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, Sloan School of Management, Operations Research Center, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (page 64). / A critical aspect of biologics manufacturing is creating a safe, reliable and consistent manufacturing process. The manufacturing process design includes process characterization (PC) experiments to demonstrate process robustness and provide data necessary for planning, risk mitigation, development of the control strategy, and successful execution of process validation. Performing PC experiments is resource intensive, both human and capital, so leveraging prior knowledge from previous experiments is essential. Until now, using data from past experiments data relied on a centralized static document called Prior Knowledge Assessment (PKA). The PKA aggregates the results of many statistical models that were created during past PC studies. Using the PKA provides insight, but leaves a lot of room for subjective decision making around questions, such as: How should products be grouped together? and What operating parameters are more important? The PKA also lacks uncertainty quantification for statistical significance. In this thesis, we aggregated data from past PC experiments across multiple molecules, and developed a machine learning framework to holistically analyze cross-product data from process characterization DOE studies. The model developed through this project provides interpretable predictions of sensitivity of Performance Indicators to Process Parameters variation. The model enables, for the first time, to assess and quantify the impact of parameters on indicators, even if they were not tested originally for a specific molecule. A novel user interface was created in order to bring the framework to life and create a "one-stop shop" for a scientist to interact with the model. This work improves process characterization decision quality. Potential benefits of this approach would be to increase speed and agility in process development and reduce number of future experiments. / by Or Dan. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

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 Electrical Engineering and Computer Science, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 87-89). / In the biotechnology industry, commercial manufacturing of biologic drugs occurs in large-scale production bioreactors (15,000L), but process development occurs in lab-scale production bioreactors (2-3L). Cell culture processes are complicated and the scale-up from bench-scale to commercial-scale can be unpredictable. This study develops an algorithmic approach to better predict the performance of a production bioreactor at commercial scale. A hybrid modeling approach is explored using historical process data and calculated equipment engineering features that characterize the bioreactors at each scale. The study reveals that current process characterization regression models cannot predict commercial-scale performance better than the mean, and that machine learning approaches can improve this performance. Engineering features are found to have a relatively small impact that varies by response variable, but paradoxically are often retained in feature selection of top-performing models. Several new hypotheses arise from these findings, revealing the need for further work with an expanded multi-process multi-scale data set. The researchers propose that by training the model on such a robust data set, it will be possible to test these new hypotheses and unlock significant potential to reduce risk, costs, time, and resources required to develop, commercialize, and manufacture new biological drugs. / by Zoë Wolszon . / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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 Electrical Engineering and Computer Science, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 69-72). / Commercial technology transfer for biopharmaceuticals is the process of transferring process and product knowledge between process development and manufacturing organizations to achieve product realization. This process often occurs before phase 3 of clinical trials, where speed and agility are critical for preventing delays in clinical programs and ensuring commercial site readiness ahead of regulatory approval. As the market is evolving with new modalities and subsequent operational challenges, there is a heightened need to optimize the technology transfer process to sustain growth of products entering an organization's pipeline. This graduate research project seeks to understand the business process workflow of commercial tech transfer and characterize its dynamics using discrete event simulation. Through this quantitative technique of business process modeling, knowledge regarding process bottlenecks and system constraints were revealed, leading to the identification of operational efficiencies which suggest a potential 19.5% reduction in lead times and 31.3% increase in organizational capacity. Furthermore, this work provides a platform for predicting program timelines and resource needs based on preliminary transfer requirements. These predictions can be updated in a Bayesian fashion for real-time project scheduling and capacity planning. / by Hillary Doucette. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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 Civil and Environmental Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 92-93). / Amgen is one of the world's leading independent biotechnology companies and competes globally to advance important medicines in a highly competitive marketplace. Biologics manufacturers such as Amgen have traditionally invested in costly, large-scale stainless steel infrastructure to support the production of biologic medication. However, more recently, changes in the economics, such as the need to deploy less-capital intensive biomanufacturing plants faster, and advances in the technology, such as process-intensification (i.e., getting more protein from each cell), have created both incentives and pressures for smaller-scale, single-use, and modular production technologies. These incentives include greater flexibility, shorter timelines for construction / rapid deployment of new facilities, and reduced costs as well as physical and environmental footprint. / To prepare for this changing business environment, Amgen must develop a manufacturing strategy that can enable the production of high quality products with significant reduction in timelines, cost, and reduced impact. To do so, Amgen is investigating a handful of these new production technologies, known as next generation manufacturing technologies, and attempting to understand their applicability in their future manufacturing model. There is a need for a transparent and standard methodology for evaluating and deploying new technologies in the manufacturing network. This study aims to address this issue and enable speed, rigor, and efficiency of decisionmaking through the use of a structured framework for selection and deployment of next generation technologies. Through literature review and engagement with Amgen experts, this study defines a next generation manufacturing technology evaluation framework. / This framework involves a hybrid, multi-attribute set of metrics that are broadly categorized into economic, environmental, and operational assessment areas. The framework is then applied to assess the economic, operational, and environmental implications of deploying single use technologies in drug substance manufacturing as a test of concept. An assessment along the three areas helps to identify that single use technologies, namely single use bags due to their cost and environmental footprint, may not always be the optimum substitute for all existing process technology. Instead, a hybrid approach, mixing new single use technology with existing stainless steel infrastructure, may help to reduce variable cost and carbon footprint of the process. / When the framework and this proposed hybrid approach was at an Amgen site, a potential savings of up to $ 1 M per year was identified as well as the elimination of up to thousands of liters in clean water losses, and up to 400x reduction in the carbon footprint of the process. Lastly, the assessment framework is applied as a management tool in the assessment of next generation drug product filling technology to demonstrate how the framework can be used to enable rapid decision-making related to future manufacturing scenarios. / by Janelle Nicole Heslop. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Civil and Environmental Engineering

Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2019 / Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (page 53). / While automated mobile inventory systems have greatly increased productivity, it has also created a new set of operational challenges. Floor health events, such as fallen product, spills, disabled robots, and floor access can degrade overall floor performance by obstructing access to product, forcing robots to re-route to less efficient paths, exacerbating congestion, increasing idle time, and potentially reducing throughput. Floor health issues are interdependent and have cascading effects, making their impacts difficult to track, visualize, and address. Reactive support and reliance on training and adoption of best practices is not scalable. As the network continues to grow, there is a need to improve real-time visibility and preventative measures into floor conditions. This project consisted of five main phases: research, hypothesis, testing, evaluation, and implementation. / The research phase was dedicated to developing an understanding of the current processes and problem statement. Then a testable hypothesis was constructed based on observations and data exploration. The hypothesis was tested via simulations and statistical analysis. The evaluation phase included analyzing the implications and use-cases of the results. The last phase of the project included developing and implementing selected applications. The model development phase of the project included simulation experiments where the dependent variable collected was the percentage change in average throughput rate and a multitude of potential explanatory features were tracked. Analysis of this data revealed that some of the best predictors of degradation of throughput rate were the types of floor cells being blocked. / There is wide range of impactful applications of these findings, including diagnostic checks to help root cause issues, automated notifications that highlight deteriorating floor conditions, automated user path planning, actionable floor metrics, and prioritization of work. Automated notifications to proactively identify deteriorating floor conditions, real-time prioritization of tasks, and a diagnostic tool were the implementations focused on during this project. / by Emily Stinson. / S.M. / M.B.A. / S.M. Massachusetts Institute of Technology, Department of Civil and Environmental Engineering / M.B.A. Massachusetts Institute of Technology, Sloan School of Management

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 Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 96-102). / Within the biopharmaceutical industry, material sciences is a rapidly growing field to continue to ensure reliable production and delivery of medicines. Consequently, there is an on-going need to evaluate and assess new materials, driven by novel process technologies and new modalities. Finding a solution to technically assess the impact of raw material attributes on the manufacturing process represents a significant opportunity to ensure supply. This study seeks to develop a novel predictive framework to assess the impact of raw material variability on the performance of commercial biologic manufacturing processes. Through machine learning techniques, the impact of two strategic raw materials is evaluated by modeling and predicting the outcomes of critical process performance variables and product quality attributes. As part of this research, we aimed to equip Amgen Inc. with a novel learning tool delivering the potential to uncover a deeper level of material variability understanding which: (1) ensures reliable supply through consistent performance, (2) provides insights to material attributes, and (3) delivers the capability to solve material-related investigations more efficiently. Models trained via machine learning showed 89 % average accuracy on predictions for new data. In addition to the demonstrated predictive power, the models developed were highly interpretable and illustrated correlations with several material attributes. Henceforth, the framework developed is the starting point of a novel methodology towards input material variability understanding. The predictive framework was implemented as a web-tool and is currently being piloted at Amgen Inc. The modular design of the predictive models and the web-tool enable the application to other production processes and associated raw materials, and could be generalized across the industry. / by Maria Emilia Lopez Marino. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Civil and Environmental 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 Civil and Environmental Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Cataloged from PDF version of thesis. / Includes bibliographical references (page 80). / The Boeing Company is the world's largest aerospace company and is constantly evaluating improvement opportunities to the production system. It is of ongoing interest to the company to have to tools to assess new manufacturing sites. Among the required tasks for such an effort, engineers must identify the processes and capabilities that will be needed. A critical element of this study is the system of internal logistics processes that could manage the flow of parts and material throughout a site. Planning the capacity of these processes is difficult when many of the parameters are uncertain and yet to be determined. This thesis proposes a method for estimating capacity requirements of internal logistics processes by employing the concepts of queuing theory and Little's Law. Using this methodology, a process model was developed and validated by discrete event simulation to provide process planners with an understanding of the relationship and importance of numerous parameters. This understanding allows planners and management to assess the capacity requirements of the processes in terms of projected costs and performance. Values of wait times predicted by the proposed model were in strong agreement with values observed from simulation (R-squared of 96.4%; MAPE of 14.9%) suggesting that the proposed methodology represents an easy-to-use and accurate representation of process parameters. In order to improve the applicability of capacity recommendations for Boeing, further refinement is needed of underlying process parameters as well as cost modeling of threshold parameters (k and pn_max). / by Dan David Cryan, III. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Civil and Environmental 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 Civil and Environmental 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 53-55). / In supply chain management, it is commonly held that reducing lead times and minimum order quantities (MOQs) from suppliers can drive down the customer's inventory levels substantially. Customers providing a consumption forecast along with a commitment to a supplier to cover some portion of raw material, work-in-process, and finished goods in exchange for reduced lead times and lower MOQs can support that goal; however, there does not exist a general method for identifying and optimizing the terms of these agreements. Existing literature describes techniques that involve vendor-managed inventory and other lead time reduction strategies, but none exists where the customer manages the ordering and replenishment policies from a vendor stock. In this thesis, we investigate a method for a company to reduce lead times and inventory level while maintaining or improving their customer service level. To do so, we introduce a new process for the business where a customer identifies the optimal subset of parts with their corresponding lead time and stocking policy trade-offs to drive inventory reductions relative to the existing state. We describe the benefits for both supplier and customer and specifically focus on the investigation of the opportunity for the customer and the appropriate segmentation of suppliers and parts for consideration in a pilot leading to full implantation. We expect this new approach to substantially reduce the inventory at the customer while improving the suppliers' ability to optimize their own manufacturing planning and setup schedules. / by Randall Markham. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Civil and Environmental 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 Civil and Environmental 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 57-60). / The development of a biopharmaceutical manufacturing process involves an assessment of all possible sources of variation throughout each of the unit operations in the drive toward six sigma manufacturing. The primary goal of this project is to develop a novel way to assess the variation in raw materials attributes throughout the life-cycle of the material and gain insights about the correlation between material variation to process performance and product quality. This thesis focuses on understanding the impact raw materials have on unit operations within biopharmaceutical manufacturing processes through machine learning techniques. To evaluate the impact of raw material attributes on process performance and exclude the variations explained by process operating parameters, a modeling framework is developed and tested. The framework contains three steps: (1) fitting models with only process operating data, (2) fitting models with process operating data and batch number information, (3) fitting models with process operating and raw material attributes data. By comparing the performance measurements from 3 different models, insights of correlations between raw materials and process outcomes could be obtained. / by Zihuai Liu. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Civil and Environmental 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 Civil and Environmental 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 57-58). / To meet increasing consumer expectations around delivery times, ecommerce retailers must take orders from 'click' to 'ship' as soon as possible. For retailers with tens of thousands of stock keeping units (SKUs), item picking is often the slowest step in the distribution center (DC) fulfillment process due to time taken for pickers to move between item locations. Pick paths are dictated by item slotting, the process of assigning SKUs to locations within the pick area, and therefore slotting improvements increase pick rate. This decreases the time taken to pick orders allowing more orders to be fulfilled in a given time period and increasing fulfillment capacity. This thesis presents a method for increasing pick efficiency through improved slotting strategy. This is achieved through placement of high velocity SKUs close to the outbound path resulting in a reduction in picker distance travelled, and on mid-level shelves where they are more ergonomical to pick. The impact of slotting strategies was compared through a simulation model. Simulations with historical data indicated a potential 5.2-10.8% increase in fulfilled units over a given time period. / by Lorcan Andrew Murphy. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Civil and Environmental Engineering