Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 269-278). / This dissertation demonstrates that valuable strategic insight and a reasonable measure of predictive power can be obtained by developing and analyzing context-rich parsimonious game theoretic strategy models during large technology transitions in concentrated industries. Such models enable clear and compact analysis of oligopolistic competitive dynamics which are typically addressed by more informal processes in much of current managerial practice. This result is established by constructing 3-player game theory models of two prominent, ongoing technology transitions in semiconductor manufacturing: EUV lithography (EUVL) and 450mm silicon wafers. The iterative multi-data-source process used for establishing appropriate game structures and payoff estimates is described in detail. Contextual realism was augmented by in-depth behavioral analysis of select industry players and by interactions with the equipment supplier facing groups at a semiconductor device manufacturer. The robust game theoretic prediction from the EUV lithography R&D model was subsequently validated by the announcement of a series of chipmaker investments totaling greater than $6B in the largest photolithography supplier in July/August 2012 (including significant financial assistance to EUV equipment readiness) and by several other industry financing events in late 2012. Although no such distinct confirmatory evidence exists for the 450mm wafer model predictions, it was found to have substantial face validity. Based on learning from the modeling efforts, criteria are proposed for determining whether other technology transitions will be amenable to such analysis. Generalizability to similar transitions in other industries (including aerospace and automotive) is discussed. Industry strategists, technology and business strategy scholars, and innovation policy makers should find the work of interest. / by George Thomas Heaps-Nelson. / Ph.D.

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 74-78). / In the summer of 2008, the United States of America experienced an oil shock, first of a kind since 1970s. The American public became sensitized to the concerns about foreign oil supply and climate change and global warming, and to the role of transportation in emissions of carbon dioxide and other greenhouse gases (GHG). Several proposed federal policies impose stringent limits on the transportation sector, in terms of fuel consumption and GHG emissions. Within transportation sector, light duty vehicles (LDVs) - cars, light trucks and SUVs - currently emit the most GHGs. Hybrid technology emerged as a promising option to address several of these challenges. A modern hybrid electric vehicle (HEV) offers significantly better fuel economy together with lower levels of pollutant and CO2 emissions. HEVs are currently categorized as Advanced Technology Partial Zero Emission Vehicles (AT-PZEV) by California Air Resource Board. Recently, a new generation of vehicles, plug-in hybrid electric vehicles (PHEV), has been announced in the immediate future by major auto manufacturers. While HEVs have a relatively small battery that is recharged by the engine or by regenerative braking, a larger battery of a PHEV and a charger allows a vehicle owner to recharge the battery from the electric grid. The plug-in technology further increases fuel economy and reduces emissions from the tailpipe. For example, a Chevrolet Volt PHEV is expected to be launched as 2011 model with 40 mile allelectric travel with no tailpipe emissions. However, there are multiple challenges associated with the new technology. HEVs and PHEVs incur higher costs due to additional components, such as electric motors and motor controllers, and a battery. Today's batteries provide energy storage density hundred times lower than that of gasoline. Electricity consumed by hybrids is generated by coal and other fossil fuel power plants that emit harmful chemicals and greenhouse gases. The infrastructure for electric cars is at the infancy stage. Some government policies designed to introduce all-electric cars, such as the California ZEV mandate of the late 1990s, failed to introduce a sustained number of electric vehicles to the market. To provide an integrated approach to the causes and effects of electrified powertrains, two plausible scenarios of advanced vehicle market penetration were developed. Federal policies and consumer preferences were considered as primary drivers. Biofuels were considered alongside fossil fuels as primary energy sources for transportation. Rapid adoption of PHEVs was found to cause a perceptible, but not a significant increase in electric power demand. The scenarios demonstrated ability to achieve fuel economy milestones and quantified the challenge of achieving 80% reduction in greenhouse gas emissions by 2050. / by Michael Khusid. / S.M.in Engineering and Management

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2011. / Cataloged from PDF version of thesis. Page 304 blank. / Includes bibliographical references (p. 247-254). / Significant energy challenges today come from security of supply and environmental concerns. Those surpass the quest for economic efficiency that has been the primary objective until recent times. In an intensive fossil-fuel energy world, it is critical to find more effective ways of using the existing resources and of identifying technologies that can improve the sustainability of the energy model. Both, distributed energy resources and renewable-based electricity generation technologies are considered, by energy experts and also policymakers, to be essential for this purpose. Co-generation of electricity and heat at the residential level, known as micro-CHP, is an attractive alternative because of the potential for enhancing energy efficiency, reducing GHG emissions, and improving the utilization of primary energy resources. This thesis aims at quantitatively assessing the impacts of a large-scale penetration of micro-CHPs within an energy system. Based on system-wide and residential metrics, this work intends to understand whether or not this technology is a valuable contribution to social welfare. For this purpose, a methodology is developed to integrate increasing numbers of micro-CHPs into a system's generation capacity expansion process over a 20-year timeframe, and into an electric power system's daily operation for a single year. Findings from our long-term analysis demonstrated that micro-CHPs helped in reducing cumulative C02 emissions. Under high-to-medium carbon price scenarios, they mostly displaced installed capacity from gas-based technologies, such as natural gas combined cycle units. Other results suggest that a larger micro-CHP penetration could be encouraged through economic incentives such as capital costs reduction, and/or lower natural gas retail prices, where conditions may favor one micro-CHP technology over another. Better economic conditions stimulate the deployment of micro-CHPs with low heat-to-power ratio (HPR), while machines with very high HPR do not appear to be a competitive alternative when compared to other micro-CHP technologies and conventional heating systems. Findings from our short-term analysis demonstrated that widespread deployment of micro-CHPs results in positive effects, such as C02 emissions reductions, energy efficiency improvements, decrease in system energy production costs, and summer peak load reduction at both system and residential levels. It was also found that these benefits could increase with the incorporation of additional features such as a hot water storage unit integrated with the heating system, micro-CHP modulating capability, and a micro-CHP price-based control strategy. However, the benefits at the system level seem to be relatively low for the level of penetration, assumed to be 10% of the total electric installed capacity. Moreover, the operation of a large number of these units considerably increases on-site natural gas fuel consumption all year round. Results also suggest that an adequate tariff design improves the economic efficiency of the system and the operation of micro-CHPs under an intelligent control strategy. When the price signal sent to customers reflects the system's short-term marginal price, the operation of the micro-CHPs is more efficient, and with minimum excess heat. Moreover, findings show that a production subsidy in the form of a buy-back rate impacts the operation of micro-CHPs which may distort the short-term marginal price signal. Depending on the tariff rate, micro-CHPs may favor electricity-only production, resulting in increased costs, increased excess heat, and decreased efficiency. In addition, it was shown that a flat electric tariff rate may result in similar results as with an hourly retail rate, in particular for micro-CHP technologies with medium to high heat-to-power ratio. In the end, the goal of this research is to have a better understanding of the conditions that influence the penetration of micro-CHPs, the economic signals that impact their operation, and the complexities that a widespread penetration brings to energy systems. We observe that this technology lends itself to qualitatively different ways of providing electricity service at value as seen by the customers. Future research is needed to explore potential of micro-CHPs for including customer choice. / by Karen de los Ángeles Tapia-Ahumada. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2011. / Page 150 blank. Cataloged from PDF version of thesis. / Includes bibliographical references (p. 138-149). / Human supervisory control systems are characterized by the computer-mediated nature of the interactions between one or more operators and a given task. Nuclear power plants, air traffic management and unmanned vehicles operations are examples of such systems. In this context, the role of the operators is typically highly proceduralized due to the time and mission-critical nature of the tasks. Therefore, the ability to continuously monitor operator behavior so as to detect and predict anomalous situations is a critical safeguard for proper system operation. In particular, such models can help support the decision making process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected. By exploiting the operator behavioral patterns which are typically reinforced through standard operating procedures, this thesis proposes a methodology that uses statistical learning techniques in order to detect and predict anomalous operator conditions. More specifically, the proposed methodology relies on hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) to generate predictive models of unmanned vehicle systems operators. Through the exploration of the resulting HMMs in two distinct single operator scenarios, the methodology presented in this thesis is validated and shown to provide models capable of reliably predicting operator behavior. In addition, the use of HSMMs on the same data scenarios provides the temporal component of the predictions missing from the HMMs. The final step of this work is to examine how the proposed methodology scales to more complex scenarios involving teams of operators. Adopting a holistic team modeling approach, both HMMs and HSMMs are learned based on two team-based data sets. The results show that the HSMMs can provide valuable timing information in the single operator case, whereas HMMs tend to be more robust to increased team complexity. In addition, this thesis discusses the methodological and practical limitations of the proposed approach notably in terms of input data requirements and model complexity. This thesis thus provides theoretical and practical contributions by exploring the validity of using statistical models of operators as the basis for detecting and predicting anomalous conditions. / by Yves Boussemart. / Ph.D.

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2007. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / "June 2007." / Includes bibliographical references (leaf 65). / Retailers collect information regarding consumer purchases on a transactional basis. This data is not completely being leveraged by manufacturers in the footwear and apparel industry to increase on-shelf availability. However, certain apparel and consumer products companies have developed best-in-class methods for collecting and utilizing data to enhance supply chain visibility and to drive increased sales. A description of these best-in-class practices is provided, strategies to use the data are presented, and the importance of collaboration among supply chain partners is discussed. Further, point of sale data from a footwear and apparel manufacturer is analyzed to illustrate how the data can be leveraged to predict subsequent season sales, to improve forecasting accuracy, and to allocate replenishment inventory more effectively. / by Jeffrey Edward Axline [and] Brian Joseph Lebl. / M.Eng.in Logistics

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 153-162). / Military intelligence analysts are increasingly tasked to sift through enormous volumes of data to identify the proverbial intelligence "needle in a haystack." One specific domain exemplifying this new intelligence paradigm is network analysis of terrorist organizations. This area of intelligence analysis uses mostly commercially available software applications to leverage the powers of social network theory against large terrorism data sets. An additional challenge is the fast paced development cycle for new sensors, which are capable of collecting data at unmanageable rates. As such, analysts are in dire need of new analytical techniques that give them the ability to effectively and efficiently transform the collected data into intelligible information and, subsequently, intelligence. Therefore, the primary focus of this thesis is to analyze two visualization techniques within social network analysis, with the intent to identify which mode of visualization is most effective for the intelligence tasks of: 1) identifying leaders and 2) identifying clusters. To test the effectiveness of the visualizations, an experiment was conducted in which participants exploited matrix and node-link visualizations constructed from a surrogate terror data set. The objectives of this experiment were to test the effectiveness of the node-link visualization compared to the matrix visualization, based on two criteria: 1) effectiveness at identifying leaders and clusters within a network, and 2) the time it takes to complete each task. Participants in the experiment were all Air Force intelligence analysts and the experiment utilized a 2 (Visualization) x 2 (Task) mixed design study within-subjects on the visualization task factor and between-subjects on the visualization technique factor. The node-link visualization resulted in statistically significantly better performance in all studied scenarios where the objective was identifying leaders. Although node-link also returned a better performance than the matrix for identifying clusters, there was not a statistically significant difference. The same lack of statistical significance holds true for the completion time dependent variable. In all cases, there was not enough difference between the times produced by the node-link and matrix to determine if either offers a statistically significant decrease in the time it takes to complete tasks using either visualization. At this time, the matrix should not be universally integrated into the current methodologies used by analysts to exploit terror network visualizations until more research is conducted into the respective strengths and weaknesses within the intelligence domain. However, analysts should be independently encouraged to explore and adapt new methods of visualization into their current practices and identify new or improved versions of the visualizations identified within this thesis for future testing. / by Christopher W. Berardi. / S.M.

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2004. / Includes bibliographical references. / The thesis addresses the inventory planning process for NFL Replica jerseys. The analysis is conducted from the perspective of the manufacturer's North American distribution center, and how flexibility can be employed to meet customer demands. NFL replica jerseys can be stocked either completed with player name and number, called "dressed" or as "blank" jerseys that can be customized at the distribution center. Player demand can change drastically from year to year. The result is that common practice is to minimize inventory at year-end, and treat each season as a single period. The approach taken utilizes the newsvendor model to determine the optimal stocking levels of replica jerseys given an expected demand forecast. Two modeling approaches were compared, the traditional newsvendor problem and a newsvendor model with risk pooling. The traditional newsvendor problem separated selected players to order as dressed jerseys and remaining demand to order as "blank" jerseys. The second approach, the newsvendor with risk pooling, provides a more flexible inventory plan that satisfies selected player demand using a combination of dressed and blank jerseys. The newsvendor model with risk pooling resulted in the higher expected profits then the traditional newsvendor model, and comparable service levels, but at much lower inventory levels. / by John C.W. Parsons. / M.Eng.in Logistics

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 92-93). / In the truckload transportation industry in the United States, a number of indexes are published that attempt to measure changes in rates, but no single index has emerged as an industry standard. Industry participants, particularly those exposed to the spot-market, have found that existing indexes do not effectively represent their experiences in the marketplace. A widely-used and valid spot-market index could allow for more effective tactical decision-making, the development of freight derivatives, stronger analysis and negotiation of contract rates, and contracts with index-tied rates. This paper examines pricing indexes from other industries and expands upon a framework of characteristics that support successful indexes. Using this framework to evaluate existing industry indexes, it finds that two commonly used indexes are not designed appropriately for use by the spot-market. It also examines rates experienced by a large North American provider of non-asset based logistics services and finds that they differ significantly from rates measured by existing indexes. The analysis suggests that indexes of the spot market would be improved by disaggregating rate information based on geography and tender lead time. / by Andrew Bignell. / M.Eng.in Logistics

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Engineering Systems Division, 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 45-46). / PharCo (an assumed name) is a leading global healthcare company with well-recognized brands of both pharmaceutical and consumer healthcare products. As PharCo continues to expand its global presence, product stock-outs in their pharmaceutical business unit have been consistently increasing. PharCo suspected that manufacturing quality defects were a major cause of stock-outs, reducing the production yield and preventing the company from meeting customer demand. To help test this hypothesis and address the stock-out challenge, we reviewed existing research on the subject of product stock-outs within the pharmaceutical industry. To understand PharCo's manufacturing process, we conducted on-site visits and reviewed their quality control practices. Finally, we designed a mixed methods approach that combines qualitative and quantitative techniques to analyze the root causes of product stock-outs at PharCo. The analysis revealed that, instead of manufacturing quality defects, regulatory issues were the primary cause for stock-outs at PharCo. Regulatory challenges associated with developments such as new product launches, license renewals, and formulation modifications need to be addressed for PharCo to reduce their stock-out level. / by Xuewen Sun and Bangqi Yin. / M. Eng. in Logistics

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005. / Includes bibliographical references (p. 151-152). / The concepts of value and value stream are crucial to the philosophy of Lean, and a better understanding of how these concepts relate to product development (PD) is essential for the creation of a Lean PD strategy. This thesis focuses on value by looking at PD processes through two different value perspectives: Product Development Value Stream Mapping and Earned Value Management. Product Development Value Stream Maps (PDVSMs) were created for two different PD projects, and the tasks from the maps were analyzed for how they each create value. The official value measurement for the two projects, Earned Value Management System data, was analyzed and compared to the PDVSMs. This comparison of the two value perspectives proved valuable, as it showed that despite some misalignments, they are congruent. The comparison also highlighted several flaws in EVMS. Finally, a combined EVMS/PDVSM hybrid management tool is proposed and discussed. / by Ryan Brent Whitaker. / S.M.