Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 121-126). / The United States Department of Defense (DoD) is a large and complex organization, which employs a capability based requirements planning process. Decisions on capability requirements are made by senior military officers supported by experienced military and civilian staff with subject matter expertise. There are also many other stakeholders involved in defining concepts, identifying missing capabilities (gaps), evaluating proposed capabilities, recommending solutions to fill gaps, and developing and deploying new and improved capabilities. The process is document-driven. As each document arrives, it is reviewed and a validation decision made. The documents are then filed away. One of the problems faced by the DoD is that, while the documents are retained, the knowledge in the documents is difficult to access except by finding, reading, and analyzing the document again. Abstracting the essential information from documents and storing it as data would enable the staff to make connections from new documents filed to older documents that have related information. Understanding the interdependencies among capability requirements would enable highly informed decisions that are more cohesive with the enterprise strategy for portfolio of systems and capabilities. While there have been incremental steps by the DoD to the decision making process with document repositories and document annotations, there are ways to further improve the process to achieve a full data-enabled, capability requirements portfolio management ability. This thesis analyzes capability requirements portfolio management challenges, and presents the findings of proof of concept experiments implementing a data driven Semantic Data Lake solution to augment decision support. The data model developed in this research is a hierarchical, linked data model, derived from the specifications for document based information sources, to demonstrate the potential use cases. A semantic data model ontology was built in the Data Lake platform with a selection of realistic data, to validate that it can support the United States DoD architectures and handle the complexity of information interdependency. Semantic Data Lake accounts for discrete data and their relationships, in addition to qualitative influences to facilitate knowledge and fact representation natively. The research findings suggest that Semantic Data Lake can provide the enablers that present the United States DoD architectural information for decision making in a coherent and dynamic way, conducive to draw conclusions that can affect the outcome of the governing of capability requirements. / by Amlan Das. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 149-152). / This work demonstrates the effectiveness of using the concept of attributes or properties to identify, compare, and select methods for the design of aerospace systems. Growing product development cost trends for these complex systems have been alarming to the aerospace industry. To curb rising development costs by improving the product design actions that drive them, the issue is viewed from a "design system" perspective that distinguishes between the product design process - the set of tasks or problems to solve to produce a design - and the set of problem-solving techniques or methods used to complete the tasks. Support of a structured approach for designers to select their methods would help ensure that methods meeting the specific quality, budget and schedule needs of each unique design situation are utilized in a manner that is transparent to the whole design team. This thesis develops a conceptual framework for method selection decision support that combines a multi-form design process-methodology model with a general collection of attributes for characterizing any method. The model and attribute framework enable the discovery and comparison of alternative design methods relative to a given design task's requirements. The framework was validated by a case study on the early system-level conceptual design phase of a recent industry flight vehicle development program. By employing graphical and matrix modeling techniques, primary research and interviews with members of the industry design team empirically substantiated the overall efficacy of the framework, indicated four particular attributes that are especially important for comparing methods, and revealed six contextual factors that influence the attribute-based characterization of a method. / by Ephraim Chen. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. / Page 104 blank. Cataloged from PDF version of thesis. / Includes bibliographical references (pages 101-103). / A review of national health records (NEHR) systems shows that privacy and security risks have a profound impact on the success of such projects. Countries have different approaches when dealing with privacy and security considerations. The aims of this study were to explore how governments can design secure national health records systems. To do that systematically, we developed a framework to analyze NEHR systems. We then applied the framework to investigate the privacy and security risks in these systems. The studied systems demonstrate that getting privacy and security right have a considerable impact on the success of NEHR projects. Also, our study reveals that the healthcare system structure has a substantial impact on the adoption and usage rates of the system. The studied cases uncover many opportunities for improving privacy and security measures in future projects. The framework demonstrates the utility of applying it to the three cases. / by Ahmed S. Alawaji. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 92-99). / Blockchain technology is a peer-to-peer infrastructure based on distributed databases and smart contracts as the business logic. The distributed ledger technology eliminates the need for intermediaries disrupting the ownership model. It can have a tremendous impact on cross-organizational process automation when combined with other innovative technologies such as machine learning and additive manufacturing. Over the past few years as the blockchain technology concept has increasingly attracted many industries. The logistics and supply chain management industry have also realized its potential applications in enabling transparency, efficient information sharing, and food safety. Several companies have identified possible use cases that could benefit from blockchain over existing IT solutions. Thesis report provides an overview of current state of blockchain adoption, its technology architecture, review of how blockchain technology and smart contract works, and the benefits and challenges involved. Further, provided a deep dive into the problem of food safety, and the food supply chain and logistics ecosystem drivers. Highlighted, the current use cases of blockchain technology in supply chain and logistics along with critical success factors that companies consider essential for blockchain technology adoption. In the interviews conducted, digital innovators and senior executives are fairly positive about the blockchain technology and its benefits. However, factors such as under-developed ecosystem, lack of governance model and regulatory uncertainty impact its adoption. The proposed framework consists of a hybrid architecture of private and public blockchains, enabling immutable record sharing and monitoring while maintaining selective data privacy. / by Shweta Agarwal. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (page 59). / Maywa Denki is a Japanese crossover creative company. It is an art studio as well as a toy design company. Maywa Denki saw the value of the meaning expressed in art works, and managed to turn such expression into the attractiveness of products. The possible connection of art and product design is to provide meaning for the public with skillful expression. Such expression has its own techniques, one of which used by Maywa Denki could be traced back to the technique used in ritual objects in traditional culture. Maywa Denki is a successful crossover company because it manages to promote individual expression. Inspired by Maywa Denki products, I proposed my own work "the Floating World". It is based on the observation of individual living condition and it includes graphic design, story-telling, object design and short videos. This thesis analyzed that technique of Maywa Denki, Maywa Denki's crossover product development path and described my own design work. / by Tingyun Yin. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 86-89). / As we are at the beginning of the Second Machine Age, where Al, Machine Learning, and Robotics technologies are increasingly influencing this revolution, we are experiencing significant automation changes in many industries such as warehousing and distribution centers. Many of the jobs in these industries aren't just being transformed but also partially or fully automated, often replacing the lowest skilled workers. Even though the core technologies driving automation today are improving exponentially, there are still many areas where human workers exceed and thrive. Some of the jobs might be automated, but there are some tasks which prove to be difficult for machines to perform. The research tries to understand how technology is automating tasks within warehousing jobs right now? By applying rigorous metrics, developed by Erik Brynjolfsson and Tom Mitchell to jobs within warehouses, the thesis aims to show which tasks within these jobs have the highest suitability for machine learning and robotics automation. The research includes the analysis of the not automated tasks and the possible reasons and opportunities for automation. / by Izabela Witoszko. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. / "June 2018." Cataloged from PDF version of thesis. / Includes bibliographical references (pages 57-60). / Gifted and talented students with coexisting learning disabilities, also known as twice-exceptional, are increasingly recognized in U.S. schools. This increasing awareness needs to be met with improved legal protection, better methods for identification and optimized teaching strategies for the unique needs of these students. For this thesis, literature from a range of disciplines including education, cognitive science, and psychology regarding twice-exceptionality is examined, with a specific focus on gifted students with language-based learning disabilities like dyslexia, along with commonly comorbid factors such as ADHD and anxiety. The challenges of these students are also modeled using human centered design tools. Personas of archetypal twice-exceptional students are presented to create empathy for them and awareness of their unmet needs. Design frameworks are examined that aim to improve education universally for all students. Research strongly suggests that twice-exceptional students are under-identified and underserved in our schools and that comprehensive, individualized teaching strategies are necessary in order for them to reach their full potential. Teaching methods are outlined that simultaneously highlight strengths and accommodate the challenges of this important group of gifted learners. / by John Francis Stillman. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 56-58). / This paper focuses on designing novel ways to alleviate human and economic impacts caused by weather and climate disasters such as droughts and cyclones. Natural disasters are becoming apparent and continue to grow in number, intensity, and impact. Authorities, organizations and community groups who focus on rebuilding and relief efforts are constantly facing challenges in redevelopment effort, environmental hazards, health care and funding support to help communities become recover and be more resilient. When dealing with aftermath due to natural disaster the communities do have heightened sense awareness and come together to provide the necessities of rebuilding infrastructure. There are short-term actions, such as an evacuation based on the weather forecasting. Can a system that properly communicates with all affected stakeholders to be prepared for the natural disaster. The implemented system takes the appropriate actions thereby by reducing the human and economic impacts. This precious window of opportunity time between the forecast and actual natural disasters is regularly overlooked which affects the recovery and resilience process. This thesis explains how to design a holistic system that can lessen the risk of natural disaster with a system for forecasting, automatic trigger responses and disburse required funding when certain threshold conditions are met prior to natural disasters. The proposed framework takes into consideration of blockchain technologies that are at the relatively early stage of development. The objectives are to develop novel early funding mechanism and explained using conceptual architecture with private blockchain and smart contracts that can be designed to automatically execute early funding mechanism when the natural hazard thresholds are reached. / by Suresh G. Rajan. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 84-86). / Cybersecurity is a growing research area with direct commercial impact to organizations and companies in every industry. With all other technological advancements in the Internet of Things (IoT), mobile devices, cloud computing, 5G network, and artificial intelligence, the need for cybersecurity is more critical than ever before. These technologies drive the need for tighter cybersecurity implementations, while at the same time act as enablers to provide more advanced security solutions. This paper will discuss a framework that can predict cybersecurity risk by identifying normal network behavior and detect network traffic anomalies. Our research focuses on the analysis of the historical network traffic data to identify network usage trends and security vulnerabilities. Specifically, this thesis will focus on multiple components of the data analytics platform. It explores the big data platform architecture, and data ingestion, analysis, and engineering processes. The experiments were conducted utilizing various time series algorithms (Seasonal ETS, Seasonal ARIMA, TBATS, Double-Seasonal Holt-Winters, and Ensemble methods) and Long Short-Term Memory Recurrent Neural Network algorithm. Upon creating the baselines and forecasting network traffic trends, the anomaly detection algorithm was implemented using specific thresholds to detect network traffic trends that show significant variation from the baseline. Lastly, the network traffic data was analyzed and forecasted in various dimensions: total volume, source vs. destination volume, protocol, port, machine, geography, and network structure and pattern. The experiments were conducted with multiple approaches to get more insights into the network patterns and traffic trends to detect anomalies. / by Lwin P. Moe. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 70-74). / Artificial intelligence - making machines intelligent - is a methodology to build, train, and run machines that are capable of making decisions on its own. Artificial intelligence technologies are gaining significant adoption across a wide range of activities in an organization across different industries. This is fueled by increasing focus on data-driven decision-making methods for all kind of tasks (external or internal) in an organization. Venture capital industry - traditional sub-segment of financial services industry - works heavily on human interactions and relationships. Venture capital investments are considered high-risk, high-return asset class. Venture investment decision-making could be optimized by machine learning applied to previous deals, company data, founder data, and more. It is quite possible that a system could analyze founder personalities, company metrics, and team attributes and improve venture capitalist's decision-making. This thesis is an attempt to analyze and breakdown venture capitalist decisions and understand how Artificial Intelligence tools and techniques could be utilized by VCs to improve decision-making in venture capital. By focusing on the decision-making involved in the following eight value chain areas of a venture capital firm - deal sourcing, deal selection, valuation, deal structure, post-investment value added, exits, internal organization of firms, and external organization of firms, we could discover the extent to which artificial intelligence tools and techniques could be used to improve human decision-making in the venture capital industry. Subsequently, we could also identify how artificial intelligence could be practically used in such decision-making scenarios and also the benefits and associated risks involved in using artificial intelligence system in venture capital decision-making. / by Chahat Jain. / S.M. in Engineering and Management