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 Aeronautics and Astronautics, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 99-109). / The majority of current unmanned aircraft system (UAS) research is focusing on small UAS operations at low altitude over rural and underpopulated areas. There is a gap in research about UAS greater than 55 pounds. The first unmanned aircraft to operate in this airspace is likely to be unmanned air cargo vehicles. This paper analyzes the commercial market opportunities for medium- and heavy-lift cargo UAS by developing value propositions for each viable market. A multi-criteria decision analysis (MCDA) tool was developed to analyze the value proposition for cargo UAS compared to other transportation vehicles. The MCDA tool evaluated the value generated from three different value attributes: cost, time, and vehicle characteristics. These were applied across fourteen different reference missions to assess the potential utilization of cargo UAS in those markets. The results of the analysis showed that a medium-lift cargo UAS is the best transportation vehicle for organ/blood transport, medical equipment transport, urgent delivery, remote delivery, and search and rescue operations. Heavy-lift cargo UAS proved to be the best transportation vehicle for oil rig delivery, HVAC service, and disaster relief. Additional findings showed that the most significant method to reduce cost for medium-lift cargo UAS is by applying autonomy and advanced command & control systems which facilitate the operation of multiple vehicles per operator. The most important consideration to reduce cost for heavy-lift cargo UAS is to increase the specific energy of the batteries used. / by Patrick C. Butler. / M.B.A. / S.M. / M.B.A. Massachusetts Institute of Technology, Sloan School of Management / S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics

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 76-77). / In an ideal production system, supply exactly meets demand. Instantaneous, correct quantities arrive exactly at the right location when needed. However, real-world production systems often have variability- a change in the quantity demanded, a broken part, a shipping delay for a snow storm. The variability can be random, so companies are left with a dilemma: too little inventory buffer and a shortage may occur; too much inventory and capital is unnecessarily tied up in inventory sitting on the shelves. Using research conducted at the Boeing 737 program as a case study, this thesis proposes the application of a multi-step approach to optimize the total cost of the production system, balancing holding cost (inventory) with the disruption cost of a shortage. The initial pilot shows that small increases in inventory can have an order of magnitude of cost avoidance. The methodology includes system observation, qualitative interviews with Boeing employees, quantitative data gathering and analysis, proposed changes, and measured results. First, the historical supply and demand variability of the system is identified. Second, the cost of a shortage is estimated for the system. Next, an analytical approach to set safety stock levels is applied to balance the cost of inventory held with the cost of a shortage. By reducing the variability in the system, inventory levels can be reduced while maintaining the service levels. This process is then repeated at regular intervals to optimize the total cost of the system, balancing inventory holding cost and the disruption cost of a shortage. / by Jacob Philip Pellegrini. / 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 87-88). / Historically, the Storage business unit of the Dell-EMC Infrastructure Solutions Group (ISG) has maintained large inventory buffers to deal with high demand uncertainty and minimize part shortages. High product configurability and complex product structures continue to present challenges to effectively managing component inventory. In addition, many supply and demand planning decisions are contextual rather than process driven, making it difficult to understand precisely how inventory level is influenced by its independent variables. The objective of this project is to develop a set of dynamic inventory policies to enable inventory reduction at ISG while maintaining or improving cycle service levels. Our approach is based on modeling the inventory behavior of the existing supply chain system, and generating inventory policies that more accurately reflect consumption within the system. Three parameterized inventory policies have been built and tested. / We modeled inventory, forecast and actual demand data, used demand classification techniques to selectively adjust policy recommendations for certain drives and validated policy performance by adjusting input parameters. Based on model training for three quarters from August, 2017 to May, 2018 and validation from May, 2018 to August, 2018 our final choice was an order-up-to policy developed by fitting empirical distributions to historical forecast errors and using those distributions to recommend safety stock levels. The policy was applied to 111 CFGs representing 2,758 part numbers. We used August, 2018 to November, 2018 as a test period and applied the policy to observe its performance. Results indicated a 96.40% service level and 36% mean inventory reduction as compared to the baseline, which had a 98.40% service level. The 3.60% loss of service represented 56 shortages. / Of those, we identified 31 that could be eliminated through simple policy refinement, leading to a revised service level of 98.55%. Overall, our results suggest that a mathematical inventory management approach can be used reliably to model the hard drive supply chain, recommend an inventory policy and realize significant inventory reduction opportunities without compromising service level. This thesis concludes by proposing important supply chain system design changes, where several issues at the root of ISG's inventory management challenges reside. / by Ephraim D. Machtinger. / 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 Chemical Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 99-105). / A critical component of the biopharmaceutical development cycle is the selection of the cell line that will become the Master Cell Bank for product manufacturing for clinical and commercial use. This cell line selection process is resource-intensive, requiring several months, involving hundreds of cell cultures and corresponding assays, and is largely conducted on a per-experiment basis. Ultimately, a single cell line that can yield product of consistently high quality and titers is selected. In this thesis, we aggregated historical, pre-clinical program data to create analytic tools. We deployed machine learning algorithms to produce insights and provide predictive power for cell line selection in future experiments. Our models reduced prediction errors by 38 - 90% for bioreactor end-point titer and product quality metrics. These interpretable and robust models lead to better knowledge of key attributes affecting titer and product quality as well. Our models are currently deployed as a web-based tool, and pilot studies prove we can generate massively parallel in silico predictions with high accuracy. Ultimately, our project can lead to more productive and higher quality cell lines and reduced development cycle times. Utilizing a modular algorithmic framework, our novel application of machine learning not only delivers efficiency and differentiation in the cell line selection process, but also promotes a scalable and transferable digital platform for analogous applications throughout the biopharmaceutical industry. / by Yucen Xie. / 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, 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 (page 91). / The retail industry is shifting to enable companies to respond faster to consumer demand and expectations. For any retail company, this requires speed in their supply chain from new product generation to final order delivery. Companies that store product in centralized distribution centers must shorten the time it takes to ship a product from when an order is placed. This thesis describes the detailed operations within a large distribution center and uses it as a basis for improving delivery time of a product or order within the four walls of the building. The current system is subjected to increased variability in workflows from work planning to work completion, causing delays within sequential work functions and a longer overall delivery time. These effects are magnified by the inherent tradeoffs in the work process format and the work behaviors of the employees. A new system of work was developed to standardize the workflow at a large distribution center and decrease observed order delivery times. This solution was a work scheduling system that established clear expectations for work completion as well as tools needed to reduce the variability in the system. Under this new system, the average order delivery time is expected to decrease to a third of its current cycle time. This research was conducted in partnership with Nike Inc. / by Stephen Michael Greenlee. / 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 85-86). / Composite airplane manufacturing requires the use of autoclaves to cure composite materials in order to create durable, lightweight parts for use in airplanes. The large size, complexity and utility consumption of this equipment makes it an ideal starting place for cost optimization. Cost modeling and the framework created by this research provide input to understand the cost impact of the complex decision between multiple part capacity and single part capacity autoclaves. The results of this research include the identification of cost drivers for the autoclave equipment as focus areas for future cost reduction efforts. Additionally, wait time modeling illustrates how multiple capacity autoclaves increase work in progress and queue lengths and how to assign costs based on the impact of batching to production flow. The framework and analysis also show cost sensitivity to offloading parts and changes in production rates by using linear optimization algorithms to evaluate different scenarios. The framework is extendable to other capital equipment with complex tradeoffs by serving as a starting point for a data driven understanding of costs from recurring, non-recurring and production flow factors. / by Mauricio Salvador Benitez Cardenas. / 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 66-67). / The retail landscape is rapidly changing as evolving consumer habits are resulting in smaller batch quantities and shorter lead times, requiring Li & Fung to have a more digitally connected, nimble vendor base. Li & Fung uses a supplier network of thousands of garment factories around the world, the majority of whom are still capturing quality and production data manually, resulting in incomplete and inaccurate records. Factories see the value in making their operations digital, but most are low margin businesses that do not have the capital to make significant investments. This project is focused on the development of a cost-effective, digital tool to capture quality and production data at the end of a production line. This new tool will: -- Allow managers to quickly access real-time data analytics on their factory, -- Enable factories to make immediate root cause corrections in the sewing line, -- Serve as a gateway for Li & Fung to more proactively manage its vendor base,-- / Give Li & Fung visibility to eliminate unnecessary inspection activities and reduce costs. The project began with an initial hardware prototype created in 2017 that evolved into the Phase One version of a mobile application which was delivered in early 2018. User testing was performed in three factories in India and Malaysia, where feedback was incorporated into a comprehensive redesign in Phase Two. The thesis will detail the needs and challenges from both the factory and Li & Fung viewpoints, and how this digital tool seeks to address them. For garment factories, the tool is cost-effective and simple, enabling factories to become digital in a very accessible way. The tool introduces garment factories to technology and Internet of Things without over-complicating their operations. / For Li & Fung, the tool provides much-needed insights into the actual performance of the vendor base, allowing Li & Fung to achieve many of its strategic initiatives related to inspection cost reduction, vendor selection, and production tracking. / by Kevin Hsu. / 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 (page 55). / J&J Vision Care (JJVC) uses production scheduling methods that are not fully optimized, causing over-production of certain SKUs, and reducing capacity for other SKUs on backorder. This makes planning a weekly run-schedule for each line difficult. It is also difficult to understand where to invest capital to create an optimally flexible fleet of production lines. JJVC is currently capacity-constrained, so optimizing the production to increase output will directly translate to additional revenue. The three main areas that the leadership team wants to explore in this project are: 1. What is our current fleet flexibility? 2. How much capacity can be freed up if our fleet was more flexible? 3. Can we create a cost modeling tool that will provide more granularity in brand and sales channel profitability? First, the brands and SKUs on each line that are "validated" to run (by FDA, etc.) must be quantified. / Not all validated SKUs on a line are "runnable" though: Process issues often arise in the plant that prevent some of these validated SKUs from being produced (e.g. mechanical tolerances, chemistry, etc.). Therefore, the gap between validated and runnable SKUs will be an opportunity to explore. One constraint originally studied was the "runnable" vs "validated" prescriptions at the Jacksonville site; The percentage of runnable vs validated SKUs is only 73%, meaning that 27% of the prescriptions that J&J invested time and money to validate cannot be produced on certain lines due to manufacturing issues. The impact of this constraint and others can be quantified to identify improvement opportunities. Second, potential additional capacity can be calculated by running a sensitivity analysis with the planning tool (i.e. the optimization model) to analyze how outputs (e.g. throughput, changeover times, etc.) are affected by changing certain inputs: Mold, core, and pack change times, production rate, minimum lot sizes, service level, etc. It is also possible to change the objective function to place more weight on certain user-defined parameters. / The impact of these changes were observed by collecting the master planning data for a defined time-period and running optimization scenarios. Various time horizons were used to gain an accurate understanding of the impact. Third, to understand how the initiatives described above improve both revenue and costs, a clear understanding of the profitability of each lens must be considered before JJVC management makes high-level strategic decisions. To make this possible, a Total Delivered Cost (TDC) model was developed and published a for the Contact Lens supply chain. / by Andrew J. Freiheit. / 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 53-54). / Cost pressures on factories and the growing movement towards sustainability are key motivations behind Li & Fung's (LF) drive towards innovation. Supply chain digitalization through initiatives such as the Internet of Things (IoT) and big data is one of LF's pillars in its Three Year Plan. LF is committed to making data visible, digitally connecting, and understanding and improving resource efficiency in its supply chain. However, there is an immense amount of data across LF's -16,000 partner factories that remains largely invisible and untapped to fulfill these objectives. This thesis serves as a case study that explored the use of sensors to monitor electricity consumption in factories. Prior to this thesis, small-scale pilots in China and India yielded energy saving opportunities of up to 15% of a factory's consumption, in a payback period of 1-2 years. Given these, a factory-wide sensor installation was developed and tested in one partner factory. / A six-step framework, and tools to support the initiative, were also developed and tested. Elements of this framework include: i) a method for prioritizing factories for rolling-out the sensor installation, 2) capacity-building materials enabling factories to install sensors and derive insights from the data, 3) environmental metrics for Li & Fung to benchmark factories, and 4) financing or incentive scheme to encourage factory participation. The case study in a factory in Dongguan, China yielded 45% energy savings in the air compressor, a payback period of three months, and additional savings opportunities from improving the use of CNC and injection molding machines. Although the sensors identified energy savings, feedback from the case study and from vendor road shows reveals that using sensors may be attractive to some, but not all factories, due to upfront cost, sensitivity to data, or competing investments or initiatives to reduce costs and/or improve sustainability. / LF may consider relying the Higg Index to improve visibility into the resource efficiency and sustainability of its network, and to segment the market for the electricity sensors project. Using the Higg Index can also provide insight to appropriate measures that a factory can take, ranging not only from installing electricity sensors but also with energy audits or direct investments in energy-efficient equipment. Sensors can also be part of a portfolio of digital, operations, and sustainability initiatives to develop a holistic way of collaborating with factories and driving change in the supply chain. Moving forward, enhancements to the electricity sensors offering whether to reduce the upfront cost, or by bundling the sensors with other supplier capabilities are recommended to improve project viability. / by Joseph P. Talampas. / 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 63-65). / As Boston Scientific's supply chain becomes more versatile in mixing their supply networks across divisions, there is new opportunity to re-optimize product flow downstream of manufacturing based on unique product attributes and network capabilities instead of solely legacy divisional flow. The current organizational structure, methods, and systems prompts product flow to be optimized within functional silos. However, there are no current methods or tools that readily enable management to evaluate the total system in an integrative manner or with respect to specific product attributes. This project aims to improve BSC's ability to determine optimal product flow by introducing a tool that optimizes across the downstream supply chain via an integrative perspective that accounts for product and network attributes. / The integration involves the major cost activities, such as freight, handling, and inventory costs, or what can be termed the total "Cost-to-Deliver" product from a manufacturing facility to end customers. The proposed optimization framework includes the inter-dependencies of cost drivers across the supply chain that are typically missed when solving in functional silos. We develop a decision support tool to determine optimal product flow across the various nodes within the downstream supply chain (manufacturing, sterilization, and multiple tiers of distribution centers) over a single period horizon that can be extend to multi-periods through a present value approach. This tool enables the decision maker to compare directly the trade-offs between two different constrained flows, as well as vary product parameters within this scenario comparison to uncover ideal product segmentation with respect to flow decisions. / To demonstrate the value for the tool, we used it to segment products with respect to the choice of transportation mode on a freight lane. We find that changing the standard transportation mode for several current products would yield five-year net present value savings of 10-35% of their current annual cost-to-deliver. Ultimately the insights gained, and framework leveraged, are relevant to other industries with multinodal supply chains with high-mix products and not just constrained to the Medical Device industry. / by Christopher DiAndreth. / 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