Thesis (M.B.A.)--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 Global Operations Program at MIT, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 55-57). / Within the Technical Operations division of Novartis Pharmaceuticals, there is an aggressive vision to be the "Toyota" of the Pharma Industry by 2010. To accomplish this, PharmOps Switzerland has embraced operational excellence, IQP (Innovation, Quality, and Productivity). Still, there is more that the site, and more specifically manufacturing, can do to fully realize the benefits of adopting all aspects of IQP. Currently, there is a lack of adequate visualization on the shop floor. The current status and schedule of production cannot be quickly seen at the tools where the work is being performed. This thesis focuses is on improving the visualization and creating a set of KPIs (Key Performance Indicators) and visual displays that will improve performance Change, especially cultural, is difficult and takes considerable time and effort. Even when changes are implemented slowly with small iterations, it might not be well received. Without a strong culture of continuous improvement, teams may not perceive that there are things that can be improved. Historical metrics are comfortable and useful to the shop floor. Visual metrics have improved communication. / by Maureen E. Lawler. / S.M. / M.B.A.

Thesis (M.B.A.)--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 Global Operations Program at MIT, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 59-60). / Crisplant is a tilt-tray sortation system used in Reno (RNO 1) fulfillment center (FC) to group items by customer orders. On average., crisplant processes about 80% of the total outbound volume through its multipart operation flow. Because of high volume and complex process flow, the majority of defects, in RNO 1 FC. are seen in crisplant costing distribution center (RNO 1) significantly in labor hours. This research paper identifies and quantifies the major defects in crisplant, and outlines the solutions to reduce the cost of handling these defects in RNO 1. The project work thoroughly assesses the entire RNO 1 crisplant operations (induct, sort, pack, SLAM, and problem solve) through four-phase approach: Understand the crisplant Process Flow, Develop a Data Collection Framework, Collect and Analyze Data, and Identify/Implement Data Driven Solutions. Lean principles and methodologies were used throughout the project work especially when identifying solutions. For example, opportunities that improved the packing process were identified based on a deep-dive analysis as a part of the Kaizen study. The project results demonstrated 50% reduction in cost of handling crisplant defects in RNO l. Furthermore, it highlighted opportunities for additional savings by identifying solutions that can also be implemented in other FCs (i.e. SDF 1, TUL 1) with similar operation as RNO 1. / by Kashyap Patel. / S.M. / M.B.A.

Thesis (M.B.A.)--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 Global Operations Program at MIT, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 69-70). / The primary goal for the thesis is to develop a recommendation for Amgen's future prefilled syringe strategy related to its drug process development, supplier relationship management plan, supply and sourcing, and procurement. The goal is achieved 1) by analyzing the historic growth drivers in the market and current market trends including changes and challenges, 2) by developing an analytical tool to understand complicated market dynamics between suppliers and buyers, 3) by developing a few future scenarios on how the market will evolve based on former analyses and models and 4) by developing and finalizing a recommendation for Amgen's future strategy. The prefilled syringe market is uniquely interesting for several reasons: 1) the prefilled syringe is an important primary drug container to both biotechnology and pharmaceutical companies, 2) there has been only one dominant supplier in the US, 3) biotech has been challenged with quality issues related to prefilled syringes and required the highest quality standards of syringe suppliers, 4) biotech's stringent quality standards and relatively low volume, compared with other big therapeutic classes such as anti-coagulants (heparins) and vaccines, can make it less attractive for the suppliers to align to biotech's needs, 5) new launch of advanced auto-injection device requires even higher prefilled syringe quality standards, and 6) the market is reshaping rapidly these days. First, the thesis analyzes the prefilled syringe market's history, major growth drivers, key suppliers and buyers, and market dynamics featuring key players. Secondly, it turns to discuss the challenges and issues Amgen has faced with these days and the backgrounds. Thirdly, it develops recommendations regarding Amgen's decisions on single versus multi sourcing, supplier selection, and supplier relationship structures. Lastly, it should be noted that all views, opinions, and assertions made in this thesis are those of the author alone, not of Amgen. / by Joonhaeng Lee. / S.M. / M.B.A.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Global Operations Program at MIT, 2010. / Page numbers proceeded by chapter numbers. Cataloged from PDF version of thesis. / Includes bibliographical references (p. 107-111). / The purpose of this thesis is to develop a means of predicting direct operating cost (DOC) for new commercial rotorcraft early in the design process. This project leverages historical efforts to model operating costs in the aviation industry coupled with a physics-based approach. The physics governing rotorcraft operation are combined with fundamental considerations encountered during rotorcraft design to identify potential design parameters driving operating costs. Sources for obtaining data on these parameters for existing designs are explored. The response data is generated by estimating operating costs for seventy-seven currently available commercial rotorcraft models under a fixed set of operating assumptions. Statistical analysis of this data is combined with the physics and first principles approach to identify key explanatory variables demonstrating a strong relationship to operating cost. Multiple regression techniques are used to develop transfer functions relating rotorcraft design variables to direct operating cost. The analysis shows that the maximum takeoff gross weight of the rotorcraft design is strongly correlated with direct operating costs. Specifically, a simple regression model using the square root of maximum takeoff gross weight as the only explanatory variable can be used to account for over 90 percent of the variation in total direct operating cost (TDOC). After accounting for maximum takeoff gross weight, the analysis suggests that rotorcraft models with two engines have higher TDOC than those with a single engine. A multiple regression model using maximum takeoff gross weight and the number of installed engines in the rotorcraft design is presented and accounts for 97 percent of the variation in TDOC. This model allows designers to quickly estimate TDOC for new rotorcraft early in the design process, before many of the major design parameters have been finalized. In addition to the aggregate or total DOC models, regression models for a few key subcategories of DOC are developed including, fuel related DOC, airframe maintenance related DOC and engine maintenance related DOC. In the case of fuel related and airframe maintenance related DOC, the maximum takeoff gross weight is found to be the single strongest explanatory variable. For the engine maintenance DOC, the engine weight is found to be the single variable most strongly correlated with operating cost. We conclude that an appropriate measure of weight (maximum takeoff gross weight or engine weight) is an important driver for direct operating cost. After accounting for weight, the models are refined by considering additional explanatory variables leading to models of greater accuracy and complexity. The modular nature of the model presented allows operating cost estimates to be improved and refined as additional details of the rotorcraft design become available during the design process. / by Michael P. Giansiracusa. / M.B.A. / S.M.

Thesis (M.B.A.)--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 Global Operations Program at MIT, 2011. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 75-76). / The Novartis BioPharmOps division is responsible for manufacturing large molecule products, including monoclonal antibodies, for late stage clinical trials and commercial sales. The BioPharmOps site in Huningue, France is expanding their product line but is also trying to reduce costs; cost pressures are increasing as biotech products become a larger part of Novartis' pipeline. The site uses a standard cost method to calculate their product costs. However, when using standard costs it can be time-consuming to extrapolate and predict costs when inputs and assumptions (such as product mix or process parameters) are changed. This project describes development of a model that allows the factory to quickly and easily simulate new product mixes and process flows. This model provides the site with a different view of their costs that will help them understand their cost drivers more completely and thereby help enable strategic decision-making at the site. A model of this type can be used to provide unexpected insights but the data in it are not meant to stand alone. By using results from a cost model like this along with operational metrics like throughput time or changeover time, a site should be able to quickly predict the cost impact of process changes or changes in the production plan. / by Christina M. Simpson. / S.M. / M.B.A.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, June 2011. / "June 2011." Cataloged from PDF version of thesis. / Includes bibliographical references (p. 72-73). / Previous studies of continuous manufacturing processes have used equipment-factored cost estimation methods to predict savings in initial plant investment costs. In order to challenge and validate the existing methods of cost estimation, feature-based cost estimates were constructed based on a modular process design model. Synthesis of an existing chemical intermediate was selected as the model continuous process. A continuous process was designed that was a literal, step by step, translation of the batch process. Supporting design work included process flow diagrams and basic piping and instrumentation diagrams. Design parameters from the process model were combined with feature-based costs to develop a series of segmented cost estimates for the model continuous plant at several production scales. Based on this analysis, the continuous facility seems to be intrinsically less expensive only at a relatively high production scale. Additionally, the distribution of cost areas for the continuous facility differs significantly from the distribution previous assumed for batch plants. This finding suggests that current models may not be appropriate for generating cost estimates for continuous plants. These results should not have a significant negative impact on the value proposition for the continuous manufacturing platform. The continuous process designed for this project was not optimized. Therefore, this work reiterates that the switch to continuous must be accompanied with optimization and innovation in the underlying continuous chemistry. / by Donovan Collins. / S.M. / M.B.A.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Global Operations Program at MIT, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 85-86). / Amgen is a leader in the biopharmaceutical industry. It manufacturers and provides human therapeutics that drastically improve lives. Amgen's reputation and brand, its goodwill, is an invaluable asset to its ability to succeed in an increasingly competitive landscape. Because of this, risk management, both in manufacturing and in supply chain arenas, are directly linked to continuing long-term sustainable growth. With an increasingly global market and expanding pipelines, biotechnology companies, like Amgen, face a supply chain challenge to manufacture and distribute products using economically feasible methods that ensure patient safety. Preventing product mix-ups plays a key role in ensuring that safety. Marking nude product that moves intra-Amgen or to contract manufacturers will provide a higher level of confidence that the right product is reaching the patient. Several solutions for marking nude vials and syringes immediately rise to the top of the strata of potential technologies. Despite being promising, each technological solution has key unknowns that must be answered by rigorous labscale testing to provide quantitative data to make the best decision on the future of this process within Amgen. Along with the testing, it is clear that the financial landscape of the different solutions varies a great deal. Each potential solution will be analyzed to determine its capital requirements as well as ongoing costs. Lastly, the solution must be realistic to implement into Amgen's current GMP. And thus, each technology will be evaluated as it relates to the overall complexity of implementation into an already tightly controlled process. From a more macroscopic industry perspective, the FDA, as well as other regulatory agencies, has been discussing this issue for several years. Strategically, biotechnology companies are all hesitant to invest in a particular solution at the moment for fear that the FDA will require a different solution in the near term. In reality, biotechnology companies risk billions in R&D and drug development and are therefore, in a way, naturally risk averse when it comes to their processes and operations. Inventory and manufacturing operations are more driven by risk management than by cost. Of course, the important factor to remember is that risk management is a precursor to drug quality and patient safety. The majority of the risks that are controlled are risks that would either prevent environmental contamination of the drugs or affect the quality of the drugs. Altruistic or not, this has profound long term business strategy implications in an ultra-competitive marketplace where another biotechnology firm would certainly oblige taking market share if Amgen were to suffer a reputation ruining event. / by Robert Hardy. / M.B.A. / S.M.

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2015. In conjunction with the Leaders for Global Operations Program at MIT. / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015. In conjunction with the Leaders for Global Operations Program at MIT. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 84-85). / This thesis and the associated project explore lean automation strategies for high volume, high complexity manufacturing systems. In particular, we study how to reduce the footprint and cost of an automotive sealing line, while maintaining current levels of production, maintainability and safety. The key challenge researched in this thesis concerns how to reduce space requirements and cost of a highly automated facility without sacrificing system maintainability, safety or throughput. For this study, any solution must utilize currently available technology. The thesis will review the basic research, concept development, layout development and solution refinement activities that lead to a final concept and recommendation. The key findings for this study include three strategies that led to a lower cost footprint that consumed less space. These strategies are: " Intelligent reduction of conveyance systems "Increased system flexibility" Increased automation density Additionally the study highlights how these strategies complement each other when addressing cost and space reduction challenges. In this particular study the three strategies yielded space savings of approximately 33% and capital cost savings of about 10%. / by Peter Evan Kimball. / 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 57-58). / Flexible automation for drilling and countersinking has been successfully implemented in the assembly of a small number of aircraft structures. These systems have demonstrated the capability to reduce the risk of repetitive use injuries, improve quality, and reduce labor costs. Despite these successes, disruptive delays to production resulted when similar technology was initially implemented in the assembly of 777 wing structures. Baseline data was collected to analyze the performance of the system, and it was found that the delays were largely a result of machine breakdowns or error conditions during production. Three changes to the equipment and processes were prioritized because they could be quickly implemented and were expected to address some of the most-common causes of the in-process machine errors. Average drilling times were reduced by 5.9%, and maximum drilling times were reduced by 10% as a result of these changes. Process simulations based on the data demonstrated that the expected frequency of production delays was reduced from 37.4% to 17.6% of all wings produced. / by Zackary R. Eubanks. / 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, 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 68-69). / Li & Fung (LF) is a global supply chain manager for consumer product brands and retailers. Worldwide, LF contracts with over 13,000 factories. Frequently, these factories experience incidents, which are internally defined as "unplanned / unwanted events which have the potential to escalate or have already caused damage to stakeholders within the supply chain." In the factory context, this includes fires, labor strikes, and unauthorized subcontracting events, among others. Every incident costs the factories, LF, and the customers extensive time and resources to mitigate and recover from. Currently, LF manages incidents as they occur. Moving forward, LF strives to proactively mitigate risk by forecasting the probability that each factory in the supply chain will experience an incident. In addition to avoiding potential factory worker injuries, predicting risk will: (1) save LF time (and money) by being alert to incidents before they occur, (2) protect the LF reputation and maintain trust, and (3) demonstrate how LF is using advanced analytics to build a better supply chain. This project includes three primary components. First, an assessment to evaluate the impact of incidents on LF was performed, by investigating several case studies of different incident types in different regions of the world. Second, a predictive analytics model to forecast the probability that each factory will have an incident was developed, using historical internal and external data sources. The results are presented quantitatively and visually to provide clear and effective messaging and recommendations to LF management. Insights and challenges are outlined in detail to provide a thorough understanding of the model and recommend future alterations. Finally, the team developed short term and long term action plans to drive responsible sourcing decisions using the available data and initiate industry change. / by Rachel Marie Schmidt. / M.B.A. / S.M.