Thesis (M.B.A.)--Naval Postgraduate School, December 2003. / [MBA professional report]. Thesis advisor(s): Philip Candreva, Kenneth Doerr, Glenn Cook. Includes bibliographical references (p. 67-69). Also available online.

Technology Portfolios are essential to the evolution of large complex systems. In an effort to harness the power of new technologies, technology portfolios are used to predict the value of integrating them into the project. This optimization of the technology portfolio creates large complex design spaces; however, many processes operate on the assumption that their technology elements have no dependency on each other, because dependencies are not well defined. This independence assumption simplifies the process, but suggests that these environments are missing out on decision power and fidelity. Therefore, this thesis proposed a way to explain the variations in Portfolio recommendations as a function of adding dependencies. Dependencies were defined in accordance with their development effort figures of merit and possible relationships. The thesis then went on to design a method to integrate two dependency classes into the technology portfolio framework to showcase the effect of incorporating dependencies. Results indicated that Constraint Dependencies reduced the portfolio or stayed the same, while Value Dependencies changed the portfolio optimization completely; making the user compare two different optimization results. Both indicated that they provided higher fidelity with the inclusion of the information added. Furthermore, the upcoming NASA Near-Earth Asteroid Campaign was studied as a case study. This campaign is the plan to send humans to an asteroid by 2025 announced by President Obama in April 2010. The campaign involves multiple missions, capabilities, and technologies that must be demonstrated to enable deep-space human exploration. Therefore, this thesis capitalized on that intention to show how adopting technology in earlier missions can act as a feed-forward method to demonstrate technology for future missions. The thesis showed the baseline technology portfolio, integrated dependencies into the process, compared its findings to the baseline case, and ultimately showed how adding higher fidelity into the process changes the user's decisions. Findings concerning the Near-Earth Asteroid Campaign, the use of dependencies to add fidelity and implications for future work are discussed.

Thesis (M.B.A.)--Naval Postgraduate School, 2003. / Title from title screen (viewed Apr. 5, 2004). "December 2003." "ADA420489"--URL. Includes bibliographical references (p. 67-69). Also issued in paper format.

In an increasingly technology-based competitive global business environment, the operational and competitive strategic potentials of advanced manufacturing technologies (AMT) and related systems cannot be overlooked. This article presents the results of an investigation of AMT implementation practices at 82 discrete-parts durable goods manufacturing plants in the USA. Several propositions that were derived from the AMT literature are tested. The results of this investigation indicate that plants that are desirous of adopting integrated technologies should be prepared to exert considerable effort on the activities in the pre-planning and justification stages of the implementation process. These and other findings that will be particularly useful to firms in the pre-planning stages of technology adoption are outlined and discussed. Research implications of this study are also presented and discussed.

The increase in the types of airspace users (large aircraft, small and regional jets, very light jets, unmanned aerial vehicles, etc.), as well as the very limited number of future new airport development projects are some of the factors that will characterize the next decades in air transportation. These factors, associated with a persistent growth in air traffic will worsen the current gridlock situation experienced at some major airports. As airports are becoming the major capacity bottleneck to continued growth in air traffic, it is therefore primordial to make the most efficient use of the current, and very often, underutilized airport infrastructure. This research thus proposes to address the increase in air traffic demand and resulting capacity issues by considering the implementation of operational concepts and technologies at underutilized airports. However, there are many challenges associated with sustaining the development of this type of airports. First, the need to synchronize evolving technologies with airports' needs and investment capabilities is paramount. Additionally, it was observed that the evolution of secondary airports, and their needs, is tightly linked to the environment in which they operate. In particular, sensitivity of airports to changes in the dynamics of their environment is important, therefore requiring that the factors that drive the need for capacity expansion be identified and characterized. Finally, the difficulty to evaluate risk and make financially viable decisions, particularly when investing in new technologies, cannot be ignored. This work thus focuses on the development of a methodology to address these challenges and ensure the sustainability of airport capacity-enhancement investments in a continuously changing environment. The four-step process developed in this research leverages the benefits yielded by impact assessment techniques, system dynamics modeling, and real options analysis to 1) provide the decision maker with a rigorous, structured, and traceable process for technology selection, 2) assess the combined impact of interrelated technologies, 3) support the translation of technology impact factors into airport performance indicators, and help identify the factors that drive the need for capacity expansion, and finally 4) enable the quantitative assessment of the strategic value of embedding flexibility in the formulation of technology portfolios and investment options. The proposed methodology demonstrates, through a change in demand at the airport modeled, the importance of being able to weigh both the technological and strategic performance of the technology portfolios considered. Hence, by capturing the time dimension and technology causality impacts in technology portfolio selection, this work helps identify key technologies or technology groupings, and assess their performance on airport metrics. By embedding flexibility in the formulation of investment scenarios, it provides the decision maker with a more accurate picture of the options available to him, as well as the time and sequence under which these should be exercised.

In the last half century, the aerospace industry has seen a dramatic paradigm shift from a focus on performance-at-any-cost to product economics and value. The steady increase in product requirements, complexity and global competition has driven aircraft manufacturers to seek broad portfolios of advanced technologies. The development costs and cycle times of these technologies vary widely, and the resulting design environment is one where decisions must be made under substantial uncertainty. Modeling and simulation have recently become the standard practice for addressing these issues; detailed simulations and explorations of candidate future states of these systems help reduce a complex design problem into a comprehensible, manageable form where decision factors are prioritized. While there are still fundamental criticisms about using modeling and simulation, the emerging challenge becomes ``How do you best configure uncertainty analyses and the information they produce to address real world problems?” One such analysis approach was developed in this thesis by structuring the input, models, and output to answer questions about the risk and economic impact of technology decisions in future aircraft programs. Unlike other methods, this method placed emphasis on the uncertainty in the cumulative cashflow space as the integrator of economic viability. From this perspective, it then focused on exploration of the design and technology space to tailor the business case and its associated risk in the cash flow dimension. The methodology is called CASSANDRA and is intended to be executed by a program manager of a manufacturer working of the development of future concepts. The program manager has the ability to control design elements as well as the new technology allocation on that aircraft. She is also responsible for the elicitation of the uncertainty in those dimensions within control as well as the external scenarios (that are out of program control). The methodology was applied on a future single-aisle 150 passenger aircraft design. The overall methodology is compared to existing approaches and is shown to identify more economically robust design decisions under a set of at-risk program scenarios. Additionally, a set of metrics in the uncertain cumulative cashflow space were developed to assist the methodology user in the identification, evaluation, and selection of design and technology. These metrics are compared to alternate approaches and are shown to better identify risk efficient design and technology selections. At the modeling level, an approach is given to estimate the production quantity based on an enhanced Overall Evaluation Criterion method that captures the competitive advantage of the aircraft design. This model was needed as the assumption of production quantity is highly influential to the business case risk. Finally, the research explored the capacity to generate risk mitigation strategies in to two analysis configurations: when available data and simulation capacity are abundant, and when they are sparse or incomplete. The first configuration leverages structured filtration of Monte Carlo simulation results. The allocation of design and technology risk is then identified on the Pareto Frontier. The second configuration identifies the direction of robust risk mitigation based on the available data and limited simulation ability. It leverages a linearized approximation of the cashflow metrics and identifies the direction of allocation using the Jacobian matrix and its inversion.