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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