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

Ineffective marketing strategies can negatively impact business competitive advantage. Small business owners who struggle to maintain a competitive advantage are at high risk of failure. Grounded in the technology acceptance model, the purpose of this multiple case study was to explore social media marketing strategies small business leaders in the wine industry use to promote brand awareness and maximize competitive advantage. The population comprised 5 small business leaders employed with 4 wineries in the wine industry in Michigan, who effectively used social media marketing strategies to promote brand awareness and maximize competitive advantage. Data were collected from semistructured interviews, company documents, and company social media platforms. Thematic analysis was used to analyze the data. Three themes emerged: customer engagement strategy, social media platform strategy, and targeted market strategy. The implications for positive social change include the potential for small business leaders in the wine industry to create jobs and support the economic development of the regional communities.

Hear how school leaders in two counties in Tennessee studied, discussed, and committed to changes in their schools with remarkable results. Learn how these leaders designed their study groups around converging topics and gathered evidence about their work. Determine how this simple but effective collaborative model can be implemented in your settings, thus building amazing leader relationships across schools.

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Multi-Tiered System of Supports (MTSS) is a model that can be implemented in school buildings with the support of school district leaders. However, the voices of district leaders involved in MTSS implementation are limited in the research. This study sought to investigate what district leaders perceived as impacting factors towards MTSS implementation and draw conclusions about impacting factors from their opinions. Ten district leaders in a mountain west state of the United States were interviewed via Zoom and had their transcripts analyzed for impacting factors using a form of thematic analysis. All participants were involved with MTSS at their district in some form. This study identified four themes from the data: Personnel Involvement, Pervasive Influences, Foundations and Framework, and Supports Beyond the Site Level. Within each theme, multiple constructs came across which may be beneficial to those looking to implement MTSS or would like to better sustain MTSS implementation in their schools. Findings of this research study can directly impact districts and schools in their planning stages of MTSS implementation that could lead to longer and stronger sustainment of MTSS in their schools. Some examples of ideas drawn from the data include how school systems may want to consider the personnel they currently have access to or could potentially gain access to; they may want to consider emphasizing data and dedicate time to work on MTSS implementation; they may want to consider creating a strong structural foundation so that future implementation will be better sustained, such as structuring practices in a way that they can continue despite changes in personnel; they may want to consider which outside supports they have available to them to assist in supporting implementation.

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

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 129-130). / Canam Group ("Canam") is a manufacturer of steel components and building products used in the construction industry. The company uses a distributed network of manufacturing centers throughout North America to build and ship joist and deck product to its customers. Each manufacturing center utilizes a similar set of equipment assets in the production process. Equipment assets are not connected to a data collection system capable of monitoring their performance and health. As a result, comparing the performance of similar equipment across sites is a challenge for the organization. The motivation for this thesis is to determine how Internet of Things (IIoT) technologies can be applied to an industrial business like Canam to improve asset monitoring capabilities. An experimental approach is used to demonstrate how IIoT frameworks discussed in literature can be employed in practice. / In the first experiment, a network connectivity audit is performed to answer a set of practical questions about data communication within an industrial machine network. In the second experiment, a commercial tool is deployed at a specific equipment asset and integrated into the production workflow to collect data about the performance of the equipment. Downtime data collected from the IIoT tool deployed in the experimentation phase is compared with data collected using an existing manual data collection process. The data collected from the IIoT device revealed a systematic under-reporting of downtime in the manual process. Machine availability was shown to be 46% as compared to 90% recorded in the manual process. A model is presented to demonstrate that improving availability of critical equipment could lead to a 6% increase in plant throughput. / The thesis concludes by combining the findings of the experimental results and literature review to develop a framework from which the business can establish an organizational vision for IIoT, an implementation plan, a project scoping methodology and vendor selection criteria.. / by David Woodruff. / 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