Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 76-77). / This thesis presents methods for determining the effects of risk from disruptions using an aerospace supply chain as the example, primarily through the use of a computer simulation model. Uncertainty in the current marketplace requires managers to be cognizant of the adverse impact of risk on their company's performance. However, managers who lack formal procedures for dealing with the potential impact of risk often are caught not knowing how much to invest in risk mitigation strategies. A computer simulation model representing a supply chain for a space vehicle was used to test different disruption scenarios to determine their impact on total production duration time. Scenarios ranging from suppliers not providing parts on time to quality test failures to disease pandemics were all considered. Randomness was incorporated through use of a stochasticity factor that was applied uniformly throughout the model. Output of the model was used to develop confidence percentiles for the complete duration times. Through testing of the various scenarios using the model we learned that most disruptions will add a deterministic time to the total estimated duration time of the system, regardless of the location of the disruption in the supply chain. In addition, we showed that a thorough review must be performed when choosing the stochasticity factor due to its large influence in determining total duration times and performance percentiles. The creation of the confidence percentiles allows the aerospace company to use the model throughout the entire 3 to 4 year production process to continually update and evaluate their buffer times and likelihood of meeting target completion dates. This buffer time can then be turned into a key performance index to better manage this supply chain. This model was created for a real supply chain, and it is currently being used by the aerospace company to help them plan and make appropriate decisions in regards to risk mitigation strategies in preparation for production of the space vehicle. They hope to expand the use of computer simulation models throughout the rest of their division to help drive down costs by increasing efficiencies in their planning. / by Bryan K. Wilson. / M.Eng.in Logistics

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2011. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 64-66). / Promotional events are a common occurrence in the grocery and drug industries. These events require consumer packaged goods manufacturers to deliver a large volume of product, beyond the typical demand, to the retailer in a short period of time. Two of these manufacturers, Manufacturer A and General Mills, are interested in exploring the benefits of an innovative distribution strategy: collaboratively shipping their promotional products direct to the retailer stores. This thesis describes a modified minimum cost flow optimization model, which was developed to compare the costs of this multi-manufacturer collaborative distribution strategy with two more traditional distribution approaches in which each company would deliver product independently. The first traditional strategy entails independently delivering product to the retailer distribution center, from where the retailer would transport the product to the stores. The second traditional strategy involves each manufacturer independently delivering directly to the retailer stores. Using a retailer that participated in a trial implementation of this collaborative distribution strategy in 2010 as a case study, the model is solved to find the lowest cost distribution strategy for the region served by each retailer distribution center. Results show that collaborative distribution is the most cost effective strategy in two thirds of the regions that were studied, and that this finding is fairly robust with respect to the input parameters. However, cost savings to the supply chain from employing the optimal strategy are relatively small, with savings to the retailer coming at an additional expense to the manufacturers. Therefore, this thesis concludes that the manufacturers' incentive to employ collaborative distribution depends upon a method of sharing savings with the retailer, or upon the expectation of increased revenue due to higher sales from employing this distribution strategy. / by Nanette Thi Le and Melanie Ann Sheerr. / 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. 105-109). / Identifying the factors that could lead to the loss of quality is difficult for large, complex systems. Traditional design methods such as Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and Robust Design have been proven effective at the component level but are less effective for factors that involve interactions between components, software flaws and external noises. This thesis applies System Theoretic Process Analysis (STPA) to two case studies at Cummins, Inc. The first case study was a technology change to a subsystem in a new product development project. The intent of this case was to determine if STPA, applied broadly to safety and hazard analysis, would be effective in identifying causes of quality losses. The second case was a historical quality improvement project. The intent of this case was to determine if STPA would be effective for developing solutions to causes of quality losses. The results of the case studies were compared to the traditional design methods. Use of STPA allowed the design teams to identify more causal factors for quality losses than FMEA or FTA, including component interactions, software flaws, and omissions and external noises. STPA was also found to be complementary to Robust Design Methods. Finally, use of STPA was effective for analyzing the complete hierarchical structure of the system for solutions to potential causes of quality losses. / by Stephanie L. Goerges. / S.M.

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008. / Includes bibliographical references (leaves 69-70). / This thesis is to analyze the suitability of different products, suppliers and customers for Direct Store Delivery (DSD) model with respect to the qualitative profile and the quantitative benefits. During the research, interviews with retailers, suppliers and industrial experts provide the basis and insight for the qualitative analysis of factors that make certain products, suppliers and customers best suitable for a DSD model. In order to quantify the benefits that DSD can bring to the entire supply chain, a generic model of the DSD system is built. Based on the quantitative analysis, the stock-out at store shelf is simulated in order to understand the effects of DSD operations to the minimization of stock-out costs at the store shelf, a major benefit that DSD is assumed to generate. With the conceptual framework and the quantitative model, this thesis is aimed at providing supply chain managers a comprehensive perspective to adopt DSD for their products and customers. / by Liang Chen. / M.Eng.in Logistics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005. / Includes bibliographical references (p. 327-339). / (cont.) market. The method is also used to extract quantitative evidence indicating the existence of enterprise-related attributes for consumer value in products. Marking the first independent review of the loss function-based value method, this study finds that the Relative Value Index is superior to existing value methods at retaining simplicity of implementation and minimal data requirements while maintaining a firm grounding in economics and consumer choice theory. The method is shown to be useful for estimation, though robustness of the results is not certain when used in this manner, and may also be extended to the analysis of large-scale engineering systems and their value to society. / The early phase of product development, sometimes referred to as the fuzzy front-end, is critical to the success of enterprises and plays a dominant role in the formation and execution of corporate strategy. In addition, it has been argued that the concept of consumer value is central to effective product development. In this research, a new product value assessment method is established for the fuzzy front-end of business airplane development. Existing value assessment techniques used in the business aviation industry are found to poorly balance the theoretical rigor of the method with the ease of use and accuracy required by practitioners in early product development. A recently-developed multi-attribute value method, based on Taguchi's loss function approach to quality assessment, is modified and extended in this study and applied for the first time to the domain of business aviation. A comprehensive 40-year historical product database is developed for use in testing and evaluating the method, referred to as the Relative Value Index (RVI), enabling the scope of value method appraisal to be expanded to an industry-wide examination over a significant time span. A top-down approach is developed for calibrating value models to empirical market data via attribute weighting factors. Sensitivity analyses and Monte Carlo simulations are developed to test the RVI method's robustness and the reliability of the results, enabling a rigorous definition of the determinants of product competition in this industry. This methodology is a useful advance in the methods to extract objective findings from historical industry market activities. The RVI approach is used to develop evidence in support of a ratio theory of product price and value differentiation in the business airplane / by Troy D. Downen. / Ph.D.

Thesis: Ph. D. in Technology, Management, and Policy, Massachusetts Institute of Technology, Engineering Systems Division, 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 141-148). / The growth of location-constrained renewable generators and the integration of electricity markets in the United States and Europe are forcing transmission planners to consider the design of interconnection-wide systems. In this context, planners are analyzing major topological changes to the electric transmission system rather than more traditional questions of system reinforcement. Unlike a regional reinforcement problem where a planner may study tens of investments, the wide-area planning problem may consider thousands of investments. Complicating this already challenging problem is uncertainty with respect to future renewable-generation location. Transmission access, however, is imperative for these resources, which are often located distant from electrical demand. This dissertation frames the strategic planning problem and develops dimensionality reduction methods to solve this otherwise computationally intractable problem. This work demonstrates three complementary methods to tractably solve multi-stage stochastic transmission network expansion planning. The first method, the St. Clair Screening Model, limits the number of investments which must be. The model iteratively uses a linear relaxation of the multi-period deterministic transmission expansion planning model to identify transmission corridors and specific investments of interest. The second approach is to develop a reduced-order model of the problem. Creating a reduced order transformation of the problem is difficult due to the binary investment variables, categorical data, and networked nature of the problem. The approach presented here explores two alternative techniques from image recognition, the Method of Moments and Principal Component Analysis, to reduce the dimensionality. Interpolation is then performed in the lower dimensional space. Finally, the third method embeds the reduced order representation within an Approximate Dynamic Programming framework. Approximate Dynamic Programming is a heuristic methodology which combines Monte Carlo methods with a reduced order model of the value function to solve high dimensionality optimization problems. All three approaches are demonstrated on an illustrative interconnection-wide case study problem considering the Western Electric Coordinating Council. / by Pearl Elizabeth Donohoo-Vallett. / Ph. D. in Technology, Management, and Policy

Thesis: Ph. D., Massachusetts Institute of Technology, Engineering Systems Division, 2011. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 120-126). / An important question for a large company with multiple product development projects is how standard or varied the sets of activities it uses to conceive, design, and commercialize products should be across the organization. To help address this question, this project is comprised of three research activities to improve understanding of the influence of standardization of product development processes on performance. Previous research indicates that process standardization has many positive (improved efficiency, knowledge transfer, decision making and resource allocation) and negative (reduced creativity, innovation, adaptation and learning, employee satisfaction) performance effects. Even focusing on specific performance outcomes, the influence of process standardization is contested. The first phase was a set of theory-building case studies at five large companies that develop electromechanical assembled products. One important lesson from the case studies was that to appropriately evaluate the impact of standardization on performance it is essential to disaggregate the process into its individual 'dimensions' (activities, deliverables, tools, etc.) because standardization on different dimensions of the process impacts performance outcomes quite differently. Another lesson was that companies differ in their process standardization approach because of differences in their portfolio characteristics and in their strategic priorities across performance outcomes. Based on the importance of focusing on individual process dimensions, a broad and systematic literature study was conducted with the aim of better capturing the current state of knowledge. This literature study resulted in a framework to characterize the problem space, a comprehensive set of relevant project characteristics, process dimensions, and performance outcomes and a summary of the established links, contested links, and unexplored links between these elements. Focusing on one set of contested links from the literature, the final research activity was a detailed empirical study at one company. The goal was to study the effect of variation in project-level product development processes, operating under the guidance of an established process standard, on project performance. The purpose-assembled data set includes measures of project characteristics, process dimensions, and project performance outcomes for 15 projects. Statistical analyses were performed to examine the relationships between process variation and project performance outcomes. Where possible, the statistical analyses were supported and enriched with available qualitative data. The results indicated that, at this company, process variation in the form of both customization and deviation was associated with negative net outcomes. Customization (in the form of combining project reviews) was associated with reduced development time and development cost, but also with lower quality, likely because of reduced testing. On net, in dollar terms, combining reviews was associated with negative outcomes. Specific deviations (in the form of waived deliverables) were also associated with negative performance consequences. Results also supported the lessons from Phase 1. Variation on different process dimensions was associated with different performance outcomes. Disaggregation was important, with many insights lost when deviations were aggregated. This project enhanced our understanding of the performance impacts of product development process standardization. The case studies highlighted the importance of disaggregating to individual process dimensions to correctly evaluate the effects of standardization. The systematic literature study resulted in a framework for organizational decision making about process standardization and a summary of the current state of knowledge - elements, established links, contested links, and unexplored links. The detailed empirical study at one company examined one set of contested links - between process standardization and project performance - and found that process variation in the form of both customization and deviation was associated with net negative effects on project performance. / by Sidharth Rupani. / Ph. D.

<p>This praxis develops a simulation-based approach to analyzing the overall reliability of complex systems with high degrees of redundancy, time varying event rates, and the potential for common cause failures. This approach is compared to traditional analytic approaches, and is shown to have some advantages, primarily by avoiding some of the simplifying assumptions used in those approaches. </p><p> Several canonical problems are solved using both traditional and simulation-based approaches to elucidate the method, and the method is then applied to more complex problems for which exact analytic solutions are not available. The method is shown to be flexible to both traditional industrial plant reliability problems and to a new class of problems involving the reliability of swarming unmanned vehicles, where there is a high degree of parallelism and dynamic formation of common cause groups. </p><p> The penultimate chapter examines the impact of common cause failures on the reliability of a swarm of unmanned vehicles performing a search mission, and develops a simulation-based approach to modeling the reliability of swarms in the presence of both independent (single vehicle) and common cause (multiple vehicle) failures. The modeling approach is exercised on a sample problem to illustrate how it can be used as part of a system design or search-planning tool for swarming unmanned vehicles. The simulation provides insight on the impact of design decisions that influence overall system reliability; it also provides metrics of success in a search scenario as a function of user-selectable parameters. </p>

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Engineering Systems Division, 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 54-55). / Improvement in sales forecasting allows firms not only to respond quickly to customers' needs but also to reduce inventory costs, ultimately increasing their profits. Sales forecasts have been studied extensively to improve their accuracy in many different fields. However, for automotive batteries, it is very difficult to develop a highly accurate forecast model because many variables need to be considered and their correlations are complex. Additionally, current sales forecasts are derived from historical data and thus do not include any other causal factor analysis. In this study we applied causal factor analysis to determine how the forecast accuracy could be improved. We focused on understanding the relationship between temperature and sales. Using regression modelling, we found that there is a quadratic relationship between temperature and battery sales. We validated the model by comparing the actual and predicted sales for various geographies and times. We concluded that the model is more robust for predicting sales across various times than through various geographies. / by Vinod Bulusu and Haekyun Kim. / M. Eng. in Logistics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2012. / "September 2012." Cataloged from PDF version of thesis. / Includes bibliographical references. / Growing concerns about the energy use and climate impacts of the transportation sector have prompted policymakers to consider a variety of options to meet the future mobility needs of the world's population, while simultaneously addressing the impact of these systems on our environment. This dissertation focuses on air transportation and high-speed rail (or "high-speed transportation"), a sector for which demand is projected to grow substantially, and for which infrastructure and vehicle investment decisions are costly and long-lived. This research examines high-speed rail (HSR) and aviation systems in three regions to explore: 1) the historical context of high-speed rail and aviation demand; 2) potential policies that may shape HSR and aviation system demand and their environmental impacts in the future; and 3) individual mode choice between HSR and aviation. The goal of this work is to improve our understanding of demand for these systems and methods to examine their climate impacts. Chapters 3 and 4 provide an empirical analysis of the European experience with highspeed rail and aviation systems. First, we contribute to econometric analyses of air travel demand by examining the impact of high-speed rail on air traffic. Using origin-destination demand data as well as airport demand data for two decades, our econometric analysis shows that the introduction of high-speed rail has resulted in substantial decline in air traffic on short-haul routes, as well as domestic air traffic in nations with high-speed rail infrastructure. Those cities that have higher density experience an even larger reduction in air traffic. However, we find that over this same time period, the expansion of low-cost carriers in Europe has had an even more substantial impact on increasing total air traffic within the European aviation system. Second, we explore cooperation versus competition between high-speed rail and air transportation systems. Through case studies and travel demand analysis, we examine how air-rail connections have formed in Europe, factors that contribute to high utilization of these connections, and their impact on travel demand. We find that although capacity shifts within the air transportation system have occurred as a result of these connections, there are a number of unique factors that contribute to their success. In Chapter 5, we shift our attention to the United States to conduct an integrated analysis of transportation and climate policies. By developing a new model to examine high-speed rail and aviation demand and their environmental impacts under alternative climate and energy policies, we find that the energy and CO2 emission savings of high-speed rail increase substantially when combined with such policies. These savings are primarily due to the relative efficiency of high-speed rail systems combined with a shift towards less carbon-intensive efficiency of high-speed rail systems combined with a shift towards less carbon-intensive electricity generation. The first three analyses assume that price and travel time are the dominant factors influencing intercity travel choice between high-speed rail and air transportation. In Chapter 6, we explore the potential influence of environmental attitudes on individual choices between high-speed rail and air transportation. By conducting an intercept survey in China, we find that rail passengers tend to be more concerned about the environment than those individuals likely to choose air travel. Second, we find that high-speed rail accidents do have a significant impact on future mode choice, and that safety concerns play a significant role in intercity travel choice. This dissertation concludes with an analysis of current policies that influence high-speed rail and aviation in the United States, and their long-range environmental impacts. Integrating our findings from the three regional analyses of high-speed rail and aviation, we make recommendations for future policies that shape these long-lived infrastructure systems. Given likely growth in demand for high-speed transportation in the United States and other regions, our goal is to inform future investment decisions and policies that meet these mobility needs while mitigating their energy and climate impacts. / by Regina Ruby Lee Clewlow. / Ph.D.