Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018 / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 140-151). / Oral delivery of large molecules is widely considered the "holy grail" of drug delivery, but attempts to achieve this within the past century have been met with a lack of success, confounded by low bioavailability. Novel mechanisms need to be assessed in order to deliver a clinically relevant amount of drug into systemic circulation, while protecting the drug from pH denaturation and the harsh enzymatic environment of the gut. To assess the field, this thesis evaluates startup companies and academic labs focusing their efforts on the oral delivery of biologics. The holistic, phased analysis of the field includes the following items: ** Value proposition assessment as applicable to Amgen's pipeline; ** Literature review into historical barriers; ** Technology landscape of the current space; ** Down-selection to highly valued technology prospects; ** Risk assessment and mitigation planning activities. / The approach outlined above led to the identification of two promising technologies (Tech A and Tech B) that use novel methods to deliver drug through the lining of the small intestine into systemic circulation. Both early stage technologies hold a significant amount of promise for Amgen if they enable both systemic and localized GI delivery successfully, but have multiple risks to address prior to use as a platform delivery option. Risks that have been prioritized for evaluation include: health concerns over long term damage and infection, low bioavailability, limited payload capabilities, and large final device size. In Silico modeling in COMSOL Multiphysics of the mechanism of action of Technology A and the resultant spread of drug product into the lining of the small intestine was completed as a preliminary test of the risk of low bioavailability. / Results from this model indicate that Technology A can be optimized via nozzle diameter and ejection threshold pressure to deliver liquid drug product into the desired locations within the small intestinal wall for optimal drug uptake into systemic circulation. If these technologies prove to be successful, the resultant product offering could prove highly disruptive in the industry and allow Amgen to revolutionize the manner in which patients interact with their medications. / by Kaitlyn Nealon. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 59-60). / Verizon distributes Customer Premises Equipment (CPE) such as set top boxes, broadband routers, and WiFi extenders to Fios customers via a variety of paths; for example: direct ship to customer (either for self-install or for later installation by a field technician), delivery via field technicians, or retail store pickup (primarily for self-install). Each method has its own benefits and shortcomings due to impacts on metrics such as inventory levels, shipping costs, on-time delivery, and system complexity. Although the majority of shipments are successfully activated in the customer's home, a non-trivial percentage results in unused returns or inventory shrinkage. These undesirable results represent a significant amount of wasted resources. This thesis is focused on identifying and realizing cost savings in the Fios supply chain through reduction in waste associated with unsuccessful shipments. / In order to effectively analyze the closed-loop supply chain, accurate and reliable process mapping is critical. Interviews with key stakeholders, together with order and shipment data analysis yielded a complete picture of the ecosystem's processes and infrastructure. Process mining techniques augmented this understanding, using event log data to identify and map equipment and information flows across the supply chain. All together this analysis is used to identify order cancellations as a key source of waste. To limit waste, it is necessary to conduct analysis both internal to Verizon's processes and externally, to determine if there are customer trends leading to order termination. Process mining was used for the internal analysis and, while it helped identify singular cases in which process abnormalities were associated with undesirable outcomes, its current form proved unsuited for root cause analysis. / Internal analysis did, however, illuminate opportunities for improvement in radio-frequency identification (RFID) usage and protocols across the supply chain. Current systems can result in poor visibility of equipment as it moves within some segments of the supply chain. The actual monetary impact is difficult to determine but likely to increase as the importance of RFID increases. External analysis is conducted through predictive modelling. Using a variety of data sources, a model with over 80% sensitivity and a low false positive rate is achieved. Operationalizing this model through real time incorporation with sales was explored but found to be overly complex. Instead, the random forest model yielded policy changes guided by the features with the highest importance. A pilot is currently in development to test the efficacy of suggested changes, as the model implies significant savings opportunity. / by Hans P. Kobor. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 59-60). / Increasing competition in the defense industry risks contract margin degradation and increases the need for new avenues to margin expansion. One such area of opportunity is take-away bids for under-performing competitor sole source contracts. Post financial crisis, the government has been more willing to entertain conversation with outside firms about existing contracts in the execution phase if the contracted firm is under performing budgetary and schedule terms. The contracted firm has the opportunity to defend its performance though, so in order to maximize the likelihood of successful take-away, the bid would ideally be submitted when the contracted firm is distracted and cannot put together as strong of a defense as would be typical. Corporate restructuring is an example of such a time; employees are distracted and leadership, communication, and approval chains are disrupted. Because the government contracting process is long and detailed, often taking on the order of one year, if restructuring at competitor firms could be predicted up to a year in advance, resources could be shifted ahead of time to align bid submittal with the public restructuring announcement and therefore increase the likelihood of take-away success. The subject of this thesis is the development of the necessary dataset and application of various machine learning methods to predict future restructuring. Literature review emphasizes understanding of current methods benefits and shortcomings in relation to forecasting, and proposed methods seeks to fill in gaps. Depending on the competitor, the resulting models predict future restructuring on blind historical test set data with an accuracy of 80-90%. While blind historical test set data are not necessarily indicative of future data, one of the firm's under assessment recently announced a future restructuring in the same quarter that the model predicted. / by Matt Casavant. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 65-67). / Ascent Aerospace is a global tooling and factory automation supplier for the Aerospace industry. Ascent's customers are typically introducing automation for the first time, or have already introduced automation to their production systems and are wary of its challenges. Choosing the appropriate technology is essential in ensuring successful implementation for both Ascent's customers and Ascent itself. Ascent has two different business units that produce equipment to automate the drilling and fastening of aerospace structures. These two units each come with vast product portfolios, and distinct approaches to address customer needs. This thesis focuses on an efficient method of evaluating how Ascent's current products align with customer's requirements, as well as identifying any technology gaps needing further exploration. / This thesis argues that Ascent's multiple business units are not currently equipped to advise their customers on investing in the appropriate technology for their production systems. To investigate such a vast solution space, a framework developed by the Systems Engineering Advancement Research Initiative (SEARI) at the Massachusetts Institute of Technology (MIT) is utilized called Multi-Attribute Tradespace Exploration (MATE). Using this framework, a software package called the Product Selection Tool, was developed to analyze how Ascent's product portfolio satisfies the customer's requirements for specific applications. The Product Selection Tool visualizes Ascent's 71 different product offerings on a single graph of utility versus cost per fastener. / The interface that displays the graph is dynamic, allowing Ascent's customers to adjust their requirements and preferences in real time, and visualize the sensitivity, or risk, of the recommended solution based on their specific requirements. This new approach allows Ascent to closely work with their customer in selecting a solution, identify areas of concern early on in the product selection process, and introduce cost-effective technology. This model can be applied to a variety of applications that have a vast solution space, reducing the complexity of understanding and communicating one's product line and/or capabilities. / by Zachary David Talus. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: S.M., Massachusetts Institute of Technology, Department of Chemical Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Cataloged from student-submitted PDF version of thesis. "June 2019." / Includes bibliographical references (pages 87-89). / Flexible, modular, continuous manufacturing small-scale plants (MCSPs) for small-molecule drugs have been recognized as potential safe and economical solutions for pharmaceutical manufacturing. However, among the variety of equipment technologies required for an MCSP platform, there are only a few technologies that have publicly available methodologies for equipment selection. In this study, a new method and tool for computer-assisted equipment selection was developed, which use key engineering correlations and design criteria to match off-the-shelf equipment with the synthesis processes of interest. Furthermore, the tool allows simultaneous equipment selection for multiple synthesis processes to allow the identification of the most flexible MCSP assets. The long-term goal of this tool is to encompass the entire span of technologies that could be used in an MCSP skid and to serve as a communal storage location for vendor-available equipment information to facilitate collaboration and design of a mainstream continuous manufacturing (CM) system. This methodology was applied to equipment selection for the continuous manufacturing of an actual Amgen small-molecule drug substance (API) as a case study. The results from this study showed that the new tool can improve the speed at which equipment is selected and can aid the process developer in decision-making for choosing the most suitable CM asset. / by Kevin Peng. / S.M. / M.B.A. / S.M. Massachusetts Institute of Technology, Department of Chemical Engineering / M.B.A. Massachusetts Institute of Technology, Sloan School of Management

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 115-117). / In the pharmaceutical industry, increasing product complexity, shifts towards specialty medicine and growth in emerging markets have resulted in increased forecast variation and manufacturing complexity for new products. In the past six years, AstraZeneca has outperformed its peers in research and development productivity, increasing the number and speed of product launches. The resulting demand variability and shifting operational environment have led to financial and non-financial impacts, such as poor inventory performance and strained supplier relationships. The objective of this research is to identify processes and procedures that amplify the impact of demand variability and the areas in the end-to-end operation that are significantly impacted. The secondary objective is to identify process improvements in the existing system and develop strategies to mitigate the risk of demand variability. / This thesis presents an analysis of the impact of demand variability on the external manufacturing and supply chain operations for new products. A case study approach is used to assess its impact on the forecast processes, manufacturing systems and supplier relationships. A simulation tool was developed as a method to measure financial impact based on inventory performance. The simulation was expanded for use as a decision assist tool to evaluate test cases developed from the current state analysis. The research illustrates that the end-to-end manufacturing and supply chain operation is experiencing significant bullwhip effects for new products. The primary sources of financial impacts are the policy stock requirements tied to monthly demand and segmentation of the supply chain causing different forecasts to be used for certain stages. Non-financial impacts include loss of trust with suppliers, manually managed complexity and limited communication resulting in the bullwhip effect. / The short-term and long-term recommendations focus on increased operational transparency and scenario-based forecast planning to mitigate the impact of demand variability on the system. Pilot programs for statistical process control implementation in drug substance manufacturing and development of a future state commercial partnership model were defined as follow-up work to this research / by Meghan Savage Wright. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2019 / Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 86-99). / Breaking the trade-off between speed and productivity is a key milestone across industries. In particular, in the biopharmaceutical industry this trade-off is exacerbated by a highly regulated environment, which hinders continuous improvement and fixes future manufacturing costs. Given the complexity of living organisms and the improvement in quality of life offered by the product - which demand agile development - the industry has traditionally taken phenomenological approaches to process development, generally sacrificing costs. Nonetheless, technological developments and lower entry barriers make the biopharmaceutical industry far more competitive than in its origins, demanding efficient and reliable processes. Developing efficient manufacturing processes for new products while being agile to market is a key differentiating capability of Amgen's process development organization. / In collaboration with the process development team at Amgen, a computational framework for in-silico upstream bioprocess development has been developed, allowing for faster, more robust, and more optimal process development. Specifically, a mechanistic model of a bioreactor has been designed, implemented, and applied to an Amgen product. The project was divided into three major components: The first was a survey of internal Amgen capabilities and the state of the art in external industrial and academic models to identify the algorithms and design the signal flow required to support the range of expected process engineering applications. The second consisted of implementing a modular, extensible software platform with the architecture and interfaces dictated by the first component. The third part consisted of applying the software to an actual product development problem capturing the primary process variables. / A constraint-based model of a metabolic network consisting of 35 reactions of the main carbon-nitrogen metabolism relevant in energy and redox balance was adapted from literature (Nolan & Lee, 2011). The metabolic network was coupled with glucose, glutamine and asparagine kinetics with temperature, dissolved oxygen, pH and osmolarity dependence. Stress induced by temperature shifts was modeled as a first-order step response coupled to a non-growth associated ATP of maintenance. The cellular model was coupled with a well-mixed bioreactor model consisting of mass balance equations. We solved the model using dynamic Flux Balance Analysis (dFBA). We first calibrated the model with experimental process characterization data for a product in development. We used a Non-dominated Sorted Genetic Algorithm (NSGA-II) to solve the calibration problem, minimizing the error in metabolite concentrations to yield estimates of 13 strain-specific parameters. / We then assessed the calibrated model's predictions of biomass growth and metabolite concentrations against a second experiment run with different process settings. Finally, I developed a graphical user interface for subject-matter-experts to simulate experiments and test hypotheses using the model. We applied the tool to three process-relevant case studies, and analyzed the in-silico results. The calibrated model can predict biomass and titer from process settings, potentially reducing experimental time from 20 days to 30 seconds, in addition to reducing the experimental cost. / by Martín Cárcamo Behrens. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Biological Engineering

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 / Thesis: S.M., Massachusetts Institute of Technology, Department of Chemical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 101-106). / The objective of this project is to improve the yield of the fed-batch manufacturing process for the production of Flublok influenza vaccine, which was approved by the FDA in 2018. In short, Spodoptera frugiperda (SF+) insect cells are grown to a specific target cell density and then infected with baculovirus containing the gene of interest (GOI). For this particular process, the recombinant hemagglutinin (rHA) is extracted from the cell and used to produce the influenza vaccine. Protein Sciences developed a fed-batch process which improved on the traditional batch process by feeding supplementary nutrients to boost cell growth. The Fed-Batch process doubled the target cell density at the time of infection which resulted in a two-fold increase in the final yield of rHA and a 30% reduction in cost of goods. This Fed-Batch process can be further optimized to increase rHA yield and reduce the cost of goods. It is important to note that simply increasing cell biomass is not enough; the cells must also be able to produce rHA at a similar specific productivity in order to increase the yield. Exploratory process improvement experiments were performed on the ambr250 ® multi-parallel bioreactor system, with the goal of identifying the growth conditions for maximizing SF+ cell density. The conditions yielding the best results from these experiments were replicated in 3L glass bioreactors. Using data from these experiments, an optimized Fed-batch process can be developed. In addition, a statistical model was developed to relate key process parameters to SF+ cell density. This model can be used to quantitively ascertain how cell density is impacted by changing process parameters. / by Randy Stein. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Chemical Engineering

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 63-64). / As part of its effort to introduce new technology and to improve the manufacturing system for the 777X production line, Boeing has made a significant capital investment in the Composite Wing Center (CWC). The new facility uses highly automated equipment and processes to support the production of components for the 777X. Since many of the automated machines are unique to the Boeing production system, opportunities exist to model and simulate specific machine systems to ensure that work is being performed as efficiently as possible. To date, most of the factory's process equipment has been installed and is operational, providing a production rate of X parts per month. To meet demand, operations will be gradually ramping up to meet the 777X production targets. The ramp-up to the target production rates will be done by a combination of additional equipment installation and process improvement projects. This research study involves the use of Discrete event simulation to provide insight into current cell capability and to identify process bottlenecks. Moreover, the simulation model incorporates process variability, the sequence of process steps within the cell, equipment downtime data, and resource constraints. The resulting simulation model was verified by comparing it to actual system performance. The model analysis and improvement recommendations show significant improvement over the current process in terms of cycle time reduction and production rates increase. In the future, the developed model will be updated regularly and will be used as a tool to monitor system throughput and to evaluate the impact of process changes to the overall system. In addition, the developed framework will be used to help other plants in a similar situation. / by Fatima Diallo. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 / "May 2020." Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 78-80). / Industrial organizations have increasingly invested in IoT technologies to monitor, control, identify faults in, and optimize operations. With increasing competition, a push to lower manufacturing costs, and pressure to create 'wow' moments for passengers, and in alignment with enterprise vertical integration strategies, airplane interiors manufacturers like Boeing's Interiors Responsibility Center (IRC) are looking to leverage IoT technology to transform not only what they manufacture but also how. This study seeks to understand the potential for analytics in increasing production manufacturing capacity in Boeing's IRC. Our analysis is driven from personnel interviews, observational time studies, review of historical machine data, and value stream mapping. / We establish that replacing human-dictated job allocations in the CNC router workstation with an analytical allocation tool built using mixed integer programming techniques can increase manufacturing production capacity and reduce schedule losses, thereby increasing Total Effective Equipment Performance (TEEP). Moreover, data-based job allocations offer a 56-fold decrease in workload variance among machines, thereby establishing a fairer work allocation scheme associated with increased job satisfaction among 72% of employees. A discrete event simulation of operations in the IRC's CNC router workstation was built and tested across ten test days for further analysis of efficiencies gained from the job allocation tool in a realistic context. The simulation, which considers equipment and personnel requirements, supporting activities such as material handling, and the factory's physical layout, revealed that as-is operations in the CNC router workstation are unable to meet demand. / Moreover, a comparison of workstation operations with and without the data-based job allocations tool via numerical experiments shows that the tool's implementation could decrease overtime hours by 59%. Additional operational inefficiencies, namely long transportation times associated with material handling tasks, were uncovered by resource state analyses of simulated operations. What-if analyses simulating potential interventions led to the identification of alternative resource staffing and material storage schemes associated with 65% to 100% reduction in overtime hours compared to the current baseline, when implemented in conjunction with the data-based job allocations tool. Finally, in this study we offer a methodology for data-based strategic decision-making, where linear programming methods are leveraged to account for ordered strategic business priorities. / by Noa Ghersin. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering