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 (page 85). / with delays on the road and variabilities introduced by the major participants in the process, ie: distribution centers, drivers, etc. These sources of variability also make it difficult to measure the impact changes in transit time have on on-time performance. This paper focuses on trying to identify indicators of variability and incorporates them into quantile regression forest, a black box forecasting model, that will provide estimated scheduled transit times for a given probability of on-time arrival at the destination. With the use of Amazon's Q1 & Q2 2013 linehaul data, an analysis on performance trends based on length of haul were categorized to develop an understanding linehauls in North America. The outbound transportation team at Amazon faces the complex trade off between providing a sufficient amount of scheduled transit time to ensure ontime delivery to destination and the utilization rate of a truck. The ability to quantify how changes in scheduled transit time impact the performance of a particular linehaul allows transportation managers to assess this trade off. The paper explores a machine learning regression technique called quantile regression forests. The model was developed in R using the quantregforest package. It incorporates numerous factors about linehaul including: origin, destination, historical reporting on sources of late to arrivals, time to depart from origin and time of departure. The strengths of this black box model are in its ability to handle a large amount of data and continuously update its predicting structure to provide more accurate recommendations. Quantile regression forests also enable the user to specify the ontime performance percentage, p, that he/she wants the model to predict based on historical data. The final model at p = 95% provided a weight mean absolute percent error of 4.57% and a root mean square error of 2.22%. A four-week pilot was conducted to validate these predictions and the results are discussed. / by Gold Truong. / 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 (page 77). / A pragmatic approach is taken at analyzing and improving Sales and Operations Planning in a project based, engineer-to-order product line. Variability of product and components configurations and long lead times of the sales process and of material procurement during project execution place additional planning challenges in comparison with a standardized high volume product business. The study focuses on improving the visibility on future customer orders and on reducing the procurement lead time of project material. Due to the nature of the market and the customers of the studied product line, incoming orders timing is very uncertain when viewed on a project by project basis. However, there is a specific dynamic when the sales pipeline is analyzed on aggregate: Tenders that end up converting into a customer order will do so sooner rather than later. Historical data and observations are used to develop and propose a probabilistic model that connects today's open tenders to the expected new business out of those tenders. The organization is able to use this model to estimate what the current activity of the sales force can produce in terms of new business. The expected benefit is that the organization can act proactively if there is an expected reduction in incoming business from a specific region or major customer; it can also make targeted efforts to increase sales activity towards that region or customer. To increase its competitiveness when bidding for new projects, the organization has embarked on an effort to reduce the overall project execution lead time. A significant portion of this lead time is waiting time for project specific material (which comprises the biggest part of the BOM in money terms). A supplier flexibility scheme is proposed, under which a material order is placed in two phases: first the desired delivery time and the component rough specification are specified, and later on the exact specs are given to the supplier. An optimization model that utilizes the above concept is developed and offers the organization an optimal way to plan the project material procurement, given a desired reduction in procurement lead time. The expected benefit is that there is a justified and optimal method to reduce procurement time without building excessive material stock; it also sheds light to the "constraints" (specific materials or suppliers) that need to be lifted for further lead time reduction. / by Andreas Christogiannis. / 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 Aeronautics and Astronautics, 2015. In conjunction with the Leaders for Global Operations Program at MIT. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 85-88). / The maintenance and operation of commercial turbofan engines relies upon an understanding of the factors which contribute to engine degradation from the operational mission, environment and maintenance procedures. A multiple information source system is developed using the Pratt & Whitney engine to combine predictive engineering simulations with socio-technical effects and environmental factors for an improved predictive system for engine time on-wing. The system establishes an airport severity factor for all operating airports based upon mission parameters and environmental parameters. The final system involves three hierarchical layers: a 1-D engineering simulation; a parametric survival study; and a logistic regression study. Each of these layers is combined so that the output of the prior becomes the input of the next model. The combined system demonstrates an improvement in current practices at a fleet level from an R2 of 0.526 to 0.7966 and provides an indication of the relationship suspended particulate matter and engine degradation. The potential effects on the airline industry from city based severity in maintenance contracts are explored. Application of multiple information sources requires both knowledge of the system, and access to the data. The organizational structure of a data analytics organization is described; an architecture for integration of this team within an existing corporate environment is proposed. / by Daniel Richard Roberson. / 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 72-74). / With a goal of becoming a $50B company by 2020, Nike is improving current processes and using innovation to break barriers in technology, supply chain and manufacturing. The objective of the internship project with Nike's North America Always Available team is to provide recommendations on how the company can reduce the lead time from a when customer places a replenishment order to when the product is delivered to the customer's stores. The project focused specifically on a direct shipping strategy for the socks category and accelerating the order flow process at the distribution centers (DC). These two areas provide tremendous opportunity for growth for Nike through improved transportation, on time delivery to customers, and alleviating product congestion at the DC. Win Socks Back In the last few years, Nike has been losing market share in the sock category to competitors who are using faster and more aggressive methods, such as air freight, to ship socks and other products to customers. This has influenced Nike to begin to look at a variety of strategies, such as improving supply chain responsiveness and relieving DC congestion, as potential solutions. The primary goal of this project is to provide strategies that will reduce the lead time from factory to customer store. The approach consisted of using analytical and business principles to help the company to review and reassess the capabilities of the factory and DCs, as well as the transportation methods for short lead time (SLT) products. The output of the socks project was a review of current capabilities, an assessment of the company's ability to execute a direct shipping strategy, and preliminary recommendations on how to execute this strategy. Accelerate Order Flow In addition, Nike is also taking a closer look at the performance of the distribution centers. The order flow project will focus on improving the processing cycles and Call-for-Routing process for Nike's biggest accounts at two distribution centers in Memphis. The project focused on all product categories for Nike's Always Available product line. At this time, every account has a different ordering, processing and transportation, and this results in complexity for the DC and customer services teams because they are not able to plan for efficiency. The focus was on two initiatives: a quick win process improvement strategy and long term enhancement plan, for order writing, DC operations, transportation and routing. The final deliverable included a compilation of the current process for six strategic accounts, an analysis on the operational strategy for an ideal future state, and a model to review DC lead time performance monthly. / Chinasa Emeghara. / M.B.A. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015. In conjunction with the Leaders for Global Operations Program at MIT. / Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2015. In conjunction with the Leaders for Global Operations Program at MIT. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 63-64). / The Boeing 777 has seen an increased the rate of production from one plane every 7 days to one plane every 2.5 days. Wing production has relied on a sizeable work force and the use of overtime to meet this demand. The primary objective is to improve build efficiency by reducing variability in the production system. The impact of eleven variables was determined using a stepwise regression to predict for total labor hours across 250 airplanes. Three variables - travelers, defects, and quality assurance response time - accounted for almost 50% of the variability in labor hours. Other variations included engineering changes and rate breaks. Moving forward, the Wing Majors shop will redirect resources to control travelers, improve quality, and minimize quality assurance delays. / by Esther Hu Mangan. / 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 69-73). / A new facility seeks to manufacture inlets for the turbine of the new NDA aircraft. The inlet is a new design and has never been built before. To complicate matters the manufacturer, Aeron 2 Company, has not traditionally produced any inlets, let alone at the expected production rates for the new aircraft that already has thousands of orders. It was decided that in order to meet demand production must be automated in a similar way to the automotive industry. Aeron, and the commercial aerospace industry as a whole, are inexperienced with automation systems and they believed that they lacked the in-house knowledge to develop and integrate their own automated system. They entered into a contract with a robotics integrator that is has traditionally operated within the automotive industry for delivery of a "turnkey" system. This research covers a six-month period that overlaps with the beginning of Factory Acceptance Testing (FAT) of the automated assembly line. Initial observations hinted at the possibility of a strained relationship between Aeron and its automation integrator. This thesis sought to understand if the relationship was indeed strained, and if so, what were the causes and could it be improved? The Aeron group most involved with this system purchase, referred to as TWS, is used as a case-study to investigate the different factors that can affect an interorganizational relationship during development of a technically complex system. The research and recommendations can be applied to other companies that are involved in inter-organizational relationships founded upon development of technically complex projects. An inductive-deductive iterative approach was used in three phases to conduct this research. Beginning with background research in phase one an inductive approach is used to develop a qualitative model of the inter-organizational system. This model leads to additional questions regarding inter-organizational relationships and their more important factors, such as expectations and trust; namely, what are the expectations that are held by each party in the relationship and what are their impacts? These additional questions are addressed in a deductive manner in phase two. Through the use of surveys sent to different automation teams across the organization, data was collected that reflected the different relationships. These survey results indicate interesting customer perceptions of their respective automation suppliers. At the time of this writing all of the automation projects are perceived to be technically complex, yet some contractors are perceived as more technically capable than others. These data also indicate a general lack of trust. Expectations are addressed using a simple matrix that outlines the different technical and management expectations held by TWS in regards to the equipment being purchased and their integrator. A number of expectations held by TWS are unrealistic and have, as of this writing, gone unfulfilled. The third and final phase revisited the initial model in phase one and made modifications based on findings from phase two. For technically complex projects, such as the development of an automated assembly line using immature technology to build a new product, it is impossible to capture every design specification for the system. Design changes will occur. It is imperative that correct expectations regarding the equipment and the firm's capabilities are correctly set at the beginning of the project to reduce a shortfall in expected performance versus actual performance. Expectations, including those that are unrealistic, that go unfulfilled lead to decay in trust. This decay in trust in turn leads to a decreased likelihood in project success. It is recommended that Aeron shy away from "turnkey" systems for such state-of-the-art projects in the future, or at least until Aeron becomes more familiar with automated assembly and their integrators become more familiar with aerospace requirements. This research is most applicable to commercial aerospace companies entering a formal relationship with an automation integrator for an automated assembly line. The research does have general recommendations that are applicable to other industries engaging in similar endeavors. Future work would include a lifecycle study of trust levels within an interorganizational relationship and how they change over the course of the project from conception to delivery. / by David L. Hume. / S.M. / M.B.A.

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015. In conjunction with the Leaders for Global Operations Program at MIT. / Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2015. In conjunction with the Leaders for Global Operations Program at MIT. / Cataloged from PDF version of thesis. / Includes bibliographical references (page 57). / Quest Diagnostics is a large company that analyzes millions of medical specimens every day using a variety of analytical equipment. It is implementing a fully automated line in a major laboratory. The automation line is Quest's first major automation initiative and will serve as a pilot for future initiatives. An important element of implementing this initiative is the transition from manual to automated specimen handling operations. Furthermore, it is crucial to model the behavior of the newly automated production system. This thesis discusses the risk analysis performed on the transition from manual operations into automated as well as a discrete event simulation developed to model the automated system in its planned final state. The risk analysis was performed by identifying the risks of the transition of each affected analyzer and scoring each risk based on its severity and probability of occurrence. A reduction factor was added for analyzers that were to be transitioned later in the sequence schedule to account for the ability of the team to learn with each equipment transition. The simulation was based on existing process flow diagrams and populated with data from the two largest labs to be consolidated in Marlborough, MA, which comprise 90% of the expected volume. The simulation results were then compared to a previously performed static simulation and to current data from the Marlborough lab, which is now operational. This revealed a discrepancy between the simulation and the current data in terms of total specimens processed at each analyzer. This is attributed to the differences between the current manual process and the expected automated process. The dynamic model shows that the planned automation line can support the expected specimen volume even with 10% reduction in equipment efficiency. A planned 20% increase in volume was also evaluated along with its associated increase in capacity. The automation line can support the higher volume with the planned increase in capacity. Although these results are promising, further work is needed to validate the model results once the automation system is fully operational. / by Rafael Garcia Secundo. / S.M. / M.B.A.

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018. / Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 59-62 ). / Li & Fung is a global, leading trading firm that connects manufacturing vendors with retailers. Li & Fung is responsible for the supply of beauty products, furniture, and apparel, with the majority of sales in the apparel category. Li & Fung has developed strong relationships with a large portion of global retailers and maintains a leading market position in the global garment market. Furthermore, Li & Fung leverages a complex supply chain of over 16,000 partner factories across 40 countries. These factories employ hundreds of thousands of workers who perform the difficult work of producing a variety of garments. This large footprint of factories and employees results in an equally large environmental footprint. Although it is well known that the environmental impact is substantial, with researchers stating that the apparel industry is one of the largest global polluters, it has been difficult to quantify the business impact as a whole, let alone the impact of a single garment. Through this internship, the objective was to quantify the environmental impact of factories and products. This quantification will enhance decision-making and arm the business with a toolset to help factories improve and drive down impact in a targeted manner. Furthermore, these quantifications are manifested in product level footprints and factory metrics calculated with the use of internally generated data and external data. The internal data provided much of the backbone for the analysis and its collection was completed through an internally developed, proprietary tool. External data was then gathered to address information gaps in the supply chain. Together this data formed the basis for Li & Fung's Environmental Assessment Tool. This tool provides potential benefits at all levels of the supply chain. In particular, it allows designers and customers to make informed decisions about product attributes that drive environmental impact, factories to compare their environmental impact against an appropriate peer group and make educated decisions, and Li & Fung to quantify their environmental impact and take steps to address environmental hotspots. / by David Hillstrom. / 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 54-55). / Li & Fung deals with many factories that are very geographically dispersed. These facilities generally do not have the capital available to invest in new technologies and processes, and the extremely manual nature of garment fabrication is the standard as a result. As customers continue to demand quicker product turn-arounds and higher levels of customization, factories need to better understand their current process limitations in an effort to optimize their internal operations. Since most of these factories collect virtually no process data, managers have a hard time focusing on areas in which to improve. This project is approaching the question of "how can we use technology in a responsible and sustainable way to better understand our process?" from the perspective of a factory manager, who cannot necessarily invest in sophisticated software and hardware systems that other industries have adopted to monitor quality. As a result, this project focuses heavily on the user experience of both the operator (quality inspector) and the manager, as both need to be able to interact with the proposed data system easily and reliably. The primary goal of this thesis is to detail the design and implementation of a data collection platform (built during internship) for use in low-tech garment factories that will: -- Enable the procurement of process data (specifically as it relates to quality) from operators in real-time. -- Allow factory management to easily view and analyze collected data. -- Employ an intuitive front-end user interface that allows operators to quickly and reliably collect data. Since a substantial portion of this internship was spent designing, building, and testing this data collection interface, the thesis will reflect the nuances associated with building and implementing factory data systems in low-tech factories where human interaction is the primary driver of system adoption. The design and deployment of this system was ultimately successful and resulted in a robust prototype that continues to provide Li & Fung with insights into how to achieve their ultimate goal of connecting their factory network to a centralized data platform. / by Brandon Douglas Jennings. / 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 63-64). / This thesis proposes a demand prediction model for utility vegetation management (VM) organizations. The primary uses of the model is to aid in the technology adoption process of Light Detection and Ranging (LiDAR) inspections, and overall system planning efforts. Utility asset management ensures vegetation clearance of electrical overhead powerlines to meet state and federal regulations, all in an effort to create the safest and most reliable electrical system for their customers. To meet compliance, the utility inspects and then prunes and/or removes trees within their entire service area on an annual basis. In recent years LiDAR technology has become more widely implemented in utilities to quickly and accurately inspect their service territory. VM programs encounter the dilemma of wanting to pursue LiDAR as a technology to improve their operations, but find it prudent, especially in the high risk and critical regulatory environment, to test the technology. The biggest problem during, and after, the testing is having a baseline of the expected number of tree units worked each year due to the intrinsic variability of tree growth. As such, double inspection and/or long pilot projects are conducted before there is full adoption of the technology. This thesis will address the prediction of circuit-level tree work forecasting through the development a model using statistical methods. The outcome of this model will be a reduced timeframe for complete adoption of LiDAR technology for utility vegetation programs. Additionally, the modeling effort provides the utility with insight into annual planning improvements. Lastly for later usage, the model will be a baseline for future individual tree growth models that include and leverage LiDAR data to provide a superior level of safety and reliability for utility customers. / by Wade Allen McElroy. / M.B.A. / S.M.