Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 53-58). / Companies spend significant resources on digital transformation projects that do not always meet expectations. This thesis contends that these projects fail or fall short because organizations do not consider the three fundamental flows of a supply chain; material, information, and payment. To address the issue, this thesis develops a lens to identify mismatches between material, information, and payment flows, and applies this lens to putaways and the post goods receipt process in the US Army's supply chain. The thesis identifies an increased risk of loss for putaways confirmed before physical movement could take place, and confirmations that occurred after seven days. The thesis recommends measuring putaway time as a key performance indicator and establishing a two duty-day key performance standard, which would hypothetically lead to a reduced rate of loss. With respect to the post goods receipt process, it was found that a failure to confirm goods receipt led to the creation of millions of dollars in phantom inventory and late payments. This thesis recommends allowing customers to pay for material even if intermediate digitized information flows were not confirmed. It also recommends monitoring material available to be received so that leaders can spot and address errors. By considering the three fundamental flows of a supply chain, digital transformation practitioners can achieve better results. / by Michael Sean Smith. / M. Eng. in Supply Chain Management / M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 66-70). / The second machine age is reshaping the way we work, do business, and collaborate. Today collaboration is switching from just among humans to between humans and machines. Mundane and repetitive tasks will be done by machines automatically, while humans can develop insights and make wise decisions supported by data streaming from intelligent machines. If and how different human-machine teaming decision-making structures would influence the organization's performance is important to understand, so that human-machine teaming capabilities could contribute the most to business outcomes. By using the augmented inverse propensity weight estimator method, this research empirically analyzes the average treatment effects of three different human-machine decision-making structures: Full human to AI delegation, Hybrid AI-Human with adequate human intervention, and Hybrid AI-Human with all steps of demand planning overrides. / These three decision-making structures are defined as treatment groups, and the traditional manual demand-adjustment process is defined as the control group. Effects of switching human-machine teaming decisionmaking structures from one to another are also analyzed. The performance of each treatment and control group is measured by the long-term forecast accuracy, short-term forecast accuracy, and customer inventory level. The project is based on an IT collaboration project between a large fast-moving consumer goods company and one of its largest e-commerce customers. The project implemented an AI-enabled demand-adjustment process to incorporate the external e-commerce customer demand signals into existing demand-planning process. Demand planners engage in the demand-adjustment process via web-based interfaces, to apply human judgment-based decisions. All the stock keeping units are randomly assigned to treatment and control groups. / The results show that after the implementation of human-machine teaming decision-making structures, both demand-forecast accuracy and inventory level are strongly improved by at least 47%. Overall, the Hybrid AI-Human with adequate human intervention model is the optimal decision-making structures between human and machine, which improves the short-term forecast accuracy by 53%, long-term forecast accuracy by 64%, and inventory level by 70%. The Hybrid AI-Human with all steps of demand planning overrides model performed worse than the previous model, because of the heavy human overrides. Additionally, those AI enabled decisionmaking structures works better for low-turnover products than high-turnover ones. / by Ye Ma. / M. Eng. in Supply Chain Management / M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 57-64). / Managing intermittent demand is a challenging operation in many industries since this type of demand is difficult to forecast. This challenge makes it hard to estimate inventory levels and thus affects service levels. The purpose of this study is to examine the impact of multiple levels of data aggregation on forecasting intermittent demand, and subsequently, on inventory control performance. In particular, we propose a procedure that integrates lead-time and customer heterogeneity into the forecasting using temporal and cross-sectional aggregation. Using data from a real-world setting and simulation, our analysis revealed that when high service levels were important for the company operations, the forecasting approach using temporal aggregation that incorporates lead-time information yielded a higher level of inventory efficiency in terms of both the holding cost and the realized service level. It appeared that when forecasts using temporal aggregation were augmented with information about customer behavior, their purchase patterns might be a helpful consideration for enhancing inventory performance. These findings allow us to provide useful recommendations for improving the current forecasting procedure and inventory control to the sponsor company of this project. / by Ngan Ngoc Chau. / M. Eng. in Supply Chain Management / M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (page 47). / In a simple representation of a supply chain, products flow from suppliers to customers, and currency flows from customers to suppliers. The period it takes a supplier to satisfy a customer order is called lead time. The period it takes a customer to pay a supplier for product is called payment term. The question this thesis will answer is: Can payment terms be used to offset lead times? Three frameworks are developed in this thesis to quantify the payment term required to offset lead times: the Pipeline and Safety Stock Inventory Offset Framework, On Hand Inventory Offset Framework, and the On Hand Inventory and Ordering Cost Offset Framework. All three build upon the commonly used total cost equation. Empirical analysis of annual reports submitted to the United States Securities and Exchange Commission in 2019 observed a relationship between payment terms and lead times. This thesis makes two contributions to the supply chain literature. First, the total cost equation is updated to differentiate between components of lead time as well as incorporate payment terms. Second, the observation that there is a relationship between payment terms and lead times provides a starting point for future research.. / by Matthew J. Dale. / M. Eng. in Supply Chain Management / M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, May, 2020 / Cataloged from the official PDF of thesis. / Includes bibliographical references (pages 73-77). / The world is facing a grave plastic waste problem. It is not new that we hear about oceanic death and morbid landfills. Only 8% of all the plastic produced is recycled in the US. This grotesque situation has been worsened by the Chinese ban of plastic waste imports from the developed western nations as of 2018. In this research we assess the feasibility of a novel approach to using existing e-commerce reverse logistics channels to take back post-consumer plastic. We use product sales data to estimate the post-consumer plastic volume. We then, design a mixed integer linear programming (MILP) based optimization model to assess different take-back routes and calculate various operational costs. In addition to the optimization model we determine the feasibility of this process by considering cost offsets such as price of virgin plastics. After that, we conduct a scenario-based sensitivity analysis to understand systemic cost and overall profit. We used the results of these analyses to formulate the strategic recommendations for companies interested in promoting or implementing e-commerce-based recycling programs. Finally, we assess the greenhouse gas emissions and corresponding externality costs through this process and perform a qualitative assessment of the stakeholder networks vital to making such a system operational. In conclusion, our results suggest that in certain scenarios it is economically feasible to facilitate a take-back process for post-consumer plastic using existing e-commerce-based reverse logistics channels while maintaining minimal additional emissions in the process. / by Saikat Banerjee. / M. Eng. in Supply Chain Management / M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The keen research objective is to develop a quality system in the Supply Chain Management geared to the agribusiness sector. The Supply Chain Quality Management (SCQM) proposed for blueberry companies must improve their operational performance. This study includes micro and small agricultural enterprises located in the Cañete region of Peru, where thanks to the interviews and questionnaires were possible to determine the critical practices, which will be applied in the agribusiness sector. The literature review found to enrich the methodical procedures that are essential practices of the innovative SCQM model. The research shows that the critical practices to develop quality management in this sector are Customer Relationship Management, Supplier Relationship Management, Process Management, and Human Resources Management. This research turns out to be utterly innovative because the use of the recently proposed technique of Quality Management into the Supply Chain combined with the best practices will lead to operational improvement andother qualitative advantages.Finally, the knowledge embedded in SCQM applied to small blueberry companies in an emerging country can provide added value to management to increase the competitiveness of the agribusiness sector. / Revisión por pares

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 56-67). / In previous decades the postal sector experienced drastic changes. Liberalization and digitization resulted in a continuous mail market decline. Simultaneously the rise of Internet resulted in a booming e-commerce parcel delivery market. To cope with these ongoing market developments Postal Operators (POs) need to rigorously restructure their delivery networks frequently in order to reduce distribution cost. Moreover, POs are searching for synergy opportunities between the mail and parcel delivery network. A recent development in the postal sector is the use of light electric freight vehicles (LEFV) in urban and suburban areas as a sustainable and cheaper solution for last-mile delivery. Limited research has been performed regarding the impact of LEFV on distribution cost and network design. This thesis introduces a two echelon location routing model for POs to determine the optimal network configuration for mail and parcel delivery in order to minimize total distribution costs using LEFV in their vehicle portfolio. A mixed integer linear programming model (MILP) is proposed including a multi-depot VRP for the first tier and continuous approximation techniques (CA) for the second tier. Using a real-world application at the Dutch PO - PostNL - the impact of merging the mail and parcel network as well as the impact of introducing LEFV was established. Results suggest that adding LEFV to the vehicle fleet leads to a distribution cost saving of 3% in the separate mail and parcel network. LEFV are a worthy alternative to vans in dense city areas, due to their high speed on short distances and their maneuverability in city areas. While merging the parcel and mail network with the current vehicle fleet leads to a distribution cost reduction of 1%, the inclusion of LEFV in a merger scenario leads to a saving of 5%. Therefore, adding LEFV to the vehicle fleet enables POs to seize synergy opportunities between the distribution networks. / by Ronald Veldman. / M. Eng. in Supply Chain Management / M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 48-49). / During the past few years, blockchain technology has shown great potential to disrupt existing supply chain finance solutions, as it could increase the efficiency of invoice processing and provide a more transparent and secure transactions. However, the costs and benefits of implementing blockchain technology in supply chain finance for involved parties are still unclear, since research on the topic is scarce. This thesis explores the net value of implementing blockchain technology in supply chain finance arrangement by using cost-benefit analysis. A cost-benefit model and the operating processes of traditional and blockchain-based supply chain finance solutions are proposed and applied to a real-world case study. We prove that blockchain technology increases the total net benefit among involved parties participating in the supply chain finance arrangement as a result of improved efficiency of invoice processing. We also find that suppliers would benefit from blockchain-based supply chain finance if the benefit from the unlocked working capital outweighs the cost of the platform fee. Another finding is that the buyer does not benefit from the technology in terms of unlocked working capital. / by Patara Panuparb. / M. Eng. in Supply Chain Management / M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 86-95). / The U.S. energy portfolio is set to undergo drastic change in the coming decades. Policies to reduce emissions combined with growing demand for energy will test existing infrastructure. Large reserves of shale gas provide an attractive near-term solution to help states transition from coal-powered plants to cleaner fuel. Most commercial production growth in shale gas occurred in the early 2000's. Yet most of the natural gas pipeline system was constructed long before the "shale gas revolution". Almost half of all interstate transmission pipeline mileage is over 50 years old. In order to best utilize the country's natural gas reserves, the pipeline transportation network must respond to accommodate changing flow patterns. Consumption of natural gas often occurs far from production sites. This research seeks to identify states where the required inflow and outflow of natural gas may be constrained by pipeline capacity limitations. / A literature review of the natural gas production and consumption outlook reveals an expected steady growth in the industry until 2050. The current state of the system is then evaluated. A simple analysis is performed to determine the inflow and outflow transmission pipeline capacity utilization rates for each state. It is found that some states, namely Florida, California, and New England, are indeed at risk for natural gas shortages. It is further discovered that some states with access to reserves, namely Pennsylvania, may be limiting production due to insufficient outflow pipeline capacity. The pipeline approval process, managed by FERC is reviewed. The process is found to be inefficient at allocating new capacity where it is needed. Alternative solutions to address the supply risk were also considered. A literature review confirms that pipeline transportation has a much lower incidence of accidents per volume of natural gas moved than either rail or truck transportation. / The dangers of underground storage are also explained. A major risk in the current pipeline system, age, is investigated using a simple analysis of PHMSA data. It is found that three of the top four causes of accidents are related to pipeline age, with older lines having higher rates of accidents. Lastly, the risks of over-reliance on natural-gas for electricity generation are discussed and it is recommended that states take a more balanced long-term approach to energy development and incorporate locally accessible renewable energy. / by Lauren E. Sittler / M. Eng. in Supply Chain Management / M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 72-75). / The rapid growth in e-commerce volumes, coupled with customer expectations of faster, flexible and cheaper parcel deliveries is increasing the pressure on retailers to design the most efficient delivery network. Collection-and-delivery points (CDPs) allow for the aggregation of demand and enable reductions in travel time and costs. CDPs also help minimize additional tours arising due to failed deliveries or failed pickups for returns. We formulate an optimization model that integrates CDPs in the design of the overall distribution network, including the location of upstream transshipment facilities. The model accounts for changes in demand density due to the placement of CDPs. It considers demand aggregation at the CDP for both forward and return flows, and the impact of failed deliveries and failed return pickups on the routing cost. The model considers multiple different route options and solves them using extended routing cost approximation formulae thus allowing the implementation of the model on large-scale problems. We then apply the model to solve a real-world case study on the last-mile distribution network of a major Brazilian e-commerce retailer. The results demonstrate that failed deliveries and failed return pickups increase both the last-mile cost and the overall cost of distribution, and CDPs effectively reduce these costs by aggregating the demand and minimizing travel time. / by Himanshu Rautela. / M. Eng. in Supply Chain Management / M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program