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, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 32-33). / This thesis investigates best practices for Highly Active Pharmaceutical Ingredient (HAPI) milling and blending. We utilize a qualitative analysis centering on a benchmarking study and quantitative analyses using a probabilistic capacity simulation and tradeoff methodology. The analyses indicate that the growing number of HAPI products in a manufacturer's portfolio may result in capacity constraints. Therefore, we recommend that manufacturers pursue process improvement technologies. Suggested process improvements include implementing online particle size measurement and Wash in Place (WIP) and Clean in Place (CIP) cleaning systems. Online particle size measurement allows for better process control and eliminates the need for HAPI blending for homogenization. Automated WIP and CIP systems decrease changeover time and allow for higher equipment availability. Additionally, the results of the analyses suggest that manufacturers consider standardizing transportation containers with the upstream vendors and downstream consumers. Lastly, from an organizational standpoint, we recommend that manufacturers include both subject matter experts and operations personnel when developing and implementing internal guidelines so as to ensure the guidelines are practical and uniformly applied. / by Prashant Setty. / 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, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 151-153). / Purpose - This thesis analyzes the transformation of the Small Components Cell in Pratt & Whitney's aftermarket division through lean manufacturing techniques. The thesis focuses on use of a labor capacity planning model, implementation of a new cell layout, and queuing theory. The project was 6.5 months long, running from June through December of 2012. Findings - In Chapter 4, the capacity planning model shows that demand changes significantly affect cell performance but that the product mix in the cell is even more crucial. The model highlights the best workforce allocations based on a given product mix or demand level. This analysis is expanded in a design of experiment that shows improving employee efficiency is the most effective means of expanding the capacity of the Small Components Cell. Four factors (employee efficiency, absenteeism, overtime, and the duration of employee breaks) have a significant effect on the ultimate capacity of the cell. The design of experiment allows the capacity planning model to be a useful predictive tool. The transformation of the cell into a lean manufacturing flow line requires a significant investment in change management process, including a focus on the logistical details of transformation, continual reinforcement of the vision with the team, and cross-training the workforce. The transformation resulted in a 94% reduction in non-value added part travel, a 72% reduction in flow reversals in the cell, and a 43% reduction in cell exits. Customer satisfaction metrics increase throughout the course of the project as well. Annualized EBIT performance improved by over 40% over the six months of the project, while the costs associated with reworking errors declined by more than 85%. However, on-time delivery of parts to customers failed to meet expectations because of the physical restructuring of the cell and a three month spike in demand which adversely effected cell capacity. Chapter 5 outlines the changes in business metrics, while Chapter 6 discusses recommendations and lessons learned. / by David Walker. / S.M. / M.B.A.

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2013. In conjunction with the Leaders for Global Operations Program at MIT. / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013. In conjunction with the Leaders for Global Operations Program at MIT. / Cataloged from PDF version of thesis. / Includes bibliographical references (page 51). / Circuit card manufacturing can be a highly risky and volatile proposition due to the placement of hundreds of small, high value components. Operator mistakes, design errors, and defective parts lead to thousands of dollars in troubleshooting and rework costs per product. Raytheon Integrated Defense Systems (IDS) Circuit Card Assembly (CCA) manufactures highly complex circuit cards at a high mix / low volume scale for various purposes. Due to the high input variability and small production lot sizes of this level of circuit card manufacturing, historical trending and defect mitigation is difficult, causing a significant portion of CCA's manufacturing costs to be attributed to troubleshooting defects and rework. To mitigate these costs, yield prediction analysis software is utilized to predict potential manufacturing defect rates and first pass yields of new designs. This thesis describes the creation and testing of a new data analysis model for yield prediction. By gathering and processing data at an individual component level, the model can predict defect rates of designs at an assembly level. Collecting data at the individual component level drives more comprehensive component-based calculations, greatly improving yield prediction accuracy and thereby allowing cost effective circuit card designs to be created. The increase in prediction accuracy translates to a potential $250,000 saved annually for Raytheon CCA from early defect identification and removal. Updated data retrieval and calculation methods also allow for much easier model maintenance, thereby increasing the relevance of yield prediction. This model can be easily incorporated into other design software as a next step in creating comprehensive concurrent engineering tools. / by Stephen Trinh. / M.B.A. / 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. / 10 / Cataloged from PDF version of thesis. / Includes bibliographical references (page 111). / We propose a novel method to quantify the cost of activities involved in the picking portion of order fulfillment. We adapt the general method of picking cost quantification to the specific situation of TP&G, a publicly held utility, to build a simulation model which calculates total cost (procurement purchasing costs + material handling costs) across TP&G's Construction Materials Supply Chain (CMSC) . We use the simulation model to demonstrate the effect of case pack quantities and various disputed (within TP&G) material handling policies on supply chain costs. Finally. we move beyond the descriptive results of the simulation model and build optimization models for a case where a single case pack quantity is held in inventory, under conditions of both deterministic and stochastic demand. We show that case pack quantity held in inventory greatly impacts supply chain costs. We also find the novel result that the optimal material picking policy for both deterministic and stochastic demand is a threshold policy whereby orders should be fulfilled with whole case packs up to the highest possible multiple of case pack quantity that does not exceed an ordered quantity. If the remainder of an order to be fulfilled exceeds a certain number of units in a case pack, that remainder should be fulfilled with a whole case pack (overfilled). This threshold can be efficiently calculated for all case pack quantities (optimal or not). / by Bradley Philip Genser. / M.B.A. / 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. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 87-88). / Monoclonal Antibodies (mAb's) are the fastest growing segment in the biopharmaceutical industry. They are used today as therapeutics and diagnostics for several medical applications, including various types of cancer, rheumatoid arthritis, psoriasis, severe asthma macular degeneration, multiple sclerosis and more. In recent years, industry trends and market pressure have driven pharmaceutical companies to focus efforts on increasing operational efficiency in order to reduce the financial burden associated with drug manufacturing. Consequentially, Novartis Pharma Technical Operations' is currently engaging in efforts to obtain Class "A" Manufacturing Resource Planning (MRP II). This project was chosen to analyze and address the current Integrated Business Planning (IBP) technically and financially by analyzing critical processes, their bottlenecks, and prioritizing improvement opportunities. We focus on tactical planning at the Multiproduct Process Unit at BioPharm Ops. This paper describe the development of a Tactical Planning optimization tool, which implements SuperPro Designerc and ScheduleProC (Intelligen Inc., NJ, USA) for campaign scheduling of active pharmaceutical ingredient production based on mammalian monoclonal antibodies (mAb's). Results have shown great potential benefits for Novartis, including but not limited to: Creating and modifying campaign schedules in hours (not days); increased operational efficiency; Max Run Rate Optimization, cycle time reduction and significant production cost savings; analytic tool to support long-term strategic decisions with the flexibility to address real-time adversity and automated conflict resolving. / by Shai Assia. / 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 Chemical Engineering, 2015. In conjunction with the Leaders for Global Operations Program at MIT. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 61-62). / A critical cost driver in the product development process is the manufacturing of clinical trial material supplies. The clinical manufacturing process operates under similar regulatory burdens as commercial biopharmaceutical manufacturing, but must operate with a high degree of flexibility to respond to emerging data from ongoing clinical programs and competitive intelligence. This project investigates the current state of clinical monoclonal antibody (mAb) manufacturing and generates possible future states for low-cost, flexible manufacturing. Pfizer currently has a world-class clinical manufacturing facility in St. Louis, MO, and forecasts have not predicted sufficient growth in the market to justify additional facilities under the current strategy. However, with many other companies turning to single-use technology, Pfizer would like to consider the benefits of a new low-cost, flexible facility for the benefits of flexibility and faster speed to market. A high-level facility model includes capital costs, technical specifications, and a dynamic view of the operational costs based on product attributes. This model shows that a campaign cost of less than $2.6M can be achieved with only 3 projects per year, which represents a facility utilization of less than 40%. At a loading of 4 campaigns per year, this facility would have a payback period of less than 3 years. The conservative facility design shows how capacity can be added with a low operational cost and less than $5M of overhead per year. Business factors such as pipeline prospects and availability of capital may prevent execution of the project in the near term, but investment in single-use equipment is advisable to gain experience in technologies that are likely to play a large part in biopharmaceutical manufacturing in the future. / by Daniel Evan Kress. / 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 53-54). / A strategic imperative for Amgen is to increase the number of patients that have access to the company's lifesaving medicines. As part of this goal, Amgen is launching a new manufacturing site in Singapore (ASM). Reliability of supply and quality control will be critical factors for successful ASM launch; therefore, this project will focus on two key objectives: optimization of raw material flow for the ASM warehouse, and transportation risk mitigation of disposable bioreactor bags. Optimization of warehouse operations helps ensure the site can supply enough drug substance to meet the needs of patients worldwide. ASM has a warehouse that is 1/3 the size of a traditional biologics manufacturing warehouse, and is projected to reach capacity during commercial production, increasing risk for the site and incurring the cost of outsourcing storage. A Warehouse Capacity Model was developed using inventory management principles, and then used to identify operations strategies that provide the greatest improvement in warehouse utilization for ASM. Transport risk mitigation for disposable bioreactor bags is critical to the manufacturing process, because ASM is using disposable technology throughout their drug manufacturing process. Even a pin-sized hole can lead to contamination and significant lost revenue. To reduce the risk to these bags during shipping, a twelve-member, cross-functional team was formed, consisting of experts at Amgen from seven different functional groups, including materials science and supply chain, to partner with the supplier to establish a transportation qualification plan for the 2000L bioreactor bag. Transport risk mitigation of bioreactor bags also reduces the required amount of storage, since fewer bags will need to be stored as safety stock. The transport qualification of 2000L bags was successfully executed, and the process was documented to guide future transportation qualification plans for disposables. The key recommendations are that in the short-term, ASM should hold materials with suppliers with warehouses in Singapore, increase frequency of delivery of materials, and utilize random storage location assignment. In the longer term, ASM should utilize storage from suppliers and Third Party Logistics Providers (3PL). For future warehouses, the Warehouse Capacity Model should be used in the design phase to give the team sufficient time to implement recommendations. For future transportation qualification plans for disposables, a pressure decay method is recommended for more robust testing of bag integrity. In addition, creation of a "library" of defects is recommended to improve visual inspections. / by Maxine Yang. / 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 64-65). / This thesis evaluates the use of postponement in the Drug Product (DP) supply chain at Amgen, which is characterized by highly variable production lead times. The motivation for the use of postponement in the DP supply chain is to reduce the lead time and improve the service level from the manufacturing site to the distribution centers (DCs). Amgen is undergoing a rapid global expansion and is now serving markets that operate on tender (bid) systems that require rapid fulfillment. To compound this challenge, FDA driven requirements have significantly increased the likelihood of generating Non-Conformances (NCs) in DP manufacturing, which in turn increases the production lead time variability. A simulation model was created in Microsoft Excel that uses historic production lead time and demand data to determine postponement levels and simulate performance of the system. Leveraging the simulation model, this thesis demonstrates that utilizing postponement in supply chains with highly variable production lead times can significantly improve service level and diminish customer lead time while potentially reducing global inventory levels. / by Nicholas Sazdanoff. / 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, 2018. / Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 94-95). / Abstract This thesis proposes a network swapping mechanism to approximate a virtual finished goods inventory pool under the constraint of fragmented commercial channel ownership. Globally, Caterpillar sells its product through a network of independently owned dealers which own their equipment inventory. These dealers are selling a product which has significant configuration complexity, a high coefficient of variation in demand, and long factory lead times - these three factors conspire to create a situation in which dealers must either maintain high levels of on-hand inventory or sacrifice customer service level. Virtual pooling can be a powerful technique for reducing held inventory and improving customer service level. In such a system, physical inventory is maintained in multiple locations, but a strong transshipment network allows inventory to be continuously rebalanced and effectively managed as a single pool. At Caterpillar, however, this is constrained by the fact that dealers are unwilling to unilaterally give up equipment that they own and which represents a potential sale, even if that equipment could be more effectively used by another dealer. This thesis proposes that a robust dealer swapping network that allows for multilateral swaps can generate universally beneficial inventory movement which lowers inventory and accelerates sales across the network. The mathematical formulation of this problem involves solving a longest path problem over a suitably defined network. In order to demonstrate the efficacy of this technique, a commercial network model was developed which allows for the simulation of multi-period equipment ordering, inventory management, and sales across a sample dealer network with and without network swapping implemented. Baseline simulation results conducted for a single vehicle class (Medium Wheel Loaders) suggest that network swapping has the potential to reduce on-hand inventory by more than 12% and decrease customer back orders by more than 17%. The swapping mechanism yields an NPV uplift of approximately USD 3 to 4M to the dealer network. This thesis concludes by proposing important extensions of the work conducted in this thesis to improve the practicality and financial impact of the proposed network swapping system. / by Kevin E. Schell. / S.M. / M.B.A.

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016. In conjunction with the Leaders for Global Operations Program at MIT. / Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 77-78). / Like companies from almost every industry, NIKE faces a marketplace that is transitioning toward e-commerce. Over the next five years, retail sales through NIKE.com are anticipated to grow meteorically. For NIKE specifically, this shift represents a change not only in where and how consumers shop, but also a significant transformation of NIKE's underlying business model. NIKE has historically derived the majority of its revenue from wholesale accounts, representing 76% of its revenue in FY15.1 The evolution from wholesale brick-and-mortar sales to online retail sales requires substantial changes across NIKE's supply chain and manufacturing strategies to better serve the online consumer's ever-increasing service, quality and product offering expectations and to capture the revenue and gross-margin upsides of vertically-integrated retail. Given the ripple effects of this business model shift that will affect every part of NIKE's business, NIKE Global Operations leadership seeks to identify data-driven solutions that support cross-channel, cross-function collaboration to capture end-to-end insight. The key objective of this thesis is to identify simulation capabilities and development processes that will help supply chain leaders get to enhanced enterprise insight, faster. Thus, the goals of this research are two-fold: 1) Develop general end-to-end simulation capability and user needs assessments, and 2) Develop proof-of-concept simulators that integrate cross-functional metrics and business drivers, and enable rapid 'what-if scenario generation and comparison. After identifying user needs regarding both simulation capabilities and simulator content, two proof-of-concept simulations were developed from critical cross-functional business questions posed by a wide array of interviewed stakeholders. The first simulation presents a holistic framework for understanding liquidation supply and demand in the context of digital's forecasted growth. The second simulation concept presents a framework for selecting products with optimal characteristics for onshore/near-shore advanced manufacturing. Both concepts demonstrate that data and organizational integration is both feasible and desirable within the supply chain strategy planning process, and that simulation tools are valuable investments that can provide enriched insight above current processes. / by Clararose Faith Voigt. / S.M. / M.B.A.