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 51-52). / The robotics industry continues to grow rapidly. More industries are moving towards automation and are looking for the robotics industry to support the industry 4.0 movement. Due to a push by consumers, robotics producers are getting pressured by customers to deliver higher quality products faster. Motivated by Cost of Quality and Design of Experiments methods, the author breaks down the production systems test of robot manufacturing to identify areas for the focus of experimentation to improve quality and resource utilization. Considering connections between First Pass Yield and Field Failure Rates, the focus on quality improvement demonstrates the strong ties from the robot manufacturers to the final end user customers. By analyzing the robotic production and test systems, the author identifies three areas for the focus of experiments: 1) Test effectivity, 2) Component failure, 3) Robot system and test cell matching. Within each of these areas further analysis then identifies the experimental topics that can be developed through modified Design of Experiments steps to improve quality and remove the waste from failures and production system issues. / by Chelsea Zarnowski. / 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 65-66). / The principle driver of maintenance costs for commercial jet engines is the replacement of components that, upon inspection, are determined to be damaged beyond their repairable limits. In order to better predict the lifetime cost of maintaining engines through its flight hour agreement program, Pratt & Whitney aims to predict the probability of needing to replace these parts using information about how an engine has been used. Using historical repair records, we study a suite of statistical models and evaluate their performance in predicting part replacement rates. Despite a preference for interpretable models, we conclude that a random forest approach provides drastically more accurate predictions. We also consider the wider business implications of improved part replacement predictions, particularly as they pertain to forecasting material requirements and reducing volatility upstream in the supply chain. / by Eamonn Samuel Shirey. / 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, 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 61-64). / The Pacific Gas and Electric Company (PG&E) operates and maintains 48,000 miles of natural gas pipeline, serving over 4.3 million customer accounts. Along with water, electric power, and transportation services, these lifelines serve critical functions throughout multiple communities. Considering PG&E provides services in both densely populated and seismically active areas, the organization has invested extensively in modeling technology to help estimate resource needs and develop resiliency plans in the event of an earthquake. This thesis aimed to develop a damage prediction model to improve emergency response time and restoration efficiency. The machine-learning based model built upon currently used predictive algorithms, while adding features necessary to account for distribution branch lines and above-ground meter sets. Research and analysis showed factors beyond ground-motion prediction equations could be used to estimate pipeline damage and were consequently included. / Furthermore, the model incorporated real-time data acquired throughout repair and restoration efforts in order to improve the predictive performance. Historical incidents were examined in the data aggregation phase in order to develop the training set. For this paper, damage was defined as the number of leaks predicted in a given plat, as defined by PG&E's mapping services. Leaks were categorized in three separate bins, ranging from 0 leaks, 1 to 5 leaks, and 6 or greater leaks. Multiple classification algorithms were chosen and evaluated against a custom scoring metric designed to discriminate and penalize false negatives. The best results were achieved using a series of five logistic regression algorithms, executed at 2, 4, 8, 12 and 24 hours following event occurrence. Results were designed to accompany currently used seismic hazard reports in a ranked table, displaying areas with the highest to lowest probability of experiencing damage. / An additional web application was designed to query specific plats for prediction results. / by Steven B. Link. / 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: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 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 (pages 122-123). / Boeing is the prime contractor for building the National Aeronautics and Space Administration (NASA) Space Launch System (SLS) core stage for upcoming exploration missions beyond low earth orbit. Due to the rigorous demands of safety on crew-rated spacecraft, the entire vehicle undergoes captive hot-fire testing before being delivered to NASA for actual flight operations. The hot-fire test is controlled by a suite of computers used to control the rocket segment and critical infrastructure interactions during the test. The complexity of the software and hardware used to control the test makes it difficult for traditional safety approaches to identify potentially unsafe system interactions by focusing only on component failures rather than overall system interactions. Traditional chain-of-failure safety analyses and reviews take significant resources and time to conduct while leaving possible gaps. This thesis discusses a method for analyzing safety of rocket test controllers by characterizing key indicators and developing a systems-based approach for hazard analysis using Systems-Theoretic Process Analysis (STPA). A resulting case study is applied for examination of a portion of the rocket testing controller system for comparison to traditional chain-of-failure events analyses. Appling STPA in the case-study resulted in 83% of the total work time needed to complete a comparable "ascent phase" analysis using FMEA. The STPA results are the same or meet a similar intent to those resolved in the FMEA with not gaps between the two methods. The recommended mitigation and constraints resulting from STPA are arguably more intuitive than those of the FMEA. / by Jeremy Paquin. / S.M. / M.B.A. / S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics / M.B.A. Massachusetts Institute of Technology, Sloan School of Management

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical 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 (pages 73-74). / Currently, manual visual inspection is the gold standard for the required visual inspection of particulate matter in parenteral medicines. Automated visual inspection machines offer an opportunity for Amgen to improve efficiency, rate and consistency, while reducing its equipment footprint. However, the implementation of automated visual inspection poses challenges that need to be resolved. This thesis identified and developed solutions to three execution pain points: (1) low detection rates of dense particles in products; (2) misuse of automated inspection machines for product impact testing; and (3) ambiguous understanding of cost drivers when selecting an inspection method. The pain points mentioned above were addressed separately. First, experiments with modified plunger surfaces were conducted to determine their effectiveness at agitating dense particles into solution where the particles could then be easily detected. / Second, embedded sensors were identified as the sensor of choice to measure the mechanical stress history of products passing through an automated visual inspection machine. Experiments were designed to test the effectiveness of accelerometers to replace the limited range of gyroscopes' rotational velocity measurements. Third, a cost benefit analysis model was created that used discounted cash flows to calculate the net present cost of selecting automated visual inspection or manual visual inspection. The results of these three work streams were promising. First, the experiments with modified plunger surfaces showed up to a 97% success rate of agitating particles into solution compared to an 1% success rate for the original plunger design. Second, experiments on accelerometers in embedded sensors showed that the accelerometers could measure centripetal acceleration that related to rotational velocity. / A linear regression model was developed to relate accelerometer readings to rotational velocity within an accuracy of 50 RPM. Lastly, the cost benefit analysis model confirmed expected drivers regarding the favorability of different inspection methods. The model also showed that automated visual inspection is the cheaper method of inspection, even with conservative estimates of cost of capital and false eject rates. A follow-up effort is necessary to achieve a more streamlined implementation of automated visual inspection machines throughout Amgen's manufacturing network. / by Prosper M. Nyovanie. / S.M. / M.B.A. / S.M. Massachusetts Institute of Technology, Department of Mechanical 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, 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 65-66). / Johnson & Johnson Vision (JJV), manufacturer of the ACUVUE® Brand Contact Lenses, is committed to launching new contact lens products every year to maintain competitive edge and long-term relevancy. However, manufacturing lines currently operate at high utilization rates to satisfy steadily growing demand, limiting opportunity to beta test new products or validate manufacturing lines. Beta testing provides feedback on product design and manufacturability while validation qualifies a line to make a particular product at commercial scale - contributing to the more than 5 billion contact lenses produced by JJV yearly. To build manufacturing capacity and introduce flexibility into the system, JJV built the Flexible Manufacturing Platform (FMP). FMP is a modular manufacturing line capable of producing any contact lens in the JJV portfolio. This thesis explores how to strategically leverage FMP to enable quicker transitions from pilot-line production to commercial-scale production. / A case study was performed on the FMP heat seal manufacturing process step, providing insight into both the technical capability and organizational processes of FMP. The heat seal was chosen due to its critical importance in maintaining product quality and patient safety. Prior to the start of this project, the heat seal process step lacked consistency and reliability. Statistical process control techniques were employed to generate a heat seal capability model that measured the effect of changing the contact time, contact temperature, and contact pressure. This revealed contact time and contact temperature to have the most influence on heat seal integrity. The capability model ultimately improved decision quality and reduced product failures by 80%. Successful execution of the case study also required observation of upstream and downstream process steps to the heat seal, yielding a thorough understanding of the entire FMP line. / This FMP current state analysis shows the remaining work needed to efficiently scale between pilot-line production and commercial-scale production. As such, there is a need for continuous knowledge transfer between the R&D and Operations teams as they develop new governance processes to merge into a single domain. In doing so, FMP can become an efficient structure to continuously launch new products. / by Taylor K. Robinson. / 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 (page 72). / The Nissan Smyrna automotive assembly plant is a mixed-model production facility which currently produces six different vehicle models. This mixed-model assembly strategy enables the production level adjustment of different vehicles to match changing market demand, but it necessitates a trained workforce who are familiar with the different parts and processes required for each vehicle. Currently, the mixed-model production process is not batched; assembly line technicians might switch between assembling different vehicles several times every hour. When a switch or 'transition' occurs between different models, variations in the defect rate could occur as technicians must familiarize themselves with a different set of parts and processes. This thesis identifies this confusion as the consequence of 'transition' complexity, which results not only from variety but also familiarity; how quickly can a new situation be recognized, and how quickly can associates remember what to do and recover the skills needed to succeed. Recommendations follow to mitigate the impact of transition complexity on associate performance, thereby improving vehicle production quality. Transition complexity is an important factor in determining the performance of the assembly system (with respect to defect rates) and could supplement existing models of complexity measurement in assembly systems. Several mitigation measures at the assembly plant level are recommended to limit the impact of transition complexity on system performance. These measures include improvements to the offline kitting system to reduce errors such as reconfiguring the physical layout and implementing a visual error detection system. Additionally, we recommend altering the production scheduling system to ensure low volume models are produced at more regular intervals and with consistently low sequence gaps. / by Robert Addy. / 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 Chemical 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). / A critical early step in the development of a new biopharmaceutical is the selection of the master cell bank. Per FDA requirements, the same master cell bank must be used for all toxicity and clinical trials, as well as all production of the drug should it be commercialized. Developing a master cell bank is a time and labor-intensive process where thousands of clones are screened through a series of experiments. The Berkeley Lights Beacon® platform can be used as a high-throughput screening tool in cell line development and has been shown to produce clonally-derived cell lines, suitable for the development of a master cell bank. In a typical use case, a Berkeley Lights chip is loaded with 1750 cells, data is collected related to cell growth and on-chip assays, and the top 50-100 are selected for further analysis. The methodology for selecting the top clones, however, is not standardized and individual users may select different top clones based on how they weigh the growth and assay data. As a relatively new tool, there is little literature outlining how to best use data collected on Berkeley Lights to select the "best" clones for further screening. In this project, we use Amgen's database of Berkeley Lights experiments to determine which parameters are most predictive of performance in future fed-batch experiments. Data from 9 chips (N=13,900 pens; N=305 fed-batch experiments) was analyzed using linear and non-linear machine learning models to identify feature importance and improve cell selection methodology. The models generated show an improved ability to rank top clones compared to the currently methodology, a finding that is expected to improve average clone quality in cell line development. / by Aaron Baskerville-Bridges. / 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 [77]-[78]). / Pratt and Whitney is expecting an increase in demand for new engines and for parts supportive of aftermarket service, maintenance, and repair. To avoid expensive capital investments in additional production capacity, Pratt is taking several approaches to better utilize existing capacity. In a business where historically margins have been high, demand was flat, and in some years decreasing, and staffing had relatively low turnover, conditions were not forcing leaders to focus on identifying ways to eliminate waste or adapt cutting edge manufacturing analytics. With the introduction of new and innovative products, Pratt & Whitney is quickly approaching conditions where demand will outpace capacity. Additionally, demographics of the employee base has started to hit a point where many key and tenured employees have started to and will continue to retire leaving a knowledge gap behind. / To attack this growing problem, Pratt is taking several approaches to win more efficiency and effectiveness out of existing capacity. These include lean initiatives supported by connected and real time manufacturing technologies. Sensors and monitors are primarily used to gather data about machine condition and performance which is fed back to calculate Overall Equipment Effectiveness (OEE), a lean metric used to identify waste in the manufacturing process. The production team in Columbus has done a lot over the past few years to increase production, but as utilization rates increase, they are looking for new ways to expand capacity. The problem faced by management is identifying and reacting to losses as they occur, rather than retroactively, which is caused, in part, by inadequate access to the data. / This problem of reacting timely to losses is exacerbated by attrition of experienced workers who had tribal knowledge of the processes and how to react, whereas newer employees have not developed those reactionary instincts yet. Pratt & Whitney in Columbus has been collecting and storing data from their forge presses for years; accessing and analyzing that data in real time and integrating decision making based off that data has not been a part of their process. Using machine state tags, that is logic based off Programable Logic Controllers (PLCs) to tell users if the machine is in a run state, going through a changeover, or sitting idle, management can view the state of machines anywhere they can access the Pratt network. This data has also been used to calculate production efficiencies by part number by asset by calculating actual cycle times and comparing them to the engineering design time per part. / This is fed as an input to the new scheduling tool developed over the past few months which is meant to capture the intricacies of how different materials perform on different presses and optimize total production time by maximizing tool life among the presses. I have identified key inputs and business analytics processes to evaluate suboptimal efficiencies in the production process. This has affected the manner in which Pratt & Whitney in Columbus conducts business and permeated throughout the management structure to be included in events from daily production meetings all the way up to weekly executive report outs. Initial results show scheduling efficiency would improve output up to 8%, and the data has been utilized to uncover other areas for efficiency gains amounting to a 25% go get by the end of the year. / This research has shown that a data rich environment can present you with a vast array of opportunities if the data can be aggregated and interpreted timely enough to feed the decision-making process of production and if the organization has a culture to embrace it. / by Caitlin Mary Butala. / 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, May, 2020 / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 113-117). / A major benefit of Additive Manufacturing (AM) is a faster timeline from design to fabrication. As AM has matured to be able to create functional prototypes and end-use products, the ability to quickly fabricate physical hardware iterations without associated tooling costs and lead times is now possible. Software companies have embraced iterative-based product development processes (PDP) such as Agile. Iterative development has allowed for the validation of innovative and untried solutions, fueling the rapid speed of software development. However, within complex hardware industries, like automotive and aerospace, almost all companies instead follow a Waterfall or Phase-Gate PDP. Large capital costs, along with the aforementioned lengthy tooling and supplier lead times, make the control and predictability of a Phase-Gate process appealing. However, the trade-off is a process where the final content gets decided near the beginning of a multi-year timeline, often translating to product launches with soon-to-be stale technologies. Within the context of automotive, this thesis explores how leading edge technology could continue development in a parallel Agile process. Though the use of AM, the new technology could be integrated later into a Phase-Gate process with minimal schedule risk or cost. This process keeps the strict one-way review gates for the more stable components, while allowing greater flexibility for innovative features that could benefit from further iteration. I use Design Structure Matrix theory to simulate the performance and schedule of this proposed PDP. I then discuss the implications of this new PDP architecture and its benefits for complex hardware industries in general. / by Donald Mateo Coates. / 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