Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Engineering Systems Division, 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages [72]-[74]). / The handicraft sector in Morocco represents more than 9% of the nation's GDP and employs 2.3 million people. However, despite their talent and expertise, Morocco's artisans are struggling to reach markets other than local regional or national markets. The main distribution model used so far has been the classic retail distribution model, which does not add value to the artisans' work, with most downstream actors playing the role of the middlemen. Other Ecommerce models such as the marketplace model or the crowdfunding model did not solve all the issues faced by artisans and customers. This paper provides the framework for a new distribution channel for Moroccan artisans to improve the efficiency of the value chain linking them to the American consumer by eliminating all unnecessary steps, overhead, and overall inefficiencies of the current distribution models. We first identify the models that are currently used to bring the artisans' crafts to the end consumer in developed markets: the market place (used by the platform Etsy), the crowdfunding model and the classic retail distribution model involving middlemen. We assessed the benefits of all the models by developing a new framework allowing to score each one of them. We then switched our focus to a hybrid model: a crowdfunding platform where the company partners with the artisans to align the different stakeholders' interests. To the extent of our knowledge, this model has not been used yet. It was found that our methodology could help artisans access international markets more efficiently. We finished by determining the optimal strategy using social media to market this platform and engage more efficiently potential customers. Keywords: handicraft, supply chain, crowdfunding, retail, social network marketing. / by Zyad El Jebbari. / M. Eng. in Logistics

Thesis (S.M. in Technology Policy)--Massachusetts Institute of Technology, Engineering Systems Division, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 109-125). / Mercury is a toxic pollutant that endangers human and ecosystem health. Especially potent in the form of methyl mercury, exposure is known to lead to adverse neurological effects, and, a growing body of evidence suggests, cardiovascular ones. Mercury's health impacts have economic consequences, and benefit-cost analyses focusing on these health benefits are used to motivate regulatory action in the United States and elsewhere. However, many existing valuation studies of the health impacts of mercury have substantial limitations, both from a scientific and economic perspective. Because they do not fully model mercury's path from emissions to impacts, they do not fully reflect the spatial and temporal dimensions of the mercury problem. In addition, many do not consider uncertain, but potentially policy-relevant health effects like cardiovascular disease. This thesis develops an integrated assessment framework that more completely represents mercury's emissions-to-impacts path, and then evaluates its policy relevance. The assessment framework integrates chemical transport modelling, exposure and health impacts modelling, and general equilibrium modelling of the US economy. As a case study, the framework is used to evaluate the benefits of the Mercury and Air Toxics Standards-a recent US regulation that targets emissions from coal-fired power plants-until 2050. I estimate the annual benefit of MATS to be 13 million 2005 USD, compared to a scenario that includes stringent air quality policy, and 414 million 2005 USD when compared to a no policy scenario. I find that the estimate is highly sensitive to uncertainties along the emissions-to-impacts path-in particular, dose-response parameterization, ecosystem lag times, and discount rate. The analysis suggests that given the large ranges of uncertainty involved, more fully representing the emissions-to-impact chain does not lead to substantially different aggregate benefits estimates, compared to those existing in the literature. However, because this approach does provide more insight into the controlling influences behind benefits, it can inform decisions about where policies should be implemented, and of what type, as well as best practices for transparently assessing mercury-related policies. / by Amanda Giang. / S.M.in Technology Policy

Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Engineering Systems Division, 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 47-48). / Forecasting demand for limited-life cycle products is essentially projecting an arc trend of demand growth and decline over a relatively short time horizon. When planning for a new limited-life product, many marketing and production decisions depend on accurately predicting the life cycle effects on product demand. For products with stable market shares, forecasting demand over the life cycle benefits from the high degree of correlation with prior sales of similar products. But for volatile-share markets, rapid innovation continually alters the shape of available features and performance, leading to products with demand patterns that differ greatly from prior generations and forecasting techniques that rely more on judgment and naive expectations. In an effort to understand opportunities and limitations of quantitative forecasting in a specific volatile-market context, we hypothesized certain characteristics about the shape and volatility of the demand trend in volatile-market product, and tested them using sample stable and volatile market data from a partner firm. We found significant differences in quantifiable characteristics such as skew and variance over the life cycle, presenting an opportunity for supply chain stakeholders to incorporate life cycle effects into forecasting models. / by Alexander Baldwin and Jaesung Shin. / M. Eng. in Logistics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 193-212). / This dissertation focuses on the capabilities of a novel public health data system - the Sentinel System - to supplement existing postmarket surveillance systems of the U.S. Food and Drug Administration (FDA). The Sentinel System is designed to identify and assess safety risks associated with drugs, therapeutic biologics, vaccines, and medical devices that emerge post-licensure. Per the initiating legislation, the FDA must complete a priori evaluations of the Sentinel System's technical capabilities to support regulatory decision-making. This research develops qualitative and quantitative tools to aid the FDA in such evaluations, particularly with regard to the Sentinel System's novel sequential database surveillance capabilities. Sequential database surveillance is a "near real-time" sequential statistical method to evaluate pre-specified exposure-outcome pairs. A "signal" is detected when the data suggest an excess risk that is statistically significant. The qualitative tool - the Sentinel System Pre- Screening Checklist - is designed to determine whether the Sentinel System is well suited, on its face, to evaluate a pre-specified exposure-outcome pair. The quantitative tool - the Sequential Database Surveillance Simulator - allows the user to explore virtually whether sequential database surveillance of a particular exposure-outcome pair is likely to generate evidence to identify and assess safety risks in a timely manner to support regulatory decision-making. Particular attention is paid to accounting for uncertainties including medical product adoption and utilization, misclassification error, and the unknown true excess risk in the environment. Using vaccine examples and the simulator to illustrate, this dissertation first demonstrates the tradeoffs associated with sample size calculations in sequential statistical analysis, particularly the tradeoff between statistical power and median sample size. Second, it demonstrates differences in performance between various surveillance configurations when using distributed database systems. Third, it demonstrates the effects of misclassification error on sequential database surveillance, and specifically how such errors may be accounted for in the design of surveillance. Fourth, it considers the complexities of modeling new medical product adoption, and specifically, the existence of a "dual market" phenomenon for these new medical products. This finding raises non-trivial generalizability concerns regarding evidence generated via sequential database surveillance when performed immediately post-licensure. / by Judith C. Maro. / Ph.D.

Thesis: Ph. D., Massachusetts Institute of Technology, Engineering Systems Division, 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 187-194). / In the last 15 years, the use of rail infrastructure by different train operating companies (shared railway system) has been proposed as a way to improve infrastructure utilization and to increase efficiency in the railway industry. Shared use requires coordination between the infrastructure manager and multiple train operators. Such coordination requires capacity planning mechanisms that determine which trains can access the infrastructure at each time, capacity allocation, and the access charges they have to pay, capacity pricing. The objective of this thesis is to contribute to the field of shared railway systems coordination by 1) developing a framework to analyze the performance of shared railway systems under alternative capacity pricing and allocation mechanisms, and 2) using this framework to understand the implications of representative capacity pricing and allocation mechanisms in representative shared railway systems. There are strong interactions between capacity planning and infrastructure operations in the railway industry; the operations on the infrastructure determine the available capacity in the system. As a consequence, the framework developed in this thesis to evaluate the performance of shared railway systems under alternative capacity pricing and allocation consists of two models: 1) a train operator model and 2) an infrastructure manager model. The train operator model is a financial model that anticipates how train operators would respond to the capacity pricing and allocation mechanisms and determine their demand for infrastructure use. The infrastructure manager model is a network optimization model that determines the optimal train timetable (infrastructure manager's decisions) that accommodates the train operators' demands for scheduling trains, considering the topology of the system, safety constraints, and other technical aspects of the infrastructure for shared railway systems. To be able to solve the train timetabling optimization problem in meaningful instances, this thesis develops a novel approximate dynamic programming algorithm based on linear programming that extends previous algorithms proposed in the literature to effectively solve large network optimization problems. This thesis then uses the train operator model to compare the operational decisions of train operators in shared railway systems with the operational decisions of even-handed integrated railway companies. We show that train operators in shared railway system access charges reflect variable infrastructure manager's costs to operate trains on the infrastructure. We also identify two cases in which the train operators may have incentives to deviate from the integrated railway systems' operational decisions: 1) when the infrastructure manager needs to recover part of the infrastructure management fixed costs, or 2) when the railway system is congested. This motivates the choice of the two case studies of this thesis, one based on the Central Corridor in Tanzania, and the other one based on the Northeast Corridor in the US. We then show how to use the framework proposed in this thesis to analyze the trade-offs associated with the use of alternative mechanisms in these two cases. To our knowledge, this is the first effort to compare alternative mechanisms to price and allocate capacity in the same shared railway system. The results of this thesis show that there are important trade-offs associated with each mechanism and none of them is superior to the other on all dimensions. We thus recommend that system stakeholders carefully analyze the implications of alternative capacity pricing and allocation mechanisms before locking the system into one of them. This is particularly important today since several countries are currently restructuring their railway sector to allow shared use. We claim that the improved understanding of the system performance gained with the framework proposed in this thesis is important to be able to design adequate capacity pricing and allocation mechanisms that can mitigate the coordination problems of shared railway systems while maintaining the benefits of shared infrastructure in the railway industry. / by Maite (Maria Teresa) Pena-Alcaraz. / Ph. D.

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2006. / Includes bibliographical references (leaves 61-63). / The healthcare industry, more specifically hospitals, has in recent times been experiencing a steady rise in nursing shortages and cost pressures. To offset these problems hospitals have increasingly relied upon supply chain automation and the use of Automated Point of Use systems (APU) to relieve nurses of their supply management responsibilities and allow them to focus more of their time on patient care, and therefore increase nursing job satisfaction and patient care quality. However, previous studies on the effectiveness of APUs have shown mixed results. It has been argued that nursing's attitude towards automation implementations plays a role in determining the success of new technologies adoption. This research, based on interviews conducted at BBC hospital, a well regarded multi-specialty academically affiliated institution, and with the help of its supply chain partner Primera, shows that nursing's perceived success of BBC's APU implementation depends on four factors - the ability of the new technology to show timesaving, the availability of supplies when needed, the accessibility of supplies when needed, and the perceived quality of the products supplied. / (cont.) In sum, automation implementations must use workflow process changes to add tangible values that nursing perceives as helping nurses to perform their jobs better and with less stress. As for both the current BBC implementation and any future implementations, this study further recommends methods in achieving these values and therefore increasing nursing acceptance of new technology implementations. / by Yue Xie. / M.Eng.in Logistics

Thesis: Ph. D., Massachusetts Institute of Technology, Engineering Systems Division, 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 86-94). / Policies that reduce greenhouse gas emissions can also reduce outdoor levels of air pollutants that harm human health by targeting the same emissions sources. However, the design and scale of these policies can affect the distribution and size of air quality impacts, i.e. who gains from pollution reductions and by how much. Traditional air quality impact analysis seeks to address these questions by estimating pollution changes with regional chemical transport models, then applying economic valuations directly to estimates of reduced health risks. In this dissertation, I incorporate and build on this approach by representing the effect of pollution reductions across regions and income groups within a model of the energy system and economy. This new modeling framework represents how climate change and clean energy policy affect pollutant emissions throughout the economy, and how these emissions then affect human health and economic welfare. This methodology allows this thesis to explore the effect of policy design on the distribution of air quality impacts across regions and income groups in three studies. The first study compares air pollutant emissions under state-level carbon emission limits with regional or national implementation, as proposed in the U.S. EPA Clean Power Plan. It finds that the flexible regional and national implementations lower the costs of compliance more than they adversely affect pollutant emissions. The second study compares the costs and air quality co-benefits of two types of national carbon policy: an energy sector policy, and an economy-wide cap-and-trade program. It finds that air quality impacts can completely offset the costs of a cost-effective carbon policy, primarily through gains in the eastern United States. The final study extends the modeling framework to be able to examine the impacts of ozone policy with household income. It finds that inequality in exposure makes ozone reductions relatively more valuable for low income households. As a whole, this work contributes to literature connecting actions to impacts, and identifies an ongoing need to improve our understanding of the connection between economic activity, policy actions, and pollutant emissions. / by Rebecca Kaarina Saari. / Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005. / Includes bibliographical references (leaves 155-162). / The research contributes to the uncertainty management of engineering systems by proposing and demonstrating a way to implement flexible platform strategy to respond to future uncertainties. In today's competitive market, where market segments are becoming more fragmented, pressure to develop and market diverse sets of products is increasing. To meet such market needs and reduce production cost at the same time, product platform strategy has been implemented in several different industries. Using a core set of common platform elements and variant-specific unique elements, a family of products can be produced to satisfy various market segments. However, the growing cost of platform development and undesired side effects of the strategy (e.g. performance tradeoff, cannibalization) is forcing companies to design their platforms with flexibility, so they can accommodate product variants, differentiate these variants, and be economically flexible to respond to specified future uncertainties. This thesis introduces a design process to architect flexible product platforms. The proposed process is demonstrated in two automotive application case studies. / (cont.) In the first case study, a vehicle floor pan is designed to satisfy two different length requirements, while being economically robust to future specification change and component demand. The second case study investigates a vehicle platform, where the flexible body in white (BIW) platform is designed for a family of three vehicle variants through identification of critical elements subset. Results showed that the flexible BIW platform is less profitable than the inflexible BIW platform, but when the degree of future uncertainty increases, the flexible design eventually becomes more profitable. This research provides additional examples that yet again confirms the general proposition "flexibility gains value as the degree of uncertainty increases." / by Eun Suk Suh. / Ph.D.

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 115-116). / Freight transportation is a complex large scale system that operates under a highly dynamic and uncertain environment. Due to the scale and complexity of the system, a highly interdependent set of decisions are made across multiple planning levels. The interaction between tactical level decisions and execution level decisions largely determines the overall effectiveness of the system. This thesis aims to provide an analysis of how the interaction between the degree of robustness of a tactical plan and the flexibility level of an execution policy affects the performance of freight transportation systems in a dynamic and uncertain environment. Such analysis is conducted under two types of transportation systems, including a generalized distribution system and a military logistic system, that operate under demand uncertainty. Execution policies with different levels of flexibility - defined as the degree of freedom to which decisions can be adjusted at the executional level - are obtained by controlling the stickiness level of the different decisions as the decision maker transitions from the tactical plan to the executional plan. Robustness is used as a metric to measure the ability of a system to withstand random changes. Tactical plans with various degrees of robustness are obtained through the use of robust optimization. The performances of tactical plans with various degrees of robustness and execution policies with different levels of flexibility are evaluated through simulation. Results from the analysis on the distribution system show that the optimal level of robustness required from a tactical plan to achieve the lowest expected total cost decreases as the flexibility level of the system increases. Finally, the analysis on the military logistics system shows that the effect of increasing the flexibility level of the execution policy on the performance of the system depends on the uncertainty level of the demand. / by Atikhun Unahalekhaka. / S.M.

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008. / Includes bibliographical references (leaves 69-70). / The objective of this thesis is to develop a supply chain segmentation model for Company X, which is in the chemical and construction materials industry. The company sells products in an expanding Southeast Asia market. At the same time, it innovates and launches new products to these markets. A major issue for the company to consider is services offered to its customers. The company has to address customer needs, analyze them, and design the products and services that will fulfill those selective demands. This thesis leverages this concern for the company by developing a model to segment the company's supply chain based upon customer services. Company Y, a subsidiary company of Company X, is selected to be a case study for the model developed in this thesis. Quantitatively, the thesis examines collected data such as customer including portions of revenue and margin from each customer; and a customer's profile potential from the size of the firm. Qualitatively, the data and information collected from interviewing relevant people, such as sales and marketing personnel, is used to characterize the company's future customer prospects. Furthermore, some selected current practices in the industry will be reviewed and benchmarked for formulating the model. / by Prakit Worawattananon. / M.Eng.in Logistics