Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 46-49). / Many businesses struggle to optimize the flow of inventory and finished goods through existing plants and facilities. The integration of inventory costs, organizational processes, and changing business dynamics make it difficult to determine the optimal flow. This thesis examines the flow of raw materials and finished goods through the supply chain of a multi-national oilfield services company. We study a centralized inventory approach, assessed through heuristics, against the existing decentralized approach. Sensitivity analysis with regard to service level, and mode of transport strengthened the analysis. We show that demand aggregation and lead time are important factors in determining the upper echelon for a company's internal distribution model. Potential safety stock reduction is 2%, which is mainly due to the improved coordination for materials flowing to the final echelon in the supply chain. However, pipeline inventory increases by 12% as a result of longer lead times. / by Patrick Scott and Boxi Xu. / M. Eng. in Supply Chain Management

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018. / Cataloged from PDF version of thesis. "June 2018." / Includes bibliographical references (pages 79-80). / A set of contracts guides every movement of cargo from one point to another. In this thesis, we focus on the contract between the charterer and the ship-owner in the liquid bulk ocean-shipping market. The contracting process begins with the two parties finding each other suitable and ends with one party being compensated in compliance with the terms and conditions of the contract for meeting a set of considerations. The question we answer is how emerging technologies, primarily Blockchain, can be used to make this process more efficient in terms of time and cost. Our research shows that while there are a considerable cost and time savings possible for certain aspects of the contracting process, there are some problem areas, such as the negotiations, that cannot be solved with the help of existing technology. We also conclude that the proposed solution needs to offer an end-to-end contract and document management tool rather than just being an improvement for one particular step in the process. An industry-wide consortium led Blockchain-based solution has potential to find wide acceptability and impact in terms of increased efficiency. / by Harshvardhan. / M. Eng. in Supply Chain Management

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (page 58). / Due to the long lead times and complexity in drug development and approval processes, pharmaceutical companies use long range planning to plan their production for the next 10 years. Capacity planning is largely driven by the long-term demand and its forecast uncertainty. The impact of uncertainties at manufacturing level, such as factory productivity and production success rate, are not entirely taken into account since only the average values of each manufacturing parameter are used. Can we better allocate production among manufacturing facilities when both demand and manufacturing uncertainties are considered? In this thesis a stochastic optimization approach is followed to minimize the deviation from target capacity limit under different manufacturing and demand scenarios. The mixed integer linear model incorporates the impact of demand and manufacturing variation on production allocation among manufacturing facilities through Monte Carlo generated scenarios. The thesis model is designed in a way that can be used as a decision tool to perform robust capacity planning at the strategic level. / by Sifo Luo. / M. Eng. in Supply Chain Management

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 66-70). / With the increased application of cognitive computing across the spectrum of industries, companies strive to ready their people and machines for future system change. Based on resource constraints, business needs, and the speed of change, many companies may opt for system augmentation rather than the adoption of entirely new systems. At the same time, changes in technology are increasing at paces never before realized. Against this backdrop, human actors and machines are working together interactively in new and increasing ways. Further, recent business model innovations, particularly in the retail space, have cast focus on logistics execution as a potential major competitive advantage. In this context, we considered the conceptual question of how best to iteratively improve a logistics planning system, which is composed of both human and machine actors, to reduce transportation and labor costs and increase the ability of the organization to think and act strategically. / In order to front these current technological realities - the need to stage for agent based systems and cognitive computing, the likelihood of system retrofit over rebuild, the ever increasing rate of change, and the rapid intertwining of human and machine roles - we proposed using human-machine interaction (HMI) design paradigms to retrofit an existing loosely coupled human-machine planning system. While HMI principles are normally applied to tightly coupled systems such as jet airplanes, the HMI architectural design applied novelly in this case showed significant application to an existing loosely coupled planning system. In addition to meeting the realities of today's competitive landscape, the developed HMI framework is tailored to a retrofit situation and also meets resiliency considerations. That novel conceptual proposal of HMI frameworks to an existing loosely coupled joint cognitive planning system shows tremendous promise to address these imminent realities. / With regards to the particular freight planning system considered, 71% of manual interventions were caused by the wrong sourcing facility being assigned to supply pallets to a customer. The remaining intervention causes were carrier changes 18%, customer restrictions 9%, and one change prompted by a data discrepancy. Further, at a conceptual level, the application of HMI frameworks to an existing freight planning system was effective at isolating data and alignment incongruences, displayed lower communication costs than recurrent system rework processes, and tethered well with system resiliency factors. / by John Bishop Ravenel. / 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 52-53). / While festivals bring a reason to cheer for everyone, businesses dealing with a spike in demand for perishables may have to live with the misery of lost sales and/or expired items. In the case of the dairy industry that deals with liquid milk, both raw material, and finished goods are perishable, which implies that merely stockpiling inventory of either item, without paying attention to potential inventory losses, cannot be an optimal strategy. In developing countries, the supplier base for perishables like milk, fruits, vegetables, flowers, etc. mostly comprise of small farmers instead of corporate/professional agencies, thus leading to supply variability. During special occasions like festivals, as individuals set aside more of the raw material for their own consumption, we encounter a reduction in supply. Around the same time, we notice a spike in customer demand, leading to a demand-supply mismatch. Companies dealing with perishables need an analytical approach to manage this. / In this thesis, we present a framework to address this problem of intermittent demand-supply mismatch using a 3-stage stochastic optimization model. We decide on the sourcing targets, the production plans based on supply realized, and finally, the dispatch plan based on orders received. As a case study, we analyze the operations and data from a private dairy company in eastern India, to understand the research problem and the applicability of the resulting model. We notice the impact of demand spikes and supply reduction in two areas: we increase supply targets in the periods preceding the demand spike; and we increase supply targets in periods when supply is expected to decrease, while demand is as usual. When there are multiple festival days within the time series, the compounding of impact depends on the sequencing of the events. / Finally, when we introduce the realistic constraint that the supply target needs to be constant throughout the time series, we see a degradation in the profitability, as we need to tradeoff between lost sales and wasted products. While the focus of this case study is the dairy industry, the conclusions from this research are broadly applicable to other industries dealing with perishables. / by Vishwanathan Parameshwaran lyer Nurani. / 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 50-51). / The success of e-commerce continues to push the bounds of delivery services as customers expect near instant fulfillment at little additional cost. This demand for delivery performance and operational cost efficiency has led to the exploration of the last-mile delivery problem using creative multimodal delivery systems. One promising system consists of a truck that can carry and deploy multiple autonomous drones to assist in the fulfillment of customer demand. The contribution of this thesis is towards furthering the understanding of the application of autonomous flying drones in such a system and improve parcel delivery performance within the constraint of the current state of technology. This thesis explores the feasibility of deploying drones in last-mile delivery by modeling and then optimizing the cost of serving customers with a system consisting of one truck and multiple drones under multiple customer demand scenarios. While this optimization problem can be solved with mixed integer linear programming (MILP), the computation requirement is such that MILP is inefficient for real world scenarios with 100 or more customers. This research applies metaheuristic methodology to solve the truck-and-drone problem for scenarios with up to 158 customers in approximately 30 minutes of computation time. The test results confirm an average of 7% to 9% in savings opportunity for a 2-drone baseline over traditional single truck delivery tours. This savings opportunity is shown to vary with customer density, number of drones carried, range of drone flight, and speed of drone relative to speed of truck. / by Yue Kuang. / 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 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