Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2003. / Includes bibliographical references (p. 50). / by Don J. Lee. / S.M. / M.B.A.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2001. / Includes bibliographical references. / by Jayson S. Kunzler. / S.M.

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014. In conjunction with the Leaders for Global Operations Program at MIT. / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2014. In conjunction with the Leaders for Global Operations Program at MIT. / 29 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 72-74). / General Motors (GM) is one of the world's largest automobile manufacturing companies and does business in over 120 countries, requiring a complex operations network. Operating with a focus on environmental impact has become a strategic pillar within the company, both in its products and in its supply chain. Specifically, the GM global logistics organization is driving toward greater emissions visibility and the identification of carbon dioxide reduction opportunities within its network. Key objectives of this thesis work include creating business tools and processes to record global logistics emissions data, which will allow GM to more accurately report logistics emissions and reduction efforts to shareholders, track network emissions over time, pinpoint carbon reduction opportunities that align with cost savings efforts, and understand and mitigate future risks to the business. The approach taken to address the above objectives unfolds into three distinct work streams: (1) implementation of industry-recognized methods and processes, (2) development of a global carbon footprint measurement model, and (3) emissions analysis of network change activities. Industry research and data analysis along with internal cost and network data were used to develop carbon measurement tools. These tools are capable of estimating mass emissions (tons C02) generated by logistics operations globally as well as the increase or decrease in mass emissions generated by individual network change events (e.g., changes in mode, mileage, shipment frequency, etc.). Furthermore, through close collaboration with logistics providers, GM fulfilled the necessary requirements to become an official shipper partner of the USEPA SmartWay program. Immediate benefits of the project work include using the resulting data for global reporting and benchmarking purposes, providing management with a new set of information that can be used to strengthen network change proposals, and tracking improvements in overall network emissions as well as the performance of individual providers. Long term benefits include stronger relationships with providers, reputational and governmental risk mitigation, and cost savings from increased fuel efficiency of operations. / by Adam J. Miller. / M.B.A. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015. In conjunction with the Leaders for Global Operations Program at MIT. / Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2015. In conjunction with the Leaders for Global Operations Program at MIT. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 102-106). / Carbon Fiber Reinforced Polymer (CFRP) composite manufacturing requires the CFRP part on the associated tool to be heated, cured, and cooled via a prescribed thermal profile. Current methods use large fixed structures such as ovens and autoclaves to perform this process step; however heating these large structures takes significant amounts of energy and time. Further, these methods cannot control for different thermal requirements across a more complex or integrated composite structure. This project focused on the below objectives and approaches: - Gather baseline energy and performance data on ovens and autoclaves to compare with estimations of new technologies; - Determine feasibility, applicability, and preliminary thermal performance of proposed heated tooling technologies on certain part families via heat transfer analyses. The project yielded the below results and conclusions: - Proved the capability of the modeling software to mimic an oven cure with less than 3% error in maximum exothermic temperature prediction; - Provided guidelines on when to use 1D, 2D, and 3D heat transfer analyses based on part thickness; - Concluded which size/shape of parts would work best for the single sided integral heating technologies; - Calculated energy intensity of incumbent technologies for comparison of future experiments on integrally heated tooling. Overall, this project helped steer the team into the next phase of their research of the technology and its applications. It provided recommendations on what type of parts the technology can be used as well as quantified the energy intensity of incumbents for comparison. / by Harrison Scott Bromley. / S.M. / M.B.A.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program, Massachusetts Institute of Technology, 2000. / Also available online at the MIT Theses Online homepage <http://thesis.mit.edu>. / Includes bibliographical references (p. 103-105). / For the past two decades, rising customer expectations and increased global competition have forced automotive manufacturers around the world to significantly improve the efficiency of their production operations. One critical area of improvement has been in external logistics, logistics involving shipment of materials from external suppliers to final assembly plants. This thesis focuses on potential cost savings and procedural improvements from the implementation of kanban systems for external logistics. These are called external kanban systems. This analysis covers many facets of external kanban systems, including their benefits over traditional external logistics systems, their impact on transportation methods, their effect on inventories, and their and their anticipated effect on organizational learning in the final assembly plant. This project was pursued to reduce the cost and improve the reliability of external logistics at Ford Motor Company's Saarlouis Assembly Plant (Ford-Saarlouis). At Ford-Saarlouis, the implementation of these external kanban systems served as a critical portion of the replenishment process, as a training tool to familiarize plant employees with kanban systems, and as a template for future external logistics improvements. / by Roger (Chip) Zaenglein, Jr. / S.M.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2006. / Includes bibliographical references (p. 71). / In order to optimize product designs it is necessary to quickly evaluate many candidate materials in terms of performance and processing costs. Evaluation using physical prototypes yields concrete results but is time intensive and costly when dealing with multiple optimization objectives. As an alternative, computer aided simulation is a reliable means of material evaluation and selection, is increasingly available to smaller companies due to the shrinking cost of computation, and is essential for handling the dual optimization objectives of manufacturability and performance in a timely and cost effective manner. To support this thesis, the author first examines iRobot Corporation's current process of experimental trial and error for evaluating and selecting a polymer material for use in the wheels of its robotic military vehicles. The author then demonstrates that the experimental derived performance results can be reasonably predicted using the viscoelastic properties of polymers, as captured in such models as the standard linear solid model, and that this predictability can be used to quickly simulate wheel performance with computer aided engineering (CAE) tools. / (cont.) Finally, the author performs a cost analysis of the current material evaluation/selection process versus the CAE approach to show the best path forward for incorporating CAE tools into the design process of smaller corporations like iRobot. / by Craig B. Abler. / S.M. / M.B.A.

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017. / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 124-129). / Despite efforts to address capacity constraints with a massive expansion less than five years ago, the Emergency Department (ED) at Massachusetts General Hospital (MGH) is again displaying consistent and serious symptoms of overreacting, including rising patient wait times and routine activation of capacity-related emergency management protocols. As MGH grapples with these challenges, it is imperative to understand precisely what is driving the congestion. In this thesis, will show there has been significant volume growth and ii) study whether these visits resulted in inpatient admissions that could have utilized alternative care pathways while preserving patient safety and quality of care. After collaborating with hospital staff to analyze ED patient volume in 2015, we conclude that avoidable admission candidates who transferred to MGH from other facilities occupied nearly 6 percent of the hospital's General Medicine capacity. Furthermore, the utilization growth associated with these patients was equivalent to 1.3 percent of all General Medicine beds. meaning transfers alone can account for the overcrowding symptoms mentioned above. In a second analysis. applying unsupervised and supervised learning methods to short-stay inpatients reveals that even generalized order data can reliably predict conditions associated with avoidable admissions. Building on this insight, we then develop a scoring method to identify avoidable admission candidates without requiring manual case review by a physician. / by Jordan S. Hoffmann. / M.B.A. / S.M.

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017. / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 83-85). / Company X is a defense contractor that has built its competitive advantage on superior product performance. Over the last 10 years, government contracts have been changing to include stricter manufacturing and cost requirements. With these changes, design engineers can no longer design the most technologically advanced product and then hand it off to manufacturing engineers to figure out how to build it. Design and manufacturing engineers must work together to design the product to meet the contract performance, cost, and manufacturing requirements. It is difficult to balance the need to create a new technology that performs and the need to develop a product that can be easily produced. There is an added contextual factor. Because Company X is a defense contractor, it must follow special requirements and guidelines to satisfy their customer. These requirements typically add time and cost to a program. Many of Company X's programs are funded by the government, which means Company X's process is dependent on what process steps the government will fund. These steps can change from program to program. Because of the variability between programs, Company X's product development process is tailorable to meet the needs of each individual program. The goal of this research is to determine the best methods to better integrate manufacturing and cost requirements into the product development process to ensure that high technology businesses like Company X can keep their technology-based competitive edge in the market while also meeting more demanding cost and manufacturing requirements. The research is divided into five parts. First, existing literature on high performing teams, product development, and design for manufacturing is studied to determine best practices. Next, two internal case studies are performed to characterize the current state at Company X. Then Toyota's product development process is studied to learn best practices from another company known for developing high performing products that are also producible. After that, a gap analysis is completed to determine where the organizational gaps and process gaps at Company X exist compared to the best practices found in the literature review, within Company X, and in the Toyota case study. In order to ensure that recommendations are viable at Company X, a cause and effect analysis of best practices is also performed. The research ends with recommendations and conclusions for Company X to improve their product development process. The case studies show organizational and contractual difficulties with balancing cost, performance, and producibility. Company X puts a strong emphasis on product performance with their goals and incentives, and there is a strong design engineering culture. The organizational structure at Company X and the funding provided by the government on contracts do not always allow design and manufacturing engineering to work together early in the development process. The two programs studied made an effort to bring manufacturing and cost requirements into the product development process early. They started with a focus on balancing cost, producibility, and performance, but as issues arose, the focus shifted to performance. The development cycle is so long at Company X that the people who start a program are not responsible to finish it. There are also separate development and production teams with no real feedback loop to share issues and lessons learned. In order to improve these organizational and contractual issues, five recommendations are made to Company X. These include: implementing true integrated product teams (IPTs), simulating Toyota's chief engineer position using goals and incentives, incorporating playbooks/checklists into the development process, applying AS6500 requirements to all programs, and developing a closed loop system for producibility efforts. Implementing these recommendations is expected to provide better quality products that are easier and more affordable to produce. These recommendations are also expected to provide faster development cycles, higher morale, fewer cost overruns, fewer schedule overruns, and better integrated products. / by Molly Elizabeth McLaughlin. / M.B.A. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2017. / Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (page 72). / Amazon inbound operations are staffed following a 'staffing-to-charge' model in which labor is planned to match the incoming volume capacity required by the weekly Sales Operations Planning (S&OP) forecast. Staffing-to-charge is a lean model of staffing that attempts to maximize labor utilization by minimizing the possibility of a labor surplus or deficit. However, due to inaccuracies in the S&OP freight forecast, poor visibility into incoming inventory, and last minute staffing changes, it is often the case that labor capacity is not adequately aligned with the actual unit receipts. This leads to additional labor costs and network inefficiencies. This project explored the current staffing policies and current system constraints such as forecast accuracy, backlog management, and hiring schedules to understand the scope of the problem. From these findings, an alternate method for staffing, known as 'Level loading,' was proposed. Level loading consists of staffing to a known and consistent headcount every day of the week with the intent to reduce staffing costs and labor capacity variability. Level loading was found to improve the efficiency of inbound operations, leading to considerable costs savings for the distribution center. The project also created an optimization model that allows Fulfillment Center managers to plan the transition from their current shifts to level loading; Amazon's Production Planning Team will implement this model by mid-2017. To fully achieve the benefits from level loading, the system requires a change in the planning of incoming freight. In particular, the incoming freight should be scheduled and planned according to a known labor capacity, as set by the level loading policy. This change to freight planning is currently being investigated. The study found that delayed restocking of the network is a costly inefficiency, similar in magnitude to the cost from excess labor capacity. To mitigate this, a labor plan that allows for greater capacity is necessary. The cost savings of more effective inbound operations offsets the additional labor costs of such a plan. The findings of this study are based on an Amazon warehouse, but a staffing model with greater labor capacity can be applied to inbound operations at any distribution center. / by Carla Li-Carrillo. / S.M. / M.B.A.

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 67-68). / This thesis proposes methods to both estimate optimal aggregate investment levels for a system of transmission towers by means of an integrated corrosion and failure simulation as well as a method to identify specific assets in need of investment through a statistical model of structural health. Limited tower replacements over the past decade have resulted in an overall aging of PG&E's transmission system, leading to managerial concerns about potential increased maintenance and replacement costs going forward. The utility is seeking to be able to forecast its future needs despite a minimal history of asset failure. This work establishes long-term investment scenarios by simulating asset aging due to atmospheric corrosion and integrating those simulations with maintenance, replacement, and failure cost estimates. In addition, the aggregate investment forecasts are supplemented with an asset health ranking methodology that enables more targeted resource deployment. Implementation of the simulation based forecasting provides long-term spend estimates - on the order of many decades - and enables the production of sensitivity analyses based on underlying parameters grounded in physical system properties. This advances current industry spend forecasting which relies on qualitative risk assessments and past cost trends. Asset health indices generated from structural properties and environmental data are also shown to correctly rank a structure with a historic reported structural issue as at higher risk than a structure without a reported issue at a rate of 70%. / by Max Tuttman. / M.B.A. / S.M.