Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 89-92). / Pharmaceutical R&D involves significant uncertainty, including high attrition rate and long time for a project to progress from a target identification phase to commercial launch. Despite this uncertainty, senior leaders must make decisions about R&D portfolio, the impact of which may not be observable for many years. Hence the purpose of this thesis is to understand the current state of Pharmaceutical R&D portfolio management and identify the gaps in R&D portfolio management research. The literature survey revealed that though there are many qualitative and quantitative approaches for the portfolio management of projects in the development phase (i.e. from pre-clinical to phase 3.), the topic of portfolio management in the drug discovery phase (i.e. target validation to lead optimization) have not been well covered in the literature. Hence the problem statement of this thesis is to develop a portfolio management approach for drug discovery. / Portfolio management in pharmaceutical drug discovery space is not only a mathematics problem but also a representation problem in terms of activities, resources, decisions, dependencies, and uncertainties. There is something about the nature of the scope of early research in pharma which makes it different from downstream phases and respective parallels in other sectors. Improved representation can lead to improved prediction in drug discovery phase. Hence as the first step, structured survey was conducted to listen to insights from an experienced professional in the drug discovery domain at NIBR (Novartis Institute of Biomedical Research) to build the required understanding about discovery phase. The survey results helped in identification of the biology, chemistry, medical, marketing, and strategy factors generally taken into consideration during drug discovery project prioritization. / Resource allocation is not considered during project prioritization - even though resource allocation determines the cycle-time that in turn influences the probability of project to reach pre-clinical phase. Proposed semi-quantitative portfolio management approach, which is based on the survey results, incorporates three key aspects of drug discovery project - scope feasibility (science), desirability (market and strategy), and time feasibility (resource allocation and cycle time). Proposed criterion based model for computing scope feasibility and desirability can be uniformly and transparently applied to all the projects across different disease areas and requires discussion between concerned teams to generate required scores. Also, proposed resource allocation model will enable portfolio management teams to generate multiple scenarios (trade spaces) on scope feasibility, time feasibility, and desirability dimensions. / Based on the thresholds, which can be calculated from past data, portfolio team and management in conjunction with other teams such as disease area representatives, chemistry team, marketing etc. can decide the best scenario. The future work needs to focus on validating the proposed portfolio management approaches and models with the real data from past projects in the drug discovery phase in order to enable to organization-wide implementation. / by Ravi Badwe. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 62-64). / Advances in data science and technology promise to help clinicians diagnose and treat certain conditions. But there are other complex and poorly characterized illnesses for which the drivers and dependent variables are not understood well enough to take full advantage of the copious patient data that may exist. For these diseases new techniques need to be explored to gain better understanding of the nature of the disease, its subtypes, cause, consequence, and presentation. Modern genetics have shown that these diseases often have multiple subtypes, as well as multiple phenotypes as indicated by the new laboratory data. Examples of such diseases include common and important illness such as Type 2 diabetes (T2D) - affecting approximately 30 million Americans, Crohn's Disease - 1 million USA suffers, epilepsy - 3.4 million Americans, and migraines - another 3.2 million in the United States. / Our research explores how machine learning (ML) can be applied to these less well understood complex diseases to improve clinical translation and management. This thesis will discuss how unsupervised machine learning techniques can be used for complex phenotype clustering to identify sub-types of T2D for better clinical management and treatment. T2D is a complex heterogenous disease affecting the world's population at rapidly increasing rates. While clinicians now better understand the heterogeneity of the disease, T2D treatment strategies still remain largely based on populations rather than on a specific patient's subtype. This thesis explores the concept of using data analytics and ML to identify sub-types of T2D as the first step in moving towards precision medicine & treatments. / This thesis includes (a) characterization of T2D as a heterogenous disease, (b) existing research attempts to dissect the disease into sub-types based on phenotypes and gene expressions, and their limitations, (c) phenotype clustering analysis on T2D patients using unsupervised machine learning techniques and MIMIC III database, and (d) analysis of the clusters/subgroups in different ways to understand their clinical significance. With multiple iterations of the clustering experiment, this thesis, (a) provides a good test of concept for sub-classification of T2D patients using unsupervised machine learning techniques such as, clustering and dimension reduction, (b) establishes a data pipeline and clustering model framework to be applied to richer datasets, (c) suggests various experiment design options for further analysis, and (d) establishes a direction for future work including advanced modelling techniques and predictive analytics for complex diseases. / by Priyasha Mehta. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 157-158). / As the software industry evolves, various software development methodologies have become widely used in the industry. Most commonly used methodologies are Waterfall and Agile, along with less known alternatives, such as spiral and hybrid methodologies. When deciding on the methodologies, program managers tend to choose one based on the team preference or historical pattern. However, each software project is unique in its own way and has characteristics that are distinct from the previous projects the team has worked on. For each project, it is crucial to adopt a suitable methodology that help teams to produce the software that meets customer needs within schedule and budget constraints. Therefore, a practical question for every program manager is "How to find a suitable methodology for a specific project?" This thesis is aimed to help program managers answer this question. / We first explore how to evaluate each software development methodology based on the two-level decomposition of software development methodology, then analyze the project characteristics based on the situational inputs in three categories: scope, schedule and budget. Thereafter, the thesis proposes a framework to match software development methodology with a specific project. This thesis extends West's work in [1] by introducing a systems approach to assess a software project and a framework to determine the degree of compatibility between a methodology and a software project. The benefits of leveraging the systems approach are: ** The decomposition of methodologies highlights which elements in a methodology play key roles of providing the advantageous ilities over other methodologies. ** The decomposition of a project enables a program manager to evaluate the input elements of a project and gain a systems view on the project characteristics. / The framework allows program managers to compare several candidate methodologies and choose the most compatible one using the mismatch scores, weighted summations that indicate the incompatibilities between the candidate methodologies and the project based on the ilities ranking decided by the program managers. To demonstrate how to use this framework for a real world project, an example project is given. The detailed steps of calculating the mismatch scores between three methodologies and the project are shown. The proposed framework can be used as a guideline for program managers to find methodologies for different projects with the information gathered from project stakeholders. This framework has some limitations. A major one is that, since the framework is quantitative based, induvial experience is used to evaluate the elements of methodologies and factors of projects. / Further work can be done to improve the objectivity of the evaluation through the surveys of industrial experts and members of teams adopting this framework. / by Yuqing Ke. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, May, 2020 / Cataloged from the official version of thesis. / Includes bibliographical references (pages 93-94). / OEM industries are facing increased challenges providing proactive and reactive equipment support. Increased product complexity and the fast rate of technology change make problems difficult to understand, prevent, and resolve. The cost of machine unavailability is extreme, and reliability-based design methods ignore service time as a key contributor to machine unavailability. Serviceability and diagnostics are an important control to minimize customer losses when problems do occur. Methods are needed that identify serviceability needs early in the product development process while managing product complexity. STAMP (System-Theoretic Accident Model and Processes) is an accident causality model developed as a new engineering approach to system safety. While it was originally created for safety, its foundation in systems theory lends itself to other emergent properties, like serviceability. This research demonstrates that STAMP techniques can be applied to address existing serviceability issues and to guide service-friendly system design in early, conceptual design phases. Two case studies, drawn from industry, are explored to verify the effectiveness of applying STAMP to serviceability. Both case studies successfully generated hardware, software, and operator interface design requirements. They also produced recommendations for the product development and support processes. By using STAMP techniques to understand system interactions and strengthen service control structures, OEMs can address many of the challenges they are currently facing providing serviceability and support. / by Hannah M. Slominski. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 51-53). / The US Army pours a multitude of time and resources to ensure a combat brigade is ready to deploy to a theater of combat and has established two premiere training sites in the US for that purpose. In contrast, homestation units do not have a dedicated opposing force unit and must therefore resource from within to maximize effective and efficient training prior to their deployment to one of the Army's two top training facilities. It is imperative that brigades develop processes to enable better training, resource management, and can expeditiously achieve a training objective in preparation for deployment. This thesis uses available systems tools to build a network of the 4 TH Infantry Division mission readiness exercise conducted in June of 2015, provide graphical depictions of the system itself, and seeks to find an optimum solution for both operating costs and time. Through the application of multicommodity modeling, a decrease of time and operating cost was achieved, 11.04% and 25.85% respectively. Additionally, future work may discover further benefits to increase resource management and speed of execution via the multicommodity flow modeling during the planning phase of a brigade-size training exercise. The analysis conducted in this thesis is meant to enhance the military decision making process and cannot replace the requisite critical thinking required by planners at the brigade level and above. / by Benjamin J. Hooker. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references. / Mergers and Acquisitions (M&A) activity has been a widely researched area over the past century by both academic and industry experts. This paper summarizes the various frameworks that have been developed to explain the motivations to conduct M&A. While the frameworks themselves have been developed based on surveys of past success and failures, they are heavily relied upon by various M&A schools of thought to advise present and future strategies for the industry. In comparing these frameworks, the paper summarizes how deal rationales drive success or failure of M&A transactions. I analyze the HP-Autonomy case study to demonstrate how the different frameworks would approach the deal in question. I also look at the failure modes demonstrated in the deal to better evaluate relevance of the frameworks to the intended deal rationale. Further I talk about how innovation fuels inorganic growth for companies in the technology domain. In doing so, I focus on the relevance of these frameworks to the technology domain and how the industry should approach and utilize these M&A frameworks. Based on the studies and the key concerns of the technology domain, I conclude on the possibility of McKinsey Framework being a truly comprehensive Framework that can be used as a basis for understanding the motivation for a M&A transaction. In summary, this paper will provide an overview of the M&A frameworks developed over past 6 merger waves, compare them within the scope of technology domain and evangelize on their applicability and relevance. / by Sridhar Narayanan. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, September, 2020 / Cataloged from the official version of thesis. / Includes bibliographical references (pages 99-100). / In the past 20 years, there have been no fewer than five major acquisition reform initiatives in the United States Air Force. Two of these initiatives, Open Systems Architecture and Middle Tier Acquisitions Rapid Prototyping, stand to change the way the Air Force acquires and engineers weapon systems due to their potential impact on Cost, Schedule, and Performance. Because of this impact, can analysis measure the effect of reform initiatives on acquisition programs and identify future combinations of initiatives to maximize benefit for the Air Force? This research analyzed the acquisition program outcomes before and after the implementation of a reform initiative utilizing the following variables: cost, schedule, performance, ease of use, and difficulty to implement. A tradespace analysis of the variables was then conducted to show how policymakers could theoretically make informed decisions on how best to implement, modify, or combine these initiatives. As the basis for research, quantitative data would be ideal for performing this analysis; however, the ability to gather this type of data before reform initiative implementation was not possible for this thesis. Due to this lack of data, qualitative information (survey techniques, and the documented purposes of the reform initiative), as well as model-based parametric analysis, were used. The research shows that, while it is possible to analyze a reform initiative utilizing this method, decision-makers should be cognizant that there are limitations to this type of predictive modeling; as such, the USAF should continue to thoroughly analyze initiatives before implementation, perform surveys through "policy gaming" when possible, ensure initiatives are not counter to each other and consider combining reform initiatives in the future. / by Daniel J. Visosky. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, February, 2021 / Cataloged from the official version of thesis. / Includes bibliographical references (pages 26-28). / Agile software development principles prioritize the delivery of value through working software. Earlier value creation is preferred to reduce the time to market and get sooner feedback from customers. A challenge in planning agile sprints to achieve value upfront is the tension that exists between the value, resources, size of each feature or story deliverable, and the dependencies among them. While the role of effort and resource constraints in value creation has been studied extensively, the role of dependencies has not been fully addressed in the agile context. In this thesis, we propose a framework to improve value delivery in agile software development by decoupling cyclic dependencies to achieve more robust multi-sprint plans in a scaled agile environment. We analyze this novel approach using an arbitrary test dataset to demonstrate how different decoupling methods yield different value trajectories. We also suggest an optimization method to maximize such value creation through sequencing by simultaneously considering timing, dependencies, and resource allocation. We perform a brute-force optimization approach on the test dataset to demonstrate how more rapid value creation can be achieved over multiple sprints. / by Nithin Thekkupadam Narayanan. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program

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 70-71). / The thesis presents the design and analysis process of a bio-inspired 3D printing body armor for neck support and protection. There are numerous examples of the structural skin or body armor among animals that generates both cranial protection and torso support. In this thesis, the mechanical behavior of the natural structure regarding the specific animal subject will be reviewed and studied using bio-inspired, flexible, design-for-manufacturing armor prototypes designed using computational 3D modeling to tackle a particular problem in real-life body protection. The design process will be demonstrated following the design thinking methodology with the emphasis on user empathy and experience design. Analysis of the prototype's flexibility and strength will be investigated to show how morphometry can enhance the architecture of material. And the accessibility will be researched under quantitative testing and qualitative interviews to the potential beneficiary. The thesis will also explore how the computer aid design can be improved based on bio-inspired analysis and potential mechanical testing. The long-term objective is to use bio-inspired design to develop an additive manufacturing technique for product design to accelerate the iteration process and increase product efficiency. / by Lei Xia. / S.M. in Engineering and Management

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, September, 2020 / Cataloged from the official version of thesis. / Includes bibliographical references (pages 42-43). / System dynamics allows managers and policy makers to analyze problems with non-linear feedback structures and thus counter-intuitive behavior. A main tool of system dynamics is to build a computational model of a system and analyze it to determine suitable policies to move the system to a desired goal. This work aims at using methods and algorithms from reinforcement learning to determine suitable policies for a system dynamics model. We introduce the techniques, methods and algorithms of reinforcement learning and apply them to a classical model from the system dynamics literature. / by Aditya Thomas. / S.M. in Engineering and Management / S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program