https://hdl.handle.net/1721.1/128235 2026-04-03T06:38:16Z https://hdl.handle.net/1721.1/159031 The State of Supply Chain Sustainability in Brazil Gonzaga Moreira Sá C Faveret, Leonardo; Ikaro Carvalho Mesquita Braga, Marcelo; Junqueira Nogueira, Rodrigo As sustainability gains importance for consumers, employees, and investors, Supply Chain Sustainability (SCS) has become an increasingly important topic for companies. The State of Supply Chain Sustainability report, a co-presentation of the MIT Center for Transportation & Logistics and the Council of Supply Chain Management Professionals, provides a clear snapshot of this subject worldwide. Although it has been increasing its range of respondents, including Spanish and Mandarin Chinese translated surveys and the original English survey, Portuguese-speaking countries have not been fully reached. This project aims to understand the state of supply chain sustainability in Brazil, the largest country in Latin America, regarding area, population, and GDP. The State of Supply Chain Sustainability 2022 survey questions were translated to Brazilian Portuguese and applied in a local survey in Brazil with specific questions to capture particularities, such as the impact of regional tax benefits in supply chain-related decision-making. Advanced statistical models were applied to guarantee the quality of the translations and compare the local results with the past results of other countries. 2025-04-02T00:00:00Z https://hdl.handle.net/1721.1/159030 Buy, Rent and Sale: Chasing better cash flows Do Couto Selem, Ana Patricia; Oyarzun Rodriguez, Juan Marcelo; Monsalve Uribe, Ricardo Leon This capstone project optimizes the inventory levels of a rental car company and improves the cash-to-cash cycle. The solution approach is a Mixed Integer Linear Program (MILP) model, considering a multiple-period inventory. The model provides purchasing and selling plans and cash and vehicle flow in the system for each quarter and each type of vehicle for five years. The analytical model was created to maximize the company’s gross margin, considering revenues from renting and sales, cost buy, opportunity cost, and general cost (maintenance, holding, and others). Moreover, it considers an initial inventory and helps the company manage these assets in the best way possible to meet the demand. The result shows an optimal solution of 3.3 billion Colombian pesos (COP) for five years in the base case scenario. Afterward, a sensitivity analysis for different perspectives related to renting period, budget, depreciation rate, and exchange rate impact was carried out. From that perspective, it is possible to understand that the primary trigger to create revenue is extending the renting period. Moreover, the sponsor company can interpret how factors in the market affect the total result success and create an action plan to anticipate these risks. 2025-04-02T00:00:00Z https://hdl.handle.net/1721.1/159029 Minimizing last-mile emissions through altitude-aware route optimization De Abreu Rodrigues, Gustavo; Jimenez Ruan, Gustavo; Amores, Jenny Carolina This study introduced an exact optimization approach to solve a new special type of Travelling Salesperson Problem. This problem considers time-windows restrictions and a new objective function—the objective regards driver assessment awareness and fuel consumption. The latter is modeled as a function of variable vehicle payload and terrain elevation data. This problem can be stated as the way to find the best route to service a set of customer demands, attempting to deliver within agreed time windows, mimicking paths that are as similar as possible to good routes executed in the past by experienced drivers, allowing small alterations as to reduce duration and fuel consumption. The authors proposed an innovative mixed integer linear programming formulation and a cluster decomposition approach that reduces search space and makes the approach applicable to solving real-world-sized problems. This model was parametrized using a small-sized mockup dataset and had its applicability tested on real data. The latter consisted of a public dataset containing trips executed and evaluated by real drivers of Amazon company. The results show that it was possible to reduce in -5.7% the fuel consumption in the routes of this dataset. Since this variable is directly related to emissions and pollution, this result shows that the suggested approach offers promising prospects for improving efficiency and reducing the carbon footprint of logistics last-mile operations. To the best of the authors' knowledge, this study contributes to the literature in that it is the first to jointly tackle driver assessment awareness and fuel consumption in a route optimization problem. Thus, it is also the first to propose a mathematical formulation and solution approach for this problem. 2025-04-02T00:00:00Z https://hdl.handle.net/1721.1/159028 Reduction of Costs and Emissions in Outbound Transport Amazonas Machado, Leonardo; Chavelas Manzo, Ricardo; Silva Tourinho Nakamura, Rodrigo The global food system accounts for nearly 30% of the total CO2 emissions worldwide. About 19% of that figure is due to transportation-related emissions. The main problem being addressed in this project is to identify the main drivers of CO2 emissions in outbound transportation for a major CPG food company in Antioquia, Colombia, which has declared sustainability as a major driver in their corporate strategy. By indirectly measuring CO2 emissions, a better understanding of the main drivers of emissions can be acquired. The cause-effect relationships on the distribution performance in emissions and cost to serve are in place. A comprehensive literature review of the state-of-the-art methodologies and techniques to assess CO2 emissions is part of this project, as well as a qualitative evaluation of the challenges of Antioquia’s topography. Two different methodologies have been used throughout this document to estimate CO2 emissions. A fuel-based approach and a distance-weight-based approach use CO2 equivalent units to estimate emissions at different levels of aggregation. Quantitative and spatial analysis allows us to conclude that regions that are harder to reach (low-volume municipalities located in hilly areas, irrespective of distance traveled) have a higher cost to serve and higher emissions due to an increase in transportation costs, fuel usage, difficulties to consolidate cargo and difficulties to increase vehicle usage due to the low volume sales. 2025-04-02T00:00:00Z https://hdl.handle.net/1721.1/159027 Reducing food losses by improving the efficiency of the banana supply chain in the Antioquia corridor in Colombia Sethuramanujam, Ananthakumar; Fernandez Cedi, Laura Natalia In 2011, the Food and Agricultural Organization of the United Nations (FAO) estimated that one-third of the food produced in the world for human consumption was lost or wasted (FAO, 2021d). Ten years later, a World Wildlife Fund (WWF) study calculated the percentage of food destined for consumption wasted along the entire chain. It reached 1.2 billion tons of food lost on farms and 931 million tons wasted at the retail, food service, and household levels, which accounts for around 40% (WWF-UK, 2021). All this wasted food could feed more than double the number of undernourished people worldwide, estimated to be between 720 and 811 million in 2020 (see figure 1) (FAO, 2021e; World Food Programme, 2020). 2025-04-02T00:00:00Z https://hdl.handle.net/1721.1/159026 Determining optimal inventory positions in an urban network Briceño Tipacti, Juan Pablo; Rocha Camargo, Gabriel Supply chain networks are becoming increasingly complex due to the aggressive growth of multiple digital trends, like the rise of e-commerce and the increased customer expectations, which have been enhanced through the pandemic over the last few years. Therefore, this study proposes a model to develop an inventory optimization strategy for a multi-tier supply chain case study in the US market, considering the supply and demand variability for local and international distribution. First, different approaches from the theoretical perspective are analyzed, from traditional inventory management to the new end-to-end perspectives. After that, details of the methodology will be explained, considering the statistical benefits of demand pooling. Finally, real numbers from a case study are applied to the methodology to measure the solution's impact, followed by the conclusions found from the study. 2025-04-02T00:00:00Z https://hdl.handle.net/1721.1/159025 Encourage circular practices in the supply chain Goitia Polo, Alejandro Jorge; Perez Dovalo, Juan Manuel Every year 300 million tons of plastic waste are produced, and the amount of plastic produced increases with the world population. The more people there are on the planet, the more waste is produced. The concepts of circular economy are gaining popularity. Companies are looking to implement circular strategies to maximize the use of materials, reduce waste and help the environment while improving their corporate image. Since the coronavirus pandemic, digital transformation has progressed faster and faster, which has boosted digital communication. Social networks began to play a fundamental role in communication since they are an efficient means of interacting with people worldwide in real-time. Due to social networks' social impact, they can be used to influence people's decision-making. This study aims to develop a model that encourages people to adopt recycling habits for polyethylene terephthalate (PET) bottles through social networks focused on the population of the United States. This study will use analytics tools such as the Bass Diffusion Model, and an economic analysis of the viability will be carried out to implement the proposed strategies. 2025-04-02T00:00:00Z https://hdl.handle.net/1721.1/159024 Causal inference improving warehouse productivity: zoned storage and killer items Montemurri, David; Herrera, Hebe Adriana; Ghiglione, Maria Florencia E-commerce companies need help with customer service experience: faster and more frequent deliveries. Then, order fulfillment becomes critical to establish a competitive advantage. The main objective of this project is to determine whether a new key product assortment, called a class-based scattered storage policy, improves order-picking operations in one of the main warehouses of the sponsor company. This e-commerce firm operates in an emerging market. As mentioned earlier, this project addresses the problem by running an A/B quasi-experiment in the warehouse, showing findings directly from a real context for the first time. For this purpose, the warehouse was split into control and treatment sections during peak season when speed is most required. The effect of the proposed storage policy is studied by comparing picking productivity through a two-sample t-test. The samples are chosen using the Coarsened Exact Matching algorithm to have similar data to analyze in observable characteristics. The result of this work indicates that the class-based scattered storage policy does not lead to an improvement in picking productivity. It can be attributed to real-context features that are presented and discussed. Additionally, strong recommendations are given to include the findings in future research. 2025-04-02T00:00:00Z https://hdl.handle.net/1721.1/159023 Demand Forecasting Analysis for Pharma Retail Moreno Quintero, Nestor Andres; Martins de Brito Sousa, Mariana; Flores Trujillo, Waldo Mauricio Gabriel Demand planning is the connection between marketing, finance, and operations. In an industry like pharma retail, products do not always behave according to a regular stable baseline. In addition, marketing enrichment like promotions or price fluctuations and the impactof government regulations and patient base characteristics increase operational complexity. Moreover, more than thirty percent of changes in the forecast from one cycle to another can lead to overstock or out-of-stock due to the high production and delivery lead times. The purpose of this project is to find a proper demand forecasting model for a selected group of stock-keeping units to improve supply processes of the most important stores of the sponsoring company, leading to further benefits such as budget purposes as a top-down analysis. Data analysis is needed for trends, seasonality, stockouts, and demand stability. Followed by the application of various forecasting models, including Machine Learning algorithms, this project provides a comparison of models to define the best baseline as a tool for the planning area to enrich to improve operational KPIs. 2025-04-02T00:00:00Z https://hdl.handle.net/1721.1/159022 Fulfillment models framework for e-commerce companies Bellido, Juan Manuel; Cabrini Souza e Silva, Renata; Gomez de la Luz, Dominique E-commerce relevance is increasing, and companies should be prepared to fulfill customers’ expectations and ensure an optimal shopping experience. Online worldwide retail sales generated 70 billion U.S. dollars in 2019, being Mexico and Brazil the main leaders for this type of channel in LATAM (Chevalier, 2020). With the objective of being more efficient and differentiate from competitors, it is vital to have an extremely consistent and aligned supply chain that follows the company's business strategy. To achieve this new challenge, the following study aims to generate a framework decision matrix, enabling companies to support decisions of introducing fresh, dry, refrigerated, and frozen product categories based on five major warehousing trends: distribution center, fulfillment center, dark-store, micro-fulfillment center and crowdsourced warehousing solutions. To develop this project a systematic literature review combining case studies, papers, research articles and experts’ validation will be implemented with the objective of establishing a framework that can be used to ensure strategies for the e-commerce retailers, thus they are able to serve and meet customer expectations regarding product quality, optimal price, and delivery time. 2025-04-02T00:00:00Z