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 70-71). / Fulfillment cycle-time, or the time it takes to pick an item from inventory, pack it into a box, and load it on a truck for shipment, is one of the main inputs in determining how quickly an online retailer can promise customer order delivery. The faster the fulfillment cycle-time, the later an order can be received and still make the appropriate truck for guaranteed, on-time arrival (e.g. same-day, next day, 3-5 business days). Thus, the customer experience is improved, as they are allowed to place an order later and still receive their purchases quickly. To take advantage of this, the retailer must first be able to measure cycle-time appropriately within their facility. This thesis examines the outbound fulfillment process within an under-performing Amazon fulfillment center (Site A) with the purpose of fully characterizing and measuring fulfillment cycle-time. Comparisons are drawn with like Amazon facilities, and a lean operations approach is taken to identify and eliminate major forms of waste in an effort to shorten cycle-time. The baseline analysis within this thesis provides evidence that current-state cycle-time at Site A is in fact 15% faster than originally thought. However, process improvements were still needed to bring cycle-time in line with the network standard. The remainder of the work within this thesis focuses on these process improvements and develops the following recommendations: 1. Standardize the pick process with a move closer to single piece flow. 2. Reduce and control queue length prior to the pack process in order to reduce non-value-added wait time. 3. Reduce batch size for critical items that must move through the facility the fastest. 4. Rearrange process steps to allow completion in parallel rather than series. The method for evaluating cycle-time and the implementation of lean solutions introduced throughout this thesis are useful as a template for similar analyses throughout the Amazon FC network, as well as within other warehousing and online retailer operations. / by Hugh Churchill. / 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 85-88). / One of the key focus areas of the General Motors (GM) Company's leadership is to collect, quickly analyze, and respond to customer feedback pertaining to product quality issues in newly built vehicles. This project is intended to complement the Quality team's initiative to develop a tool to combine data sources on product quality. Currently, the tool prioritizes issues based on the frequency of reported incidents, and does not integrate responses to open-ended survey questions. The objective of this project is to recommend methods in which customer satisfaction input can be used to improve product quality. We leveraged customer data and analytical tools to do three things. First, we identified sources of customer feedback across the organization to strengthen collaboration on listening to the customer. We then created a survey to assess the gap between customers and GM employees' definitions of terms such as quality, dependability, and advanced technology. Lastly, we used text analytics to provide structure to open-ended survey responses, which enabled us to identify concerns expressed by customers that were not otherwise captured using the current tool. The cross-functional approach enabled us to gather quantitative results to support observations and anecdotes of misalignments between consumers and GM employees define terms. Analysis shows that Dependability definitions are similar between employees and consumers, but that there is a significant gap for High Quality. Text analytics uncovered that customers were highly dissatisfied to discover that their vehicles did not have features they expected to be basic attributes. / by Cynthia Lin. / 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 47-48). / This thesis demonstrates the applicability of flexible labor and standard work in increasing labor productivity and improving quality in fulfillment center produce operations. Three hypotheses were tested: (1) labor productivity would be increased by implementing a flexible labor staffing model and shift-by-shift labor requirement projections; (2) additional labor productivity from redesigned products would be captured using the flexible labor staffing model with updated projections; and (3) product quality would be improved by defining and refining standard work for inspection processes and optimizing inspection frequency. Indeed, implementation of flexible labor generated an average 44% productivity improvement. Furthermore, introducing redesigned products and updating projections generated an additional average 16% productivity improvement. While stalled implementation of standard work made it difficult to show that standardizing inspection processes and optimizing inspection frequency improves product quality via improved inspection consistency, the project did identify several produce categories that did not require inspection and established a starting point to standardize inspection processes by documenting inspection best practices. During these changes, setting easily achievable targets that required an increase in performance improved team morale, while overly aggressive targets would have worsened morale and hindered implementation. AmazonFresh produce processes can be further improved by ensuring the Produce Receive function serves as a "first line of defense" against quality issues, redesigning more products to reduce downstream labor requirements, implementing standard work in inspections, experimenting with lower bin inspection frequencies, institutionalizing quality and productivity metrics, and deploying changes across all AmazonFresh sites. Concurrently, AmazonFresh leadership may consider installing an incentive system that supports productivity and quality improvements, create roles within the central organization to support process improvements, and incorporate volume and service level requirements when designing new sites. Finally, future research opportunities include observing the effect of inspection frequency on inspector performance and assessing whether recent advances in sensor and conveyance technology can improve or replace existing processes. / by Vrajesh Y. Modi. / M.B.A. / S.M.

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 69-73). / General interest and international regulations have begun to create a world in which consumers demand to know how their products are made and companies must provide that transparency. With over 15,000 suppliers, Li & Fung wants to obtain better insight into its suppliers' environmental sustainability characteristics. The Sustainable Apparel Coalition's (SAC) Higg Index is currently viewed as the foremost way to assess a supplier's environmental sustainability; however, its facility module only applies to certain types of industries. Li & Fung requires a tool that encompasses its full supplier base and is short and robust enough to provide valuable insight into its supply chain and help it engage factories in decreasing their environmental footprints. This thesis discusses the methodology used to create such a tool and the information acquired during a successful pilot. To facilitate integration and adoption, the categories and some of the questions and language are modeled after that in the Higg Index. However, this scorecard incorporates many different and more focused questions, and the majority of its answers are standardized to promote easy analysis post-assessment. Its scoring system is also quite robust, aiming to award points accurately and with attention to the information and quality of environmental initiative undertaken at a factory. Finally, it incorporates a novel benchmarking and visualization section, which not only will help the user compare factories, but also more clearly see the areas in which a given factory is excelling or needs improvement. This knowledge will then allow Li & Fung to engage with its suppliers and help them decrease their environmental impact. The scorecard was piloted in 14 factories in the Shenzhen, Shanghai, and Hong Kong regions. Factory types included categories such as apparel, home textiles, umbrellas, and bags. Benchmarking was done across factory types to investigate commonalities and compare initiatives. / by Emily D. Koepsell. / 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. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (page 47). / As a global manufacturing company, Company X has built up an extensive and varied portfolio of manufacturing facilities. These facilities vary greatly in capability, age, and performance. This creates difficulties in effectively managing the manufacturing portfolio to best utilize the different resources. One approach to this issue of resource management is to analyze the manufacturing facilities as having a lifecycle. The lifecycle of manufacturing facilities is hypothesized to be a function of the people involved in the operations at the facility. This is generally captured under the term "culture." Part of the work focuses on characterizing the culture of a facility through the performance of its equipment in an effort to predict future performance, thus helping to determine the expected productive lifecycle of a facility. A decision tree framework is developed to help aid in this lifecycle determination process. The decision tree uses a combination of existing and modified metrics in providing recommendations for reference when determining the future of the manufacturing network. Importantly, the decision tree analysis is intended to be completed on a recurrent basis and executed with a focus on process discipline. Implementation of the decision tree will lead to improved decisions for the manufacturing network and an increased understanding / by Michael Kenneth Hammer. / 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 53-54). / Between 2017 and 2019, industrial robot installations are estimated to increase by 13% on average per year. As the industrial robot market has grown, so too have customer demands. Many industrial robot manufacturers are in a position to capture this growth by improving in on-time delivery, quality performance, and product offerings. This master's thesis is devoted to providing manufacturers methods for increasing quality performance for robotic controllers. To improve quality performance, we focus on finding the connection between controller quality performance at suppliers, manufacturing, and customer sites. We consider this valuable in the context of a manufacturer's R&D to set operational quality targets and predict the cascade effect in the supply chain. This study includes a deep dive in quality performance and methods to predict future performance. The analysis includes a look at quality metrics in the robot industry. We forecast future performance against these metrics using an available dataset and regression modeling. Because we do not discover strong regression models, we propose dataset statistics to forecast future quality performance. We have 2 recommendations based on our research. Moving forward, we recommend increasing transparency for quality data collection to create a more robust model with stronger prediction capabilities. We also recommend a total cost of quality approach in determining ideal quality metrics. / by Sonny Patel. / 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 (page 67). / In the last few years, US hospitals have faced severe challenges with bed capacity management that leads to capacity congestion. Delivering patients to the right bed at the right time is very important to patient care quality. However, the current process employs a self-reporting system to receive bed availability from each unit. This method does not provide consistent estimates nor does it provide a standardized, proactive bed capacity management perspective. In addition, the Department of Medicine (DOM) has a very complex patient population, both clinically and non-clinically. Various team structure and uneven distributed bed resources introduce additional challenge on patient discharges. The project aims to develop a predictive analytics tool that consistently and reliably identifies potential patient discharges in the next 24 hours. The prediction tool allows hospitals to incorporate a more proactive bed capacity management process. Every day, a ranked list with each patient's likelihood to be discharged will be the output. This list guides a more focused conversation within the care team to make patient discharge decisions. In addition, the prediction tool provides a comprehensive summary of barriers to discharge. In this work, we extended the model developed by Zanger [9] for predicting surgical patients' discharges to medicine inpatients' discharge prediction. By partitioning the training and validation set by the date on 12/31/2017, the current performance for the full model on January 2018 medicine inpatients has a prediction power of - 0.74 (Area Under Curve of a Receiver Operating Characteristic curve - AUC ROC there onwards). We further evaluated the model performance for specific patient populations. With patients' Length-Of-Stay (LOS) up to 3 days, the model's performance in terms of AUC ROC can reach ~ 0.8; 0.78 for model with patients' LOS up to 5 days, 0.77 for model with patients' LOS up to 7 days, and 0.72 for model with patients' performance up to 12 days. In addition, the model can capture 57.8% discharges in the next 48 hours, and 33.1% discharges in the next 24 hours. / by Ling Qi. / 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 75-76). / Visualization technology is advancing at an impressive rate, and Raytheon is investing heavily in the technology to improve product performance and manufacturability. Raytheon Missile Systems (RMS) has recently upgraded their visualization space, called a CAVE (Computer Aided Visualization Environment), to a state of the art facility with over 500 square feet of advanced visualization screen space. This is part of an overall corporate effort to improve visualization capabilities; there are similar facilities in Massachusetts and Australia, with plans to add more in Alabama, Texas, and beyond. The RMS CAVE contains a stereoscopic 3D display with cutting edge immersive technology for large scale 3D visualization of hardware and facilities. The CAVE hosts a wide variety of design review events across the whole product lifecycle focused on product design and manufacturing, as well as facility / factory modernization. This work explores the use cases taking place in the CAVE, both established and experimental. Established use cases are design for manufacturing and assembly (DFMA) reviews, facility reviews, and demonstrations. New use cases introduced include 2D data immersion reviews, and VR based training. Despite the strong value that the CAVE was providing to those who used it, it was underutilized. The CAVE utilization was only 30%-50% at the inception of this work, leaving ample capacity to host additional events. The focus of this project is to understand and leverage the RMS CAVE in Tucson, AZ to its full potential by increasing utilization and maximizing the value of meetings taking place in the CAVE. To do this, we document the value proposition, and identify novel use cases to improve utilization and effectiveness of the CAVE. The CAVE value proposition is comprised of five main elements - immersive 3D, visual real estate, collaboration and engagement, context and decision making, and improved efficiency. The CAVE has historically been used as an immersive 3D environment, and this is reflected in the current use cases, which heavily leverage 3D content. While immersive 3D content is valuable, user research reveals that the ability to have a large amount of critical information in one place is just as important. To leverage this underutilized capability, the Immersive Design Center (IDC) team held four brainstorming sessions to identify relevant events that could be enhanced through use of the CAVE. Implementation of recommendations from these brainstorming sessions has resulted in a utilization increase from an average of 32% to an average of 67% in only a few months. Furthermore, analysis indicates the majority of this increase is a result of newly identified use cases. Qualitatively, groups have been more engaged, and collaboration has been much stronger than in a traditional meeting space. These qualitative benefits materialize into tangible savings, with comparable facilities recouping initial investment costs in only six months. Raytheon has identified specific savings related to design for manufacturing and assembly (DFMA) reviews, laser scanning and facility reviews, and increased meeting efficiency. Results indicate that meetings are running 30% faster in the CAVE when compared to a traditional meeting space. We also explore a case study in virtual reality training, considering the application, customer feedback, and the business case. Feedback was positive, but there are several business strategy factors to consider including development costs, updates due to assembly process improvements, and commercial structure. / by Timothy Leonard Marquart. / 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, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 60-61). / This thesis explores the topic of the development of alternate parts for the aerospace industry, drawing on industry examples to demonstrate methods and approaches and the benefits to firms engaged in these activities. I will explore reverse engineering techniques, obstacles, and the role regulations play in the development of alternate parts. I will also demonstrate that reverse engineering is no longer a back room secret activity, yet instead a innovative engineering discipline that if matured and leveraged properly can provide valuable benefits outside of alternate part development. / by James Paul Tapley. / 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, 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 63). / The recent movement in healthcare reform requires hospitals to care for more patients while simultaneously reducing costs. Medical institutions can no longer afford to simply add beds and hire staff to increase capacity. They must use existing resources more effectively and develop innovative solutions to increase capacity. This project focuses on the redesign of surgical patient flow through multiple Post-Anesthesia Care Units (PACUs) at Massachusetts General Hospital (MGH). The PACU is where surgical patients recover following their procedure that takes place in the Operating Room (OR) suite. Some patients experience delays when leaving the OR due to the lack of a staffed PACU bed. These patients begin the recovery process in the OR which causes delays for to-follow cases. In addition, the OR nursing staff rather than a PACU nurse must monitor recovery, which drives higher costs and frustrates staff members. Therefore this study examined the sources of delay and sought to redesign the flow of surgical patients through the PACUs. Our main recommendation is to incorporate a "Fast Track" for the outpatient population that eliminates delays and expedites outpatient processing in the PACU. Segregating the outpatients and implementing the one-stop "Fast Track" recovery process will reduce average outpatient PACU length of stay (length of stay) by 27%, the equivalent of adding 1.8 beds of capacity. Through the application of operations management techniques, we can decrease the patient processing time or length of stay in the PACU, which in turn increases throughput and creates additional capacity. / by Trevor A. Schwartz. / S.M. / M.B.A.