization using historic data containing variations in store densities. Sales cannibalization was defined as a decrease in the sales of one or several existing stores as a result of nearby store openings. The proposed methodology, combining regressions with geographic and other forms of cluster analysis, allowed measurement of the cannibalization, while controlling for other relevant sales drivers such as consumers' and geographic areas' characteristics, seasonality and the nature of the demand of the product category (impulsive and non-impulsive purchases). The analysis found evidence of sales cannibalization in the store network studied and showed that its severity varies according to consumers' and geographic areas' characteristics and product category. This last finding was particularly relevant, as cannibalization was consistently more severe for non-impulsive products. Based on the cannibalization measurements, sales functions were created and then optimized to find the number of stores maximizing total sales. This number represents saturation, meaning a point beyond which any new store opening in the area just redistributes sales. The number of stores maximizing sales, however, may not be the goal, particularly when fixed costs associated with operating stores are important and when attempting to maintain attractive businesses for store owners, which is relevant in franchised settings. / by Sergio C. Sapaj. / S.M.in Engineering and Management

<p> With pressure on the U.S. Defense Acquisition System (DAS) to reduce cost overruns and schedule delays, system engineers&rsquo; performance is only as good as their tools. Recent literature details a need for 1) objective, analytical risk quantification methodologies over traditional subjective qualitative methods &ndash; such as, expert judgment, and 2) mathematically rigorous system-level maturity assessments. The Mahafza, Componation, and Tippett (2005) Technology Performance Risk Index (TPRI) ties the assessment of technical performance to the quantification of risk of unmet performance; however, it is structured for component- level data as input. This study&rsquo;s aim is to establish a modified TPRI with systems-level data as model input, and then validate the modified index with actual system-level data from the Department of Defense&rsquo;s (DoD) Major Defense Acquisition Programs (MDAPs). This work&rsquo;s contribution is the establishment and validation of the System-level Performance Risk Index (SPRI). With the introduction of the SPRI, system-level metrics are better aligned, allowing for better assessment, tradeoff and balance of time, performance and cost constraints. This will allow system engineers and program managers to ultimately make better-informed system-level technical decisions throughout the development phase.</p>

<p> There is a need in the energy services industry for companies to accurately estimate and verify energy savings. This starts with the accurate development of a utility baseline. Systems Engineering principles can be used to determine the optimal method to use for developing a utility baseline. Several Systems Engineering tools were used in this thesis, including a stakeholder analysis, needs and requirements files, requirements traceability, functional flow block diagrams, a work breakdown structure, trade studies, and verification and validation of requirements. These tools helped to identify three main components of the process for further analysis. The trade study was then used to determine the best way to address these components of the process, and resulted in innovative methods that had not previously been considered. The recommendations in this work will benefit both the energy services company and their customers.</p>

<p>The most common traditional non-functional requirement analysis is reliability. With systems becoming more complex, networked, and adaptive to environmental uncertainties, system resiliency has recently become the non-functional requirement analysis of choice. Analysis of system resiliency has challenges; which include, defining resilience for domain areas, identifying resilience metrics, determining resilience modeling strategies, and understanding how to best integrate the concepts of risk and reliability into resiliency. Formal methods that integrate all of these concepts do not currently exist in specific domain areas. Leveraging RAMSoS, a model-based reliability analysis methodology for Systems of Systems (SoS), we propose an extension that accounts for resiliency analysis through evaluation of mission performance, risk, and cost using multi-criteria decision-making (MCDM) modeling and design trade study variability modeling evaluation techniques. This proposed methodology, coined RAMSoS-RESIL, is applied to a case study in the multi-agent unmanned aerial vehicle (UAV) domain to investigate the potential benefits of a mission architecture where functionality to complete a mission is disseminated across multiple UAVs (distributed) opposed to being contained in a single UAV (monolithic). The case study based research demonstrates proof of concept for the proposed model-based technique and provides sufficient preliminary evidence to conclude which architectural design (distributed vs. monolithic) is most resilient based on insight into mission resilience performance, risk, and cost in addition to the traditional analysis of reliability.

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 83-85). / Enhanced oil recovery (EOR) through carbon dioxide (CO₂) sequestration from anthropogenic sources has been gaining attention in policy circles. In particular, it is viewed as a potential way to help accelerate the deployment of carbon capture and sequestration (CCS) technologies. The interest in the EOR-CCS model stems from the economic, geologic and regulatory benefits this model offers when compared to the waste-driven CCS model that utilizes saline aquifers for CO₂ storage. However, there are still some major challenges impeding the deployment of the EOR-CCS model; chief among these challenges is the mismatch between CO₂ supplies from anthropogenic sources and CO₂ demand from EOR operations. One potential way to address this challenge is through a CO₂ stacked storage system. A CO₂ stacked storage system utilizes brine formations adjacent to EOR oilfields for the purpose of storing any additional quantities of CO₂ the EOR operation cannot handle. The concept of a stacked storage system with focus on CO₂ supplies from coal-fired power plants was analyzed using a case study. A U.S. coal-fired power plant and a U.S. EOR oilfield were used to model a stacked storage system in order to determine the economic and technical viability of such a model. More specifically, this thesis has three main objectives. The first is to determine the overall cost of implementing the stacked storage system. The overall cost of the system came to approximately $90 per ton of CO₂ avoided. / by Ibrahim Toukan. / S.M.