https://hdl.handle.net/1721.1/107234 Fri, 03 Apr 2026 21:06:07 GMT 2026-04-03T21:06:07Z 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. Wed, 02 Apr 2025 00:00:00 GMT https://hdl.handle.net/1721.1/159031 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. Wed, 02 Apr 2025 00:00:00 GMT https://hdl.handle.net/1721.1/159030 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. Wed, 02 Apr 2025 00:00:00 GMT https://hdl.handle.net/1721.1/159029 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. Wed, 02 Apr 2025 00:00:00 GMT https://hdl.handle.net/1721.1/159028 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). Wed, 02 Apr 2025 00:00:00 GMT https://hdl.handle.net/1721.1/159027 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. Wed, 02 Apr 2025 00:00:00 GMT https://hdl.handle.net/1721.1/159026 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. Wed, 02 Apr 2025 00:00:00 GMT https://hdl.handle.net/1721.1/159025 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. Wed, 02 Apr 2025 00:00:00 GMT https://hdl.handle.net/1721.1/159024 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. Wed, 02 Apr 2025 00:00:00 GMT https://hdl.handle.net/1721.1/159023 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. Wed, 02 Apr 2025 00:00:00 GMT https://hdl.handle.net/1721.1/159022 2025-04-02T00:00:00Z https://hdl.handle.net/1721.1/152070 End-of-Life Inventory Optimization during Runout Events for a Manufacturing Automotive Company Calero Mantilla, Francisco Andres; Esposito, Andrea Effective and efficient inventory management is today more crucial than ever. Three factors drive its importance: (i) the ever-increasing complexity of bills of materials, causing firms to dedicate a large part of their spending to their supply base; (ii) unprecedented uncertainty, making inventory indispensable for business survival; and (iii) an emerging inflationary economy, which multiplies the cost of holding inventory. One of the most critical inventory management processes is the runout phase, which refers to stock depletion as products approach their end of life. Operational excellence during the runout phase is particularly important not only due to the negative externalities of holding excessive stock but also to the costly risk of scrapping the remaining inventory if materials become obsolete. In this capstone project, we propose an end-to-end approach to improve runout inventory management. This approach aims to guide companies in defining the problem of runout inventory management, preparing the raw data, choosing the right models, setting up the analysis, and interpreting its results. We illustrate each step of our approach by using a real-world case study from a large automotive manufacturer. Using historical data from one of their largest factories in the United States, we trained and tested several predictive models to estimate the demand during the last month of the lifecycle of materials three months in advance, when the last replenishment orders are issued. Based on our analysis, we provide several recommendations to successfully improve the company’s demand forecasting and runout inventory management processes. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152070 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152069 What makes beauty more beautiful? Botanical Supply Chain Network Optimization Choe, Yeonjoon; Chen, Kefan The beauty and personal care industry has undergone significant changes in recent years, with consumers becoming increasingly concerned about the ingredients used in their skincare products. There has been a growing preference for natural and organic ingredients, with many consumers looking to avoid products that contain harsh chemicals or synthetic ingredients. As a result, many consumer product goods (CPG) manufacturers, including Company A, have begun to develop new product lines that contain natural botanical ingredients. For our Capstone, Company A is looking for ways to evaluate and upgrade its procurement and logistics operations for natural botanical ingredients, with the ultimate goal of designing an efficient and eco-friendly supply chain. To achieve this goal, the Capstone project focused on two key areas: supplier optimization and route optimization. The first objective was to assess the current supply chain network to provide visibility on its performance and identify areas for improvement. The second objective was to formulate recommendations for redesigning the current supply chain network with the goal of decreasing travel distances and reducing environmental impact. To address these objectives, the project utilized various analytical tools, including Excel, Power BI, and Python. The team analyzed Company A's data and characterized current operations using various statistical analyses. The team also used a network design model based on the transshipment problem to connect supply, transfer, and demand sites. Finally, the project used an environmental impact evaluation feature to estimate the carbon footprint of each route in a given network. In summary, the project focused on two key areas: supplier optimization and route optimization. Using various analytical tools, the project identified areas for improvement and formulated recommendations for redesigning the current supply chain network to reduce travel distances and environmental impact. Ultimately, the project provides clear strategic directions for improving the supply chain network for natural ingredients in Company A's beauty business. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152069 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152068 Sustainable Network Design of Perishable Foods Tartaglia, Maria Anna; Wang, Yin Miki Our project analyzes the trade-offs between costs, service level, inventory strategy, and CO2 emissions in a global food and beverage retailer's current network design. We analyze the network design of a perishable product and evaluate which levers or variables can change (transportation mode, suppliers’ locations, inventory) in their existing network, as well as what variables are constraints and cannot be changed (truck sizes, product specifications, DC locations). We explore how full truckload vs. less-than- truckload transportation impacts their network and consider new supplier locations. We create a network design model in Python that offers the sponsor company various solutions that highlight the trade-offs between costs, service level, and CO2 emissions. Key insights of this project are the following: (1) the company should utilize LTL transportation; (2) there is a cost benefit of adding a third supplier to the network design; (3) if the company wants to achieve a higher service level, the total costs of inventory, transportation, and COGS will be higher in comparison to the total costs of a lower service level; and (4) for each service level, as the cost decreases the total emissions (kg CO2) per week and expected number of expired items also increases. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152068 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152067 Reimagining Procurement: Differentiated vs Standardized Services Jaffar, Hassaan; Meleney, Melania Our sponsor company is a leading player in the healthcare market. Its procurement organization is split into three divisions: corporate, business unit, and global services. The procurement strategy is set by corporate procurement, and then business unit and global services perform the procurement execution, and measure their procurement through metrics in experience, efficiency, and effectiveness. Our main question was whether the policies set by corporate procurement, such as supplier segmentation, are beneficial to those metrics in procurement execution, and whether the procurement process could be re-engineered to improve those metrics. We conducted an analysis of transaction data, created a Value Stream Map, and utilized process mining technology to assess the current process. We then used the principles of process re-engineering to redesign the end-to-end process. We concluded that the policies set by corporate procurement were not beneficial to procurement execution metrics, as the most effort was being placed on the least important segment of suppliers. We created a re- engineered process, which promotes technology such as machine learning, which could lead to improved procurement process metrics. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152067 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152066 Transforming Warehouses Towards a Sustainable Future Alhasan, Osama; Lobanov, Kirill Climate change is a global problem, and CO2 emissions are the primary cause of rising temperatures. Many companies, including our capstone sponsor Maersk, have committed to reaching net zero emissions by setting decarbonization targets. In this project, our goal was to identify specific actions that could be taken at the warehouse level to reduce greenhouse gas emissions and help companies achieve their decarbonization goals. Our approach involved identifying sources of emissions, shortlisting technologies that could be adapted based on their applicability to the operations of Maersk warehousing and distribution subsidiary companies in the United States and developing a methodology to evaluate each solution's key performance indicators: payback and environmental impact. The result was a set of recommendations prioritizing each solution's implementation and scaling to other facilities. Our analysis revealed that some solutions, such as solar energy, could reduce Scope 2 carbon emissions by 100% due to eliminating supply of electricity from the grid while also decreasing electricity costs by 39%. Finally, to evaluate the different sustainable solutions in an integrated way, we developed a framework that identifies the key factors and patterns affecting the attractiveness and further implementation of each solution. Our findings suggest that combining initiatives such as the electrification of moving assets with renewable-energy generation systems can significantly improve the payback period further, reducing it by almost 9%. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152066 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152065 Optimization of Cost and Carbon Emissions in a Multi-Echelon Distribution Network Benhassine, Amina; Shan, Boping Over the past decade, the oilfield services industry has experienced two major trends: the drive to reduce costs and the push for sustainability. In this context, our sponsor company seeks to optimize the distribution of materials and supplies in their global network, while considering both distribution costs and greenhouse gas (GHG) emissions. Our project has three objectives. The first is to develop an optimal transportation plan for materials and supplies – through the network of suppliers, manufacturing centers, distribution centers, and field warehouses – simultaneously minimizing distribution costs and GHG emissions. The second objective is to estimate the potential cost and GHG emissions reductions the company could achieve by bypassing the manufacturing centers for the eligible parts. Finally, our project aims to provide a deep understanding of the trade-offs between distribution costs, GHG emissions, and lead time. To achieve these objectives, we built a Mixed-Integer Linear Programming model that minimizes distribution and GHG emissions costs under demand and maximum lead time constraints. Our model provides an optimal transportation plan that recommends the quantity and mode of transport throughout the echelons of the network for all parts in scope. Our results show that bypassing the manufacturing centers could lead to a 3.7% reduction in distribution costs and a 1.7% reduction in GHG emissions. Moreover, our results show that most of the distribution cost reduction is due to the reduction in duties and that a small number of parts accounts for most of the cost savings. Finally, by varying the weight assigned to the distribution cost and to the GHG emissions cost in the objective function, we demonstrate that the company can achieve quick wins in emissions reduction. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152065 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152064 Predicting Food Bank Demand: A Socioeconomic Analysis and Forecasting Model Investigation Rakestraw, Kaitlyn Danielle Lee Food insecurity is a problem that affects people in every county within the United States. To combat this issue, many organizations across the country provide charitable food assistance to their communities. The demand for these services is variable, and many of these organizations do not have a consistent method of predicting future demand. This research explores the demand for one food bank, the Mid-Ohio Food Collective (MOFC), and analyzes how this demand differs based on the socioeconomic vulnerability of an area. Additionally, a variety of forecasting models are tested to determine which can best predict demand for the MOFC’s services, and the implications of improved forecast accuracy are investigated. This study suggests a framework for categorizing United States counties into two distinct groups, namely "more vulnerable" and "less vulnerable," utilizing socioeconomic factors. When applied to the case study of the MOFC, this classification allowed for identification of differences in demand patterns between the two clusters. Through an evaluation of the RMSE, MAE, and MAPE of nine time series forecasting models, it was found that a naive forecasting model performs well in forecasting demand for the more vulnerable counties in the MOFC’s service area. However, it was found that switching from a naive forecasting model to an exponential smoothing model with level and trend components can significantly improve demand forecasting accuracy for the less vulnerable counties. By switching to an exponential smoothing model with level and trend components for the less vulnerable cluster, the MOFC can improve their forecasting accuracy from a 9.9% MAPE using the naïve model to a 4.3%. The factors utilized in this study are relevant and applicable to all counties in the United States. As a result, the insights gained from this research can be effectively employed by food banks throughout the country, enabling them to improve the accuracy of their demand forecasts. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152064 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152063 Transforming Micro-Retailing in Emerging Markets Eustis, Emma Nicole; Sonnenberg, Hannah Justine Nanostores, small family-owned businesses, are a critical component of the Mexican economy, providing employment and acting as major customers for consumer-packaged goods companies. Our capstone paper presents a study of innovative business models aiming to help nanostores survive and grow, at a time when the Mexican economy is projected to expand. The study includes field research from over 4,000 nanostore owners and consumers in Mexico and explores innovative business models worldwide to identify potential solutions. We employed a disciplined entrepreneurship process to narrow down six models, which were then workshopped in Mexico with suppliers, consumers, and nanostore owners to assess viability and gather additional insights. The study emphasizes the importance of gathering direct feedback to ensure solutions align with the expectations of nanostore owners and truly help them survive. We are confident that our six models will help the nanostores thrive in the face of potential disruptions. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152063 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152062 Profit-Driven Network Redesign Through Value-Creation Services DeHaan, Morgan Jessica; Ke, Yujia Design of supply chain networks is a key strategic decision in supply chain management. Our capstone sponsor Armada is a supply chain service provider to restaurant chains across the United States. The company is envisioning a redesign of their current service network based on two components: (1) the addition of new distribution centers (referred to as iDCs), located closer to high volume service areas, (2) the deployment of value-creating services from the iDCs. In this capstone, we develop a series of models to support these decisions. First, we build a model that identifies demand dense areas as “urban clusters” where an iDC would best serve Armada’s clients. Second, we develop a model that minimizes costs in the network while maximizing revenues through the defined additional services. The results of these models provide us with two distinct network configurations based on cost-minimization and profit-maximization: one placing iDCs by key demand-dense areas and the other favoring high revenue generating regions. This study shows that layering revenue-maximization methodology with cost-minimization algorithms in mixed integer linear programming will alter results and favor the highest revenue generation location(s). Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152062 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152061 Procurement Control Tower: Proof of Concept through Machine Learning and Natural Language Processing Kumar, Bishwajit; Barros Gómez, Pablo Andrés An organization’s procurement process is pivotal for its success in a competitive market. The increased uncertainty and complexity of post-pandemic supply chains have made procurement a more valuable focus point among organizations and a differentiating factor to achieve a competitive advantage. The sponsor company of this study believes that the key to being competitive in today’s VUCA (volatile, uncertain, complex, and ambiguous) market relies on getting faster insights into the problem areas, having enhanced decision-making capabilities, and optimal exception management. For that reason, it seeks to understand if said competencies are encapsulated within a Procurement Control Tower’s value proposition. To meet our sponsor’s requirements, we divided our research into two components, a qualitative and a quantitative component. The first evaluates and defines the scope, value proposition, and deployment strategy of the Procurement Control Tower. The latter provides proof of concept by creating a working prototype of one of its use cases. The selected use case for the prototype is Spend Analytics, more specifically, the categorization and sub-categorization of the unclassified spend data for an assigned business unit. To create the prototype, this study compares multiple Machine Learning algorithms and selects Random Forest as the best-performing one in terms of accuracy. The algorithm’s predictive power is then enhanced by pre-processing the data with Natural Language Processing. The final model performs with 94% accuracy at a category level and 90% at a sub-category level. This study's primary finding, obtained through the categorization of approximately 250 million USD of unclassified spend data, is that implementing the Procurement Control Tower in the sponsor's business provides measurable value. For our assigned business unit, it creates renegotiation opportunities with suppliers, increases budgeting accuracy, and reduces the man-hours required. The final algorithm of the prototype has been presented to the sponsor company, which is currently deploying it for the assigned business unit. To scale up the benefits of the solution across the organization, the sponsor plans to deploy it for the remaining business units. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152061 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152060 The Green Route: An Analysis of Mode Change as a Strategy for Carbon Emission Reduction Bruttomesso, Elizabeth Marie; Pant, Shruti Having recognized the growing need for decarbonizing the maritime transport industry, the sponsor company is assessing the economic feasibility of implementing Green Intermodal Corridors. For a route to be considered a viable option as a Green Corridor, it must have the potential for significant decarbonization while also being economically implementable. This paper covers the feasibility assessment for three corridors, exploring the different scenarios possible in each of these routes and how they compare with the base case of current operations in terms of costs of operation and carbon emission reduction. The report discusses the scenario simulations over the alternate routes that deploy an electric barge and a hybrid barge in combination with an electric truck (Route 1), an electric truck (Route 2), and an electric train in combination with an electric truck (Route 3), in place of the regular diesel trucks for the delivery of cargo from the port to the warehouse. We created a model to show the trade-off between cost and emissions and to assess the best way for the company to make decisions on its options for future infrastructure and routing. The best and easiest transition for the company, as determined by the feasibility study, is using electric trucks in place of diesel trucks for inland delivery over Route 2. The successful implementation of viable green routes can be driven by utilizing government funds and incentives that can partially offset the high initial capital expenditure. Although the cost and emission figures, as ascertained in the report, do not make a strong case for green corridors in themselves, this transition can definitely be made possible by encouraging the transportation industry, the government, and all the stakeholders involved to make the necessary contributions to establish green routes with lower carbon emissions. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152060 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152059 Quantifying packaging material complexity to improve portfolio management Lara, Marcela Navarr; Lynch, Joseph Anthony Product portfolio complexity poses a significant challenge for many consumer packaged goods (CPG) manufacturers, resulting in higher costs, risks, and production time. This work aims to assist the sponsor company in managing and measuring its complexity and determining the financial impact of delisting complex SKUs. We used a four-phase methodology involving data collection and mapping, analytics, complexity analysis, and financial analysis to achieve this. The complexity analysis was applied to the primary and secondary packaging for a variety of product SKUs using the commonality index metric, which indicates the frequency of components present in an SKU. We then connected this metric with aggregated and granular financial metrics to identify the relationships between complexity and costs. Our results showed that SKUs with a low commonality index exhibit an average total cost 40.8% higher than those with a high commonality index. Additionally, our results found that SKUs with a low commonality index had a packaging materials cost that was 105% greater than SKUs with a high commonality index. Therefore, modifying specific SKU components with low commonality makes cost savings possible. We suggest using the commonality index and aggregated and granular financial metrics as a guideline for delisting and introducing new products to effectively manage and reduce supply chain complexity in the CPG industry. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152059 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152058 Unraveling the Relation between Trucking Modes: A Correlation Analysis between Less Than Truckload Metrics and Truckload Market Tension Moran, Sean; Tosi, Nicolo In 2021, The United States trucking industry generated over $400 billion in revenues. As the economy cycles through waves of contractions and expansions, the transportation industry moves through cycles of slack and tension. This research quantifies the relationship between TL market tension metrics and key LTL metrics on a national and corridor level. To evaluate the strength of the relationship between the variables we used Pearson's correlation. Our models spanned 6 years of data and used public national LTL carrier data as well private C.H. Robinson data. The research found a positive, statistically significant correlation for key truckload metrics, including from the contract market (route guide depth) and the spot market (cost per mile and load to truck ratio), especially when lagged 1-3 months. We find this relationship to be true for national public LTL carrier data as well as private C.H. Robinson data. However, because the cost per mile data is correlated with future LTL volume and LTL volume is correlated with future cost per mile, we believe route guide depth and load to truck ratio to be better bellwether indicators. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152058 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152057 Time Series Forecasting and Dynamic Pricing for Cloud Usage Ekanem, Donald Inyene This capstone explores different classical time series forecasting models to forecast cloud usage for an Infrastructure as a Service (IaaS) provider. The objective is to provide forecast information to help with capacity planning and propose a pricing model to optimize the capacity and manage revenue. The Mean Absolute Percentage Error (MAPE) performance criteria was compared for all candidate forecasting models to select the most suitable one. Analysis of the data showed a high linear trend in most of the zones, as well as a weekly seasonality. An elastic pricing model was proposed to influence customer demand behaviors to smoothen out capacity during the week. The conclusion is that the demand can be forecasted using a linear model with weekly seasonality. The determination of the most suitable forecasting model and prescribed elastic pricing model will help the sponsor company plan and manage capacity and revenue more optimally. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152057 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152056 Risk Mitigation to Increase Time to Survive Huang, Szuya; Szuma, Gabriel In our increasingly interconnected global economy, businesses confront heightened risks of supply chain disruptions. This capstone project, sponsored by Tempur Sealy International Inc., a leading manufacturer in the mattress industry, focuses on evaluating and enhancing supply chain resilience to mitigate these disruptions. The project introduces a unique methodology that combines Time to Survive (TTS) analysis and procurement optimization. This dual approach quantifies the cost of resilience measures, providing a tangible value to efforts often viewed as abstract or precautionary. In addition, this methodology supports key decision-making processes within the company, particularly in relation to storage capacity investments, inventory planning, and procurement strategy. By analyzing scenarios and potential disruptions, we offer valuable insights that not only highlight areas of potential risk but also suggest viable solutions for improvement. Our findings demonstrate that through the optimal allocation of resources and strategic procurement practices, Tempur Sealy can maintain its existing levels of supply chain resilience while also achieving cost savings. This balance is crucial in ensuring the company can effectively manage potential disruptions without compromising financial performance. Finally, we posit that the tools and methodology developed during this project have broader applications beyond the specific case presented. We believe these methods can be generalized and utilized by other companies across various industries. This approach enables businesses to justify investments into risk mitigation measures, providing a clear, quantifiable value to these often overlooked yet crucially important efforts. This study, therefore, contributes to the literature on supply chain management and resilience, offering practical tools and insights for businesses navigating the complexities of the global supply chain. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152056 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152055 Detect, Communicate, Collaborate: An agile digital network to manage disruptions Tewari, Prateek; Wei, Yusong This project sought to address critical gaps in the extended supply chain network of the sponsor company by evaluating the efficacy of establishing a communication protocol with upstream suppliers to detect and mitigate supply chain disruptions. Leveraging an agent-based simulation model, the study examined what supply chain elements should be activated, and what internal and external stakeholders should be tracked to facilitate effective communication during disruption. The simulation results demonstrate the model's robustness in various scenarios, achieving a significant reduction in the detection lead time of disruptions ranging from 12% to 53%. By implementing the Supply Chain Digital Risk Console, Sponsor Company can benefit from early detection of disruption events and timely communication with appropriate stakeholders, resulting in faster responses to potential disruptions, a reduction in the Value at Risk, and an improvement in the supplier On Time In Full (OTIF) order fulfillment rate, thereby improving company’s overall supply chain performance and resilience. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152055 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152054 Solutions for Preventing Trailer Theft Hawkes, Harry; Lim, Lydia Motor carriers with tight margins and cost constraints are under increasing pressure to improve utilization of their labor and assets. Many carriers face theft of their trailers that are exchanged between shippers, carriers, and contractors. Our project aims to answer the following question: How can trucking companies incorporate anti-theft measures to improve asset retention and therefore utilization of their trailer pools? Through our research and interviews with transportation professionals, we gained a better understanding of the complex industry relationships at play and identified potential areas of improvement to prevent trailer thefts. Our findings showed that there was no single solution that would be able to solve the trailer theft problem, and that cooperation between industry players would be difficult to achieve. However, carriers looking to reduce trailer thefts should implement a layered solution that encourages behavioral changes, expands physical deterrence, and improves process design. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152054 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152053 Impact analysis of packaging box composition on supply chain emissions (A case study with Dell Technologies) Dildabekov, Nauryzkhan; Rai, Ritesh This capstone project studies the methods to effectively calculate scope 3 emissions for the packaging boxes of Dell Technologies. The study emphasizes that all sustainability elements are interconnected, and trade-offs need to be considered to optimize overall sustainability in packaging. The model answers questions such as the relationship between material composition, and greenhouse gas emissions, how it can help teams make better decisions, and how greenhouse gas emissions can be controlled by changing packaging variables such as recycled content. The study also presents dashboards that calculate and analyze the total emissions under scope 3, category 1, and category 4 for different packaging types shared by Dell Technologies. The dashboard requires two inputs: a split of air vs. ocean mode of transportation and the packaging type. Our results show that weight, distance traveled, and material type are major factors contributing to emissions. Category 1 is affected by the weight and type of material, while Category 4 is affected by the weight and distance traveled. The study also suggests that Dell Technologies' control over category 1 emissions is limited by the nature of the supply chain, and changing material or suppliers for corrugated boxes is the only direct influence they have. The project presents insights into which component of the packaging generates the maximum emissions, how recycled content impacts emissions and the variation in emission factors by region. Ultimately, this project can help Dell scale this exercise across multiple use cases to achieve their sustainability goals. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152053 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152052 Empty Miles Reduction in the Downstream Network for a Consumer Goods Manufacturer Neversu, Sneha; Murugesan, Anumanth Sarma Logistics and transportation companies face a significant challenge with empty miles, which results in costs without generating revenue. To tackle this issue, we explored two potential strategies: one involves generating revenue by collaborating with external companies, while the other involves adopting backhauling strategies to identify potential loads within the network. To generate revenue, we considered utilizing the company’s private fleet as a third-party logistics (3PL) fleet for other organizations and analyzed the associated costs. To identify feasible backhaul opportunities, we used a heuristic method that involved creating all possible lane combinations for existing nodes and then filtering pickup options based on criteria such as drive time, distance, cost savings, and greenhouse gas emissions. In addition, we conducted a market sensitivity analysis to assess the robustness of our potential opportunity lanes. Our study revealed that there is a significant potential to increase revenue of about $24 million per year and enhance the company’s topline performance by utilizing the private fleet as a 3PL for other external companies. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152052 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152051 Designing a Resilient 3PL Network Snow, Charles E.; Tanaka, Yusuke Our project sponsor, a major third-party logistics provider in Japan, experienced a severe disruption that destroyed one of their primary distribution centers for a specific industry. This disruption led to increased lead times, degraded service levels, higher logistics costs, and the loss of a client. Consequently, our research focused on supply chain disruptions and resiliency. We aimed to answer three research questions: (1) what was the loss caused by the disruption? (2) how should the network be rebuilt to recover from the disruption? (3) how can resiliency be added to mitigate the risk of future disruptions? We addressed these questions by collecting real- world data, including data before, during, and after the disruption. We then developed mixed- integer linear programming models of the pre-disruption network and networks optimized with additional candidate distribution centers. Then a scenario-planning approach was employed to evaluate the costs and resiliency of these models. Our results revealed the loss caused by the disruption (7.4% cost increase), the estimated improvement of the company's disruption recovery plan (3.5% cost reduction), and the potential to achieve a more resilient network without additional costs. The results can be used not only to recover from the disruption but also to enhance the efficiency and resiliency of their logistics network. Furthermore, our research highlights the potential utilization of the developed network model for mitigating future risks and enabling contingency planning in the event of network disruptions. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152051 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152050 DO COMPANIES' ENVIRONMENTAL COMMITMENTS DIFFER ACCORDING TO THEIR SUPPLY CHAIN POSITION? del Valle y Rivera, Julia Fernandez; Vilar da Costa, Samara As concerns around climate change increase, companies have been more eager to adopt environmental sustainability goals. The focus of this research is to provide insights into how a company’s position in the overall supply chain impacts their decisions to set environmental sustainability goals and initiatives. In this report, we take both a quantitative and qualitative approach to highlight the sources of pressure that influence companies’ setting of net zero goals and how they differ depending on the company and industry type. The quantitative analyses applied use data from the 2023 Survey on Supply Chain Sustainability—an annual questionnaire commissioned by the MIT Center for Transportation and Logistics and the Council of Supply Chain Management Professionals. The qualitative analysis gathered key insights from supply chain executives through interviews. Our findings confirm that, while investors continue to be one of the key drivers for companies to address sustainability as part of the corporate strategy through net zero targets, there are other sources of pressure at play. Our results also show that companies present different behaviors regarding goal setting based on their position within the overall supply chain, with downstream players having the greatest levels of commitment via their net zero goals. However, we learned that when it came to near-term initiatives to reduce Scope 3 emissions in line with the net zero goals, downstream was no different than the upstream and midstream positions— they all show most companies are unprepared to meet their carbon neutrality targets. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152050 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152049 Resilience in Upstream Supply Network Khedr Elzanfaly, Mostafa Khedr Mohamed; Merino Sandoval, Gianmarco Alexander After the Covid-19 pandemic, organizations re-evaluated their supply chain strategies and began a race to build resilience in their networks. However, quantifying the level of resilience of any supply chain is a complex task, given the uncertainties associated with disruptions and the dynamics of global markets. This paper proposes a novel framework for quantifying supply chain resilience, with a focus on the upstream side of the network. Using Social Network Analysis (SNA) indicators and Business Impact concepts, we developed a methodology that captures the impact and robustness within the different tiers of suppliers. The framework also proposes resilience score metrics for comparing different sourcing strategies and network designs. Using a synthetic network, the framework suggests that enhancing flexibility and redundancy could bolster the resilience of other nodes in the supply chain by up to 50%. However, these strategies may also impact overall resilience and introduce criticality to certain nodes, limiting the overall resilience enhancement to only 3%. These findings stem from an analysis based on specific assumptions and characteristics. Consequently, the results may vary when implemented in unique supply chain networks with distinct characteristics. The proposed framework provides a valuable starting point to practitioners for understanding and improving supply chain resilience. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152049 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152048 Rationalizing Inventory: A Multi - Echelon Strategy for Safety Stock Justification Chen, Yu-Ta; Alexander, Rohan This work presents a multi-echelon inventory optimization model for a manufacturing company to evaluate optimal inventory levels for a selection of products and their sub-components. The guaranteed service model is employed to identify potential improvements in inventory allocation while maintaining service levels. The model's results are compared with the company's current inventory policies to provide insights into the effectiveness of the proposed approach. First, demand forecasting was conducted for the selected products, and the results showed a close match with the company's existing forecasts. Next, a multi-echelon inventory optimization model was formulated using bill of materials, component lead times and standard costs. The model was optimized in Python to minimize total inventory holding cost, with constraints on service level, service time, and bounded demand. The model's output suggested an "all-or-nothing" type of inventory policy, wherein a stage either maintains zero safety stock or holds the maximum permissible safety stock. The results of the model revealed that 54% of the analyzed sub- components do not require any safety stock to be held. Additionally, the model proposes pooling inventory in stage 0, which is the finished product stage. The true financial impact of the model's results is difficult to gauge, because only a small portion of the product portfolio was used in the optimization. Potential areas for future work include investigating the impact of phantom stock removal, applying the stochastic service model to this problem, and understanding the impact of multi-echelon modeling on supply chain resilience. The insights provided in this work can serve as a starting point for manufacturing companies aiming to optimize their inventory policies and better manage their supply chains. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152048 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152047 Tackling Food Waste: A System Dynamics Approach to Analyzing Food Waste in Wholesale Markets and Developing Targeted Interventions for Sustainable Operations Syed, Furqan Khalil This study addresses the global issue of food waste in wholesale markets, where 1.3 billion tons of food are wasted annually (in the whole value chain) while millions face food insecurity. Our contribution is a preliminary framework to tackle food waste challenges and promote a more sustainable and efficient food supply chain, emphasizing the importance of implementing targeted strategies and recommendations for lasting impact. Partnering with the World Union of Wholesale Markets (WUWM), our goal was to understand food supply chains in wholesale markets and identify opportunities to mitigate food waste. This study uses system dynamics (SD) modeling techniques, including causal loop diagrams and stock and flow diagrams, to analyze supply chain-related food losses and propose potential intervention strategies. We identified five key supply chain dimensions influencing food losses: Market Strategy, Supply Chain Operations, Infrastructure, Partnerships, and Macro-trends (economic, political, and technological). These dimensions underscore the need to balance commercial objectives and environmental concerns, efficient stock management, adequate storage facilities, collaboration among stakeholders, and consideration of broader food waste trends. Through this study, we demonstrate the utility of SD models in analyzing wholesale supply chains, providing valuable insights into managing and mitigating food waste. In addition, we identified key potential solutions such as timely investment in infrastructure, particularly cold storage, partnerships with food banks, and tracking waste against targets. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152047 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152046 Developing a Dynamic S&OP Process for Third - Party Logistics Elmquist III, Richard Augustus; Dávila Novoa, Luis Rodrigo This paper explores the complexities of temperature-sensitive food supply chains and the role of third-party logistics (3PL) providers in managing them. Specifically, we partner with the world's second-largest cold chain 3PL provider to establish a dynamic Sales and Operations Planning (S&OP) process for their warehousing services. In this project, we propose a novel inventory forecasting framework that could complement the S&OP process proposed aimed at aligning supply and demand, optimizing inventory level ls, and preserving the quality of temperature-sensitive food. We start by reviewing the related literature on food cold chain management and demand forecasting methods, complemented by qualitative interviews with key roles within the company. We select a specific warehousing site for our minimum viable product and extracted the data of inventory positions for every customer during a 3.5-year period. We propose segmentation criteria based on customer inventory size and variability and develop forecasting models for each segment and used Seasonal Autoregressive Integrated Moving Average (SARIMA) and Facebook Prophet. The accuracy of the models is measured using the Mean Absolute Percentage Error (MAPE) performance metric. The valuable insights offered by the forecasting models allows us to propose create additional freezer capacity at the site. We also identify underutilized space in the cooler segments that could potentially be repurposed to increase freezer capacity. Finally, we discuss next steps regarding the opportunities to improve the forecasting models and scale the dynamic S&OP process across the entire company network. Overall, our findings highlight the importance of a dynamic S&OP process for 3PL providers in managing temperature-sensitive food supply chains effectively. The insights from our inventory forecasting framework can help 3PL providers to optimize their operations, better align supply and demand, and preserve the quality of temperature-sensitive food throughout the supply chain. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152046 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152045 Digital Twins: Warehouses of the Future Moutaz, Faisal Ali; Chen, Yumeng As warehouse operations grow in complexity, many organizations turn to digital twins to increase their performance capabilities. Digital twins are virtual replicas of physical entities and their interactions with one another. The technologies in a digital twin capture real-time data to support improvements and decision making. This project focuses on digital twins as a promising solution for enhancing performance metrics within a warehouse operation, including efficiency, productivity, and scalability, particularly in the picking process. Because order picking is one of the most labor-intensive activities in the warehouse, we examined the feasibility of employing machine learning to forecast labor requirements. To develop a digital twin prototype for the order-picking process, we explored several technologies aimed at improving efficiency and productivity: sensors, automated guided vehicles (AGVs), picking robots, and automated storage and retrieval systems (AS/RS). By conducting stakeholder interviews, process mapping, and gathering data pertaining to historical order demand and daily labor hours, we formulated a workforce forecasting model that harnesses machine learning techniques. Leveraging the forecasting model alongside the recommended technologies will allow the warehouse team to enhance their key performance indicators (KPIs) for efficiency and productivity. This project culminates in a comprehensive roadmap for implementing these solutions, with the potential for scaling this digital twin prototype to other processes and warehouses. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152045 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152044 Development and Evaluation of Market - Based Routing Guide Strategy Zheng, Aaron; Oliver, Jorge The truckload market in the United States is large, fragmented, and highly competitive. Shippers utilize routing guides to manage and tender shipments to carriers. This research examines how the macro market characteristics and micro shipper characteristics affect routing guide performance. Specifically, each key market area to key market area lane is classified into one of four characteristics based on the annual number of loads at both the macro market level and micro shipper level: Balanced, Headhaul, Backhaul, and Sparse. This capstone project addresses three main questions: Are the macro market characteristics stable over time? How does routing guide performance vary by macro market characteristics and micro shipper characteristics? What specific strategies improve routing guide performance? Our research shows that the macro market characteristics are stable over time. There are significant differences in the routing guide performance by micro shipper characteristics, but not macromarket characteristics. We find that the routing guide strategy is a function mainly of what a shipper experiences, not what the entirety of the market experiences. When shippers develop a routing guide strategy, the micro shipper characteristics trump macro market characteristics. Hence, there is an opportunity to leverage the macro market characteristics for those lanes that are low-volume for a shipper but are in high-volume in macro market lanes, which represent about 53% total number of unique key market area to key market area lanes that shippers managed and 9% in total number of loads. The procurement framework we developed takes a portfolio approach by assigning specific strategies based on micro shipper characteristics and macro market characteristics. Our framework helps shippers leverage both micro shipper characteristics and macro market characteristics not only to reduce the efforts in managing transportation lanes but also to improve routing guide performance. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152044 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152043 Cost and Carbon Implications of a Patient - Centric Supply Chain O'Brien, Kyle; Kumar Yadav, Shobhit Trends toward patient-centric deliveries in the pharmaceutical industry pose a challenge for integration of sustainable supply chain design. This patient-centricity entails more distributed demand, smaller shipments, and more frequent deliveries. Our research utilizes scenario planning for quantification of CO2e and taxation within a pharmaceutical distribution network located in Brazil, where tax policy has a major impact on supply chain costs. Comparison between various scenarios allows for analysis of the taxation and CO2e emissions variations, with results showing how the patient-centric scenario is associated with increased CO2e emissions, and how taxation is not directly impacted by patient-centricity. Despite this, taxation does have a major effect on decision making for the location of distribution centers in Brazil. A scenario assessing the consolidation of demand at a weighted center-of-gravity (CoG) distribution center resulted in an estimated 10.1% savings on tax and 23.4% of CO2e reduction when compared to the base case scenario. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152043 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152042 Calculating Financial Business Risk to Identify Supply Chain Vulnerabilities Lien Yai, Pik; Lucas, Romain The COVID-19 pandemic has highlighted the vulnerabilities of supply chain systems, and companies must take Supply Chain Risk Management seriously to build resilience against future unknown disruptions. However, measuring risk and its impact is challenging due to data availability, interpretation of different types of risks, and complex product-supplier networks. Xylem, a global water technology company, posed a challenge to the capstone team to quantify the impact of risk using revenue as a measure. The team developed a Python program to quantify the impact of risk by suppliers, called Business Risk value, which is based on mapping parts, suppliers, models, and revenue in a structured and objective manner. In contrast, Xylem's previous approach lacked transparency and standardization. The team found that procurement spending is not simply correlated with the revenue impact of the company. The new Business Risk value can capture suppliers whose actual Business Risk value is high but went un detected in the old method because their procurement spending was low. The old method prioritized suppliers with high procurement spend, which may not add up to the actual revenue impact and creates unnecessary redundancy in the supply chain. The team suggests that Xylem expands the global database to include smaller suppliers to focus on mapping at least the Business Risk value throughout the supply chain to build resilience. Additionally, the team recommends mapping Time-to-Survive (TTS) to include as an indicator for the duration of impact time, which has not been factored in until now. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152042 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/152041 Incorporating Equity into Vaccine Access Schumm, Matthias; Tagorti, Mehdi COVID-19 vaccine access inequity was a major challenge during the pandemic. This inequity was present between countries and regions and within cities. We developed a novel approach to measure and improve vaccine access equity to address this issue. Our approach first created a vaccination attainment index based on CDC COVID-19 data. We then selected the most relevant spatial, e. g., the density of medical facilities, and socioeconomic factors, to train an XGBoost model on the county level of the United States. Using this model on census tracts within counties, we used the Gini and Theil indices to measure equity. We identified the main drivers of vaccine access based on SHAP values. With the main drivers identified (percentage of American Indian and Alaska native population, health insurance coverage, and transportation options), we conducted a case study on Cambridge, MA. We improved the short-term access equity by adjusting each census tract’s density of medical facilities (from Gini 0.14 to 0.13). Our novel approach provides decision-makers with a tool to identify and address drivers of vaccine access equity in their region and predict vaccination attainment on the tract level. These insights are crucial to ensuring equal access to vaccines and other essential healthcare services for everyone. Fri, 08 Sep 2023 00:00:00 GMT https://hdl.handle.net/1721.1/152041 2023-09-08T00:00:00Z https://hdl.handle.net/1721.1/143093 Closed-Loop Supply Chain Design for Sustainable Procurement of Office Supplies at MIT Rahman, Shah Akibur; Prakash, Pranav Massachusetts Institute of Technology (MIT), home to almost 27,000 students and staff with almost 400 departments, labs and centers, has a largely decentralized procurement system and informal reverse logistics flow or cost rebate programs for office supplies. Building on the success of the centralized procurement process for personnel protective equipment (PPE) and cleaning supplies, this capstone explores the feasibility of a consolidated office supplies procurement process and options for take-back schemes in the form of reverse logistics in order to support the MIT Climate Action Plan of net zero carbon emission by 2026. In order to achieve these key objectives, initially we mapped out the entire demand driven delivery process and order fulfillment at MIT to identify opportunities. Next, using historical purchase data of top purchased products on campus we designed and proposed an office supply stockroom model that could be located at the distributed mail centers or other shared locations. These locations could also serve as a drop off point for used office supplies to be picked up for recycling. Qualitative data has been collected in the form of user surveys and interviews to gauge user perception, knowledge and readiness towards making sustainable choices. Finally, by linking the forward and reverse logistics flows, the project frames the circular supply chain that is enabled by the stockroom model to increase sustainable purchasing and reduce waste and cost for the institution. Mon, 13 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/143093 2022-06-13T00:00:00Z https://hdl.handle.net/1721.1/143092 Who Are You Gonna Call?: A Smart Recommendation System for Carrier-Shipper Matching Fellin, Lauren Jennifer; Lin, Kai-Wei For a broker in the transportation industry, one of the most critical decisions for the carrier representatives within the company is determining which carrier to select for a customer load. In order to determine which carrier would be the best for a shipment, certain criteria need to be selected in order to align and develop a scorecard ranking system. To decide which carrier should be selected from its database for a shipment, GlobalTranz is seeking a strategic scorecard system that would complement its current carrier-shipper matching platform. In this capstone project, a customized ranking system was developed that would allow the carrier representatives to make strategic decisions. A narrowed down list of criteria was created that encompassed three major metrics including geographical fit, level of service, and financial fit. The prototype recommendation system will enable the carrier representative’s decision to be more objective. This solution will standardize the current decision process and facilitate efficiency in the future. Mon, 13 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/143092 2022-06-13T00:00:00Z https://hdl.handle.net/1721.1/143091 Understanding the State of Supply Chain Sustainability Gupta, Avanika; Wenske, Taryn The emphasis on sustainability within supply chains across industries has increased in recent years. Today, companies across the globe report on sustainability efforts and progress each year and set goals to reach ambitious environmental and social sustainability targets. This increased focus has prompted questions regarding how sustainability practices are interpreted and understood. How do different demographic groups (i.e., gender, language, location, age, and industry) interpret the current state of supply chain sustainability? Have the long-term implications of COVID-19 affected companies’ commitments to supply chain sustainability? Our analysis used response data from the 3rd Annual State of Supply Chain Management Survey and context gathered through supply chain executive interviews to answer the two main research questions. After slicing the survey response data into demographic categories – gender, age range, region, survey language translation, and industry – we performed non- parametric Mann-Whitney-U and Kruskal-Wallis ANOVA tests to see if the different groups interpret sustainability commitments significantly differently. When testing within single demographics, results showed significant differences in responses by demographics. This seemed to explain some of the difference in how people interpreted supply chain sustainability; however, when isolating groups further, this became less apparent. Upon isolating the gender, age range, and location demographics by major industries, fewer responses showed significant differences. From this, we can conclude that comparisons of sustainability guidelines and practices should be industry-specific, rather than specific to other demographics such as gender, age, or location. Our capstone results could provide the basis for future research to understand the variations in how different groups of people interpret supply chain sustainability within the same company, industry, or outside of an organizational setting entirely. Mon, 13 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/143091 2022-06-13T00:00:00Z https://hdl.handle.net/1721.1/142957 Democratizing Access to Supply Chain Finance for Small & Medium Enterprises Kulluk, Emre Muzaffer; Nicholls, Daniel Granados Small and Medium Enterprises (SMEs) are 2.6 times more likely to be rejected for a loan than a multinational, and the worldwide trade finance gap for SMEs is estimated at $1.7 Trillion USD. The main barrier to finance for SMEs is the high costs of due diligence during the financing process. Our research partner, a third-party logistics (3PL) provider was interested in exploring using their trade data to inform creditworthiness decisions for SMEs. Previous research has shown that alternative databases can be used to improve the risk assessment of SMEs’ creditworthiness, however, we found no evidence that supply chain operational data from 3PLs can be used to improve the creditworthiness assessment of SMEs for trade financing. Through a partnership with a 3PL with a financial institution branch, we collect insights into the challenges and opportunities for 3PLs to leverage their databases to better inform credit scoring decisions for SMEs. We also use two publicly available databases to illustrate the methodology we propose in our research for 3PLs to build their own credit scoring methodologies. We document the proposed features to be explored by 3PLs which to build their own credit scoring models. Aligned with the research on alternative databases, we conclude that the use of operational supply chain data from 3PLs can be useful to strengthen credit scoring models for trade financing of SMEs. In addition, we propose solutions to common challenges drawn from the nature of a 3PL’s data structure and initial model iterations (i.e., cold start problem, feature acquisition). Supply chain operational data from 3PLs can be leveraged to build a credit score model and could be a steppingstone for 3PLs to take a central role in the trade ecosystem. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142957 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142956 Network Design in Maintenance Inventories for Electric Utilities Maen, Jason Andrew; Winters, Nicholas Shiverick Within the electric distribution space maintaining a balanced inventory of spare parts forms a critical component of resiliency and restoration in the event of an outage. We find that leveraging network design provides an opportunity for utility companies to improve the effectiveness of the inventory they hold, enabling better service at a lower cost. For one regional utility company in the United States, an inventory reduction of 35% was found by adopting a hub and spoke model over a previous decentralized model. We believe that this observation can be extended to other companies with distributed assets, highly variable demand for inventory, long lead times, and a high cost of downtime. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142956 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142955 The Impact of Logistics Provider Data Maturity in Defining Scope 3 Transportation Emissions Lestari, Nora; Xiong, Jessica Yao Global warming is a reality. According to the UN’s Intergovernmental Panel on Climate Change, the Earth’s average temperature may increase by another 1.5°C to 2.0°C in the next thirty years, causing extreme weather, deteriorating air quality, depleting resources, and disrupting economies. By 2025, an estimated 1.8 billion people worldwide may suffer absolute water scarcity. Xylem Inc. is among the companies aiming to reduce its carbon footprint to offset this trajectory. Xylem aims to reduce its supply chain carbon footprint by 2.8 million metric tons by 2025. Among supply chain activities, transportation and logistics remains one of the greatest contributors to carbon emissions. To help the company take swift climate action, our capstone helps Xylem understand emissions hotspots along its inbound transportation network and identify emissions reduction initiatives by way of establishing an accurate and reliable Scope 3 emissions baseline. By developing a hybrid activity-based emissions calculation tool, we assessed the data maturity of Xylem’s top ten logistics suppliers, added supplier evaluation criteria, and provided means to overcome data limitations. We quantified trade-offs between emissions and commercial levers (e.g., cost, customer service level, etc.) and estimated the emissions effects of supply chain decisions using scenario, comparative, and regression analysis. The results show that shipment mode selection, shipment weight, shipment type, and shipment transit time have a meaningful impact. The combined effects of these variables, coupled with supplier selection, may help Xylem reduce up to 50% of its inbound transportation carbon footprint. The outputs of our study include a set of tailored demand and production planning, sourcing and procurement, inventory management, and customer relationship management recommendations and a prioritized implementation roadmap to support Xylem in its pledge towards net zero operations. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142955 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142954 Global Home Care Supply Network Design Optimization Gonzalez, Alejandro Danniel; Hoque, A H M Shahidul Companies can expect to lose almost 42% of one year’s profit every decade because of supply chain disruptions. Working to have better supply chain resilience and robustness is now a necessity to stay competitive and profitable. This capstone addresses the creation of a comprehensive and scalable vulnerability assessment framework for an FMCG company to help assess risks in supply chains and take the right resilience measures. Currently, the sponsoring company is facilitating this process by event simulations, but results are not consistent, as the input variables to its simulation model are not based on empirical data. To address this problem, this research project developed a step-by-step methodology for creating a vulnerability map for the supply chain in scope. Questions to answer were: what can go wrong, what is the likelihood of it occurring, what is the consequence from it, and what are recommended resilience strategies? The approach taken was threefold. First, we mapped the supply chain by location and gathered data of natural disruptions and their consequences to establish statistical database. Second, we developed a model that simulated the natural disruptions for each country in scope by using Monte-Carlo technique. Third, we translated the results of natural disruptions into operations shutdown days. Our results were fairly high and showed that our sponsoring company’s supply chain in scope could expect to have 227 days of total operations shutdown in the next 10 years. Results were visualized on a vulnerability map with the countries as nodes together with a breakdown of where most of the risks come from. In closing, our sponsoring company now has a model to better assess vulnerability on its supply chain and can therefore focus on resilience strategies to mitigate the risks by more accurate simulations of events. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142954 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142953 Quantifying Warehouse Automation and Sustainability Peterson, Taylor; Gonzalez, Miguel Garcia Beginning in 2020, the e-commerce grocery retail industry grew rapidly, largely due to the COVID- 19 pandemic. In addition to this shift in consumer shopping preferences, retailers are facing another challenge: a looming labor shortage. This shortage of workers has caused enormous disruptions across the supply chain, particularly for activities performed within warehouses and fulfillment centers. To tackle these challenges, companies are embracing a series of strategies to help ease the pressure of the labor shortage in warehouses. One of these leading strategies is automation. At the same time, the energy consumption of automation equipment raises concerns of its environmental impact among investors, regulators, and customers alike. Although there are general greenhouse gas accounting standards, there is no comprehensive link between warehouse automation and emissions. This research proposes a framework for measuring greenhouse gas emissions stemming from warehouse automation. The result is a dynamic carbon emissions calculator that determines the total CO2 emissions derived from the energy consumption of various automation technologies. The framework is validated using real data from an e-commerce grocery retailer and provides results indicating that sustainability and automation are not mutually exclusive. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142953 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142952 Inventory Rebalancing through Lateral Transshipments Halbe, Himanshu; Je, Jinwoo Disruption in supply and demand causes imbalances in inventory positions across an enterprise’s supply chain network. In the medical device industry, having the right product at the right location in the right quantity is of critical importance. Therefore, companies thrive to maintain an optimal inventory position across their distribution network. Boston Scientific, a global leader in the medical device industry, has been facing an inventory imbalance post-Covid across its distribution network. To optimize the inventory position across the company’s distribution network, this study has explored lateral transshipment, a practice of repositioning inventory between same echelon distribution centers. We have primarily used Mixed Integer Linear Programming (MILP) to find the optimal transshipment solution at each SKU and distribution center level. Existing inventory classifications systems, and newly developed heuristics to select high priority SKUs for optimization. Simulation studies were conducted to generate stochastic demand, and analyze how the optimized inventory model compares to the current model. Our research shows that lateral transshipment reduces 10% to 25% of total inventory cost while maintaining a superior inventory position compared to the current inventory model under varying demand. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142952 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142951 Strategic Approach for Assessing Supply Chain Resilience Investment Options Clark, Rachael Grace; Pan, Weiqian In today’s business world, supply chain resilience has been brought to the forefront of management topics due to the increasing number of both natural and manmade supply chain disruptions in the past decades. Without an effective method to identify the most impactful resilience actions to take and the amount of investment needed, companies face challenges in appropriately allocating resources to improve overall resilience of their supply chain networks. In collaboration with researchers from the MIT Center for Transportation and Logistics and with data from a sponsoring company, this project characterizes impactful resilience actions and necessary investments within a supply chain network to increase supply chain resilience for a firm. Furthermore, this project provides a roadmap assessing various disruption mitigation and resilience actions and provides a theoretical methodology to identify a firm's performance loss at risk as an indicator for the maximum amount of investment for a particular resilience action. This methodology serves to support supply chain managers in creating effective and actionable resilience strategies to prepare for and respond to unexpected disruptions and build sustainable operations. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142951 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142950 A Strategic Tool for Finding Optimal Last Mile Fleet Size & Fleet Composition, using Knapsack, Bin Packing, and Aggregate Planning Guajardo Ramos, Jesús; Gremillion, Frances Elizabeth As a result of the COVID-19 pandemic, the ecommerce share of retail has grown at an accelerated rate, increasing the number of home deliveries and delivery speed expectation from customers. Moreover, demand variability over the year and delivering a wide variety of products in terms of weight and volume has increased the last mile delivery cost for most companies. We developed this capstone project for one of the biggest retailers in Mexico, Coppel. Our work is focused on creating a strategic tool that allows our sponsor company to define the optimal fleet composition for their last mile delivery operation at each logistic facility, and to formulate an allocation strategy for their different order types. To find the optimal solution in terms of cost, we built a combination knapsack & bin packing model, with aggregate planning. To see how level of service, defined as the delivery speed, and demand variability over the time impacts cost and CO2 emissions, we selected 3 different regions, which are Culiacán, Tecamac, and Monterrey. These three regions were selected, as they cover most of the characteristics of Coppel’s last mile operation. Our results indicate that it is financially beneficial for Coppel to reassess their order allocation restrictions and the usage of third-party, for both full truck rentals and small parcel carriers. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142950 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142949 Synchronization of Ocean Export Supply Chain Li, Yulu; Ramírez Moreno, Michelle Stephanie Ocean freight represents more than 70% of the global trade by volume. Given the price increases in transportation rates, companies are more interested in driving efficiencies and cost optimizations through supply chain synchronization. In this document, synchronization refers to the optimal coordination of transportation costs, inventory, and service level, while considering flexibility and sustainability. This project aims to provide an understanding of key components of synchronization, and ultimately provide a framework that illustrates the collection of supply chain elements to drive synchronization that companies can use to improve their supply chains. In order to do this, we analyzed information about the CPG company using Power BI. We performed a Center of Gravity analysis to propose the best location for the mixing center. We built a Mixed Integer Linear Programming model that provided the optimal volume allocation from the supply warehouses to the mixing center, from the mixing center to the ports of loading, and from the ports of loading to the ports of discharge. The results show that there is an opportunity to reduce 9% of the costs by optimizing the volume allocation and incorporating rail transportation in the inland freight from the supply warehouse to the mixing center and from the mixing center to the ports of loading. This project aims to represent an enabler for companies to run scenarios and decision- making. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142949 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142948 The influence of manager-centric competencies on the performance of micro and small enterprises in Latin America Fredericks, Elise Nicole; Rodriguez, Maria del Pilar Pardo According to an OECD report, micro and small enterprises (MSEs) constitute 99.5% of all firms in Latin America and employ approximately 60% of the LATAM population. However, despite their economic and social importance, MSEs are subject to high failure rates, primarily stemming from low productivity, lack of managerial skills and poor demonstration of supply chain management expertise. Therefore, the focus of this study is to evaluate which business competencies and integrative practices lead to the successful development of MSEs in Latin America using quantitative models such as principal component analysis, ordinary least-squares regression, and analysis of variance. The results show that the competencies integrated in a firm have only marginal direct effect (positive/negative) on firm performance. Significant effect is observed from supplier integration, customer integration and proactive innovativeness. However, when taken in context with the behaviors and actions of the manager, all competencies that are integrated in a firm showcased a significant effect on the selected performance parameters. Therefore, the study shows that (1) the performance of MSEs in Latin America is highly dependent on the interaction between firm competencies and the personal characteristics of the enterprise manager, and (2) to achieve higher levels of profitability and sales, managers must adjust their behaviors based on the different stakeholders they engage with. Effectively, managers must be conscious of their influence on firm performance as this significantly affects business growth in the long-term. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142948 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142947 Managing Disruptions: Understanding Shipper Routing Guide Performance Caza, Grace; Shekhar, Varun Shippers utilize routing guides to tender shipments to carriers at contracted rates. They also tender loads on the spot market, where they compete with other shippers for carrier capacity and market pricing. Although previous studies have looked at routing guides and shipper procurement practices, none have explored the resiliency of routing guide performance; specifically, whether routing guides are able to insulate shippers against the volatility experienced by the spot market during disruptive events. This study considers loads tendered by shippers using a routing guide and shippers’ decisions on when to utilize a routing guide vs. sending tenders directly to the spot market. Routing guide performance is measured during planned events such as DOT Roadcheck week, national holidays, and unplanned events such as hurricanes. Hypothesis testing is used to determine the statistical significance of the differences in routing guide performance across three periods relative to a benchmark: leading up to, during, and after disruptive events. This study found that routing guide performance changes year-over-year when the same repeating disruptive event is considered due to market cycles. Better performance is observed for loads that occur on high-volume lanes and when considering planned events compared to unplanned events. This study shows that routing guides perform differently during disruptive events and that there are opportunities for shippers to improve both their routing guide performance on low-volume lanes and their decision-making processes for when to utilize the spot market. Understanding routing guide performance behavior in response to freight disruptions can help shippers better manage their freight networks in terms of volume and cost. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142947 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142945 Total Landed Cost Model Chaudhry, Muhammad Sohaib; Lee, Kun-Zhe As more and more multinational companies are exploring low-cost countries to purchase commodities from, it has become critical for companies to calculate the total landed cost before making a purchasing decision. This capstone project establishes a model for the sourcing team of GE Gas Power to capture costs such as materials, labor, energy, transportation, even custom duty and tariffs for each country to optimize the total landed cost. Sensitivity analysis is also conducted on a sample part provided by GE to evaluate how changes in various cost factors impact the best-cost country ranking. A comparison is also made between the outcome of the model and the purchasing decision made by GE in the past. As a result, our recommended best-cost country based on the model matches with some of GE’s existing sourcing countries. Our model also suggests some countries that GE never purchased from, which resulted in total landed cost difference of up to 17% compared with purchasing the sample commodity from current countries. This list would help GE further explore those countries with some examinations of qualitative measurement and standards before making the final purchasing decision. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142945 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142944 Capturing Value in Pharmaceutical Distribution Strategies Matz, Lauren; Ogbuefi, Irene Market Share in the pharmaceutical industry has been dominated by manufacturers that develop the most effective Go-To-Market Strategy. New promises in Cell & Gene Therapy and Personalized Healthcare Products open a wealth of opportunities for new market share in the Asian Pacific region, if manufacturers that can position their supply chain and associated partners effectively from the start. While previous supply chain strategies for pharmaceutical distribution have relied on a single large distributor to manage affiliate level in-country logistics, administration, & payment management services, alternative distribution strategies and partnership schemes may provide greater value to the overall healthcare ecosystem for patient-centric products. To perform a proper evaluation of the potential value a novel distribution strategy could deliver to the manufacturer, patients, and healthcare system, a Multi Attribute Value Analysis (MAVA) Model was created for two use case countries. The alternatives under consideration were a traditional distributor strategy, a switch to a multiple-partner strategy to handle different components of the CGT/PHC supply chain flows, and a switch to an in-house management strategy with the manufacturer handling the majority of the distribution roles. The criteria chosen for the MAVA model evaluation included financial, logistics, and patient factors that aimed to capture a holistic view of the distributor’s performance. Value functions mapping a criteria’s rating to a normalized score were determined, and weight importance assignment was elicited from key stakeholders. Upon generating data for the initial MAVA model run, the total value a distributor could provide was determined by the model. For the new personalized healthcare product segment, it was found the multi-partnership strategy provided the best overall value in both use cases countries (with a final score of , primarily due to better performance in critical Inventory Management and Patient Engagement KPIs. From this study, it is evident that considering the market structure and pharmaceutical regulations of individual countries helps pharmaceutical companies tailor supply chain strategies to each country’s context to maximize patient satisfaction, resource mobilization and cost optimization. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142944 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142943 Improving Delivery Performance Through Predicted Transit Times Lee, Debra Maintaining high on-time delivery performance is critical for third-party logistics providers. Not only does reliable on-time delivery function as a competitive edge and allow logistics providers to serve their customers more effectively, but it can also improve operational efficiency for the provider itself. Our sponsor company is a third-party logistics provider that must balance a growing portfolio of shipments that leverage external less-than-truckload (LTL) carriers. We therefore proposed and validated a two-pronged approach that utilized machine learning to improve LTL transit time predictions and then used these predictions in an integer programming model to maximize on-time orders. Independently, the prediction model improved the transit time RMSE from 3.07 days to 1.97 days. However, we obtained the most improvement in delivery performance through the optimization problem. By investigating the effect of lengthening the buffer days, or additional days added to the lead time beyond the predicted transit time over a rolling weekly basis, we obtained up to a 41% improvement in on-time deliveries over the status quo. Overall, the research demonstrates the strength of this mixed approach and provides flexibility to expand to other modes of transportation or a variety of objectives that may arise when planning shipments. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142943 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142942 Less is More: Simplifying Inventory Tactics Miller, Andrew; St. Lifer, Alex Millions of small and medium-sized enterprises (SMEs) fail every year in the United States, and in many instances the root cause of failure is more often due to operational inefficiencies than external factors. One way SMEs can overcome operational inefficiencies is by implementing inventory management strategies, such as modularization and postponement, to increase profitability, production efficiency, and forecast accuracy. The sponsoring company for this project is a small consumer packaged goods (CPG) company that recently implemented a modularization and postponement strategy to its main product. We quantified the impact of the new strategy by measuring six key performance indicators (KPIs) before and after implementation through statistical analysis and Monte Carlo simulation analysis. Our analysis found that the new strategy increased profitability by 14.57%, increased production efficiency by 50%, decreased MAPE by 42.2% and increased warehouse capacity by 52.6%. Modularization and postponement can be successfully implemented in SMEs, and these strategies increase profitability and reduce operational inefficiencies. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142942 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142941 Designing A Resilient Supply Chain Peng, Pai; Rueckerl, Karoline There has been a significant increase of interest in supply chain resiliency since the onset of COVID-19 as multiple supply chain disruptions have affected companies across the globe. By increasing resiliency, companies aim to increase their ability to adapt to changes occurring throughout their supply chain network. The 3PL company, Coyote Logistics sponsoring this capstone, is trying to increase their customers’ supply chain resiliency through supply chain network design with a focus on transportation costs. Two Coyote customers from different industries were selected as case studies for this project, one from the retail big box industry and one from the packaging industry. An optimization model was implemented to investigate the effect of supply chain resiliency and network design on transportation costs by iterating the model over various demand and resilience threshold scenarios. The analysis across various scenarios revealed that a more resilient supply chain network only minimally increases transportation costs. For example, a 50% resilient supply chain network only resulted in a 3% increase in transportation cost for one customer. Whereas the other customer’s supply chain network though equally resilient in some scenarios was not sufficient to meet certain levels of demand in others, highlighting the importance of facility capacity in resilient supply chain network design. Therefore, it is critical to understand facility capacity relative to demand locations when designing a resilient a supply chain network. For example, facilities should be spread out geographically and the facilities should be sharing the customer demand fulfillment responsibilities equally. This project underlines Coyote’s work with their customers to increase their ability to respond to disruptions in the supply chain and design a more resilient network for the future. In further studies, more capacity information specific to distribution and manufacturing facilities as well as a multi-stop fulfillment strategy should be considered. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142941 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142940 Transforming eCommerce Product Segmentation with Machine Learning Arora, Ankita; Bosch, Alejandro Souza Inventory management is one of the key elements of supply chain management for any organization to manage costs versus service level tradeoffs. Product segmentation for inventory is therefore a key lever for inventory management. Traditionally, this segmentation is done using only a single criterion. This paper presents a framework that uses a hybrid approach combining a multi-criteria decision-making technique, analytical hierarchy process, and machine learning algorithms, support vector machines and artificial neural networks, to improve product segmentation using multiple criteria as opposed to single criteria. Our results show an addition of 20-30% SKUs that should be in ‘A’ class that wouldn’t have been classified as ‘A’ products using a univariable approach. The machine learning models show an accuracy of 92.3% for linear SVM and of 86.5% for ANN with 8 nodes, with linear SVM outperforming ANN. Hence, our work demonstrates that using a hybrid model with AHP and SVM results in a flexible and customizable segmentation model that is highly beneficial for any rapidly growing company with a heterogenous product portfolio and can serve to increase the service level as well as decrease inventory costs for companies. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142940 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142939 Uber Freight: Assessment and Determination of Optimal Design Features for a Drop Trailer Service Offering and Network Ker, Soon Kiat; Liu, Siqing The trucking industry hauled 72.5% of all freight transported in the U.S. and served an essential function in transporting cargo nationwide from one place to another. Recently, the industry has suffered from significant disruption, such as a shortage of drivers and carriers. One of the leading causes of these issues is the lengthy detention time in most operational activities, such as loading and unloading time at the warehouse. Previous research recognized that the drop trailer offering serves as an effective solution for reducing the waiting time at warehouses and improving the on-time delivery rate. When it comes to our sponsoring company - Uber Freight, this type of service is still nascent, with several strategic questions unanswered. Specifically, two of the most crucial key research questions are 1) where it should expand its drop trailer service 2) what load requirement and network characteristics are best serviced with a drop trailer. Our capstone project first deployed the K-Means clustering method to address these questions to uncover the underlying pattern and key network characteristics of states that have successfully implemented the drop trailer service. The result showed that Illinois, Indiana, and Florida possess the highest feature similarity with those states and hence, are recommended for Uber Freight to introduce drop trailer service. Our project deployed a CART decision tree to decompose the critical features from our cluster results that provide a structured recommendation for drop trailer implementation to answer the second question above. The analysis indicated four features necessary for a Drop offering to be favourable compared to live loading dry-van offering. These four features lay out two sets of market conditions with their strategic consideration for Uber Freight to implement drop trailer in the future. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142939 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142938 A Direct Route to Sustainability: A Network Optimization Model to Reduce UNICEF Zimbabwe’s Carbon Footprint Farran, Karim; McCormack, Timothy As part of Agenda-2030, the United Nations developed the Sustainable Development Goals (SDGs) with the objective of achieving a better and more sustainable future. One of the key drivers for reaching this objective is to lower carbon emissions globally. In line with the carbon SDGs goals, the United Nations Children’s Fund (UNICEF), a United Nations agency responsible for providing humanitarian and development aid to children worldwide, sponsors this capstone project to analyse the carbon emissions of its supply chain transportation network in Zimbabwe. Through this capstone project, we study the effects, in terms of carbon and costs, for utilizing direct routes between UNICEF’s suppliers and end beneficiary. In this capstone project, we formulate a multi- objective optimization model that optimizes simultaneously the total costs and carbon emissions. Yet, in contrast with other green network design models that rely on aggregate methods for calculating carbon emissions (such as the Green House Gas protocol), in this study we not only use an equation that considers a more granular fuel consumption estimation of the vehicles (i.e. adapted from the Network for Transport Measures), but we also calculate the theoretical fuel consumption of the vehicle by using the comprehensive modal emissions model that also considers specific characteristics of the road, engine speed, engine displacement, velocity, total weight, road slope, and acceleration. To the best of our knowledge, this study is the first attempt to combine two of the most detailed estimation models of transport emissions. In addition, our proposed network design model includes the flow of multiple products within UNICEF Zimbabwe’s supply chain network, as well as consolidation capabilities, that is, the output of the model provides the optimal combination of products to consolidate within each truck shipment. Our results are visualized using the Pareto Frontier that displays all optimal carbon and cost combinations for UNICEF’s network. This Pareto Frontier serves as a tool that UNICEF’s management can use to choose the desired levels of cost and carbon emission. Ultimately, we find that enabling direct routes in the model produces a win-win situation where both carbon emissions and costs are reduced. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142938 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142937 Optimizing Returnable Transport Item Repositioning in a Global Supply Chain Ikeya, So; Latorre, Lisandro de The automotive industry is an exemplary leader in the use of returnable cases for the transportation of inbound freight. Their use reduces the need of expensive and expendable packing and contributes to lower greenhouse gas emissions. However, the utilization of returnable cases is hindered when there is high variability in demand and lead times that make the return process challenging. This imposes a network design challenge in which the flow must be analyzed and determined to guarantee delivery continuity. Focusing on this problem, this research deals with an expanded network that enables more possibilities for the transportation of returnable transport items. The results will measure the potential total costs reductions and the impact on greenhouse gas emissions that result from the new network design. With this motivation, we developed a series of single-objective mathematical models to obtain minimum costs and emissions targets. We then used goal programming on a bi-objective mixed-integer linear program to investigate the quantity and flows of returnable cases that minimized both costs and emissions targets obtained previously. The optimal solution is reached with a 3.4% decrease in costs, 1% increase in CO! emissions, and a 51% reduction in the use of expendable packing. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142937 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142936 Demand Prioritization for a Water Technology Company Li, Xiaoyue; May, Jennie With demand outpacing production capacity, increased variability in supply lead times and customers' conflicting requests to escalate orders, there is a demand prioritization debacle. This Water Technology Company is faced with inefficiently and manually prioritizing individual orders. To ensure the appropriate demand is fulfilled first, the Water Technology Company is seeking a standardized and repeatable demand prioritization process. This sustainable solution will allow for objective and immediate demand prioritization while taking into account all business stakeholder interests. By implementing a Multi-Criteria Decision Making (MCDM) model, the top criteria were identified. Along with MCDM, a hybrid approach to the Analytical Hierarchy Process (AHP) was applied to identify the weights for each of the six criteria. In order to normalize demand orders so the selected criteria could be compared to one another, value function patterns were applied to the criteria inputs. The MCDM demand prioritization model is expected to generate improvements in the key metrics of Profit Margin, Days to Delivery, and Days to Customer Want. The largest expected improvement is with the increase in Profit Margin, ranging from 75% to 250%. Simulation tests were conducted to review the robustness of the six criteria and the group aggregation methods of the criteria weights. This demand prioritization process can be customized and implemented to any industry in any country. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142936 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142935 Overcoming the Driver Shortage by Addressing Driver Detention Mera, David Esteban; Sirikande, Sandeep Kumar In the United States, long-haul trucking is the main mode of transportation of goods. The trucking industry has been facing a shortage of drivers in the past few years and it is only increasing every year. Previous studies have found that multiple reasons are contributing to the driver shortage. These include underutilization of drivers’ working hours, unfavorable working conditions leading to reduced driver retention, the inability to attract new drivers due to high costs in acquiring licenses, and lower income compared to other comparable jobs in the market. The current study focuses on addressing the driver shortage issue by studying factors causing underutilization. Driver utilization is calculated using the number of hours a driver drives on a typical working day and comparing it with the federal hours of service regulation. The data analyzed for this study was collected from a midsize U.S. transportation company. Specifically, this is the driver log data from the truck's Electronic Logging Devices (ELDs). Statistical data analysis showed that the driver detention time or dwell time at warehouses for loading and unloading contributed to the underutilization. More significantly, it was higher during the weekends than the weekdays. Through interviews, this capstone has identified the major factors affecting the dwell time during the weekends: having inexperienced personnel during weekends, lack of communication, training of personnel, and the scheduling of the trucks. By acting on these factors and improving warehouse operations, the industry can achieve valuable improvements in truck driver utilization. These improvements can help warehouses with efficient load operations and also address and alleviate the driver shortage problem by approximately 20%. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142935 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142934 A Shipment Difficulty Model for Third-Party Logistics Resource Allocation Mueller, Christine M.; Yao, Lili The market for obtaining truckload capacity is becoming more dynamic as demand for truckload freight capacity in the US increases. Freight brokers form a vital connection between shippers and the hundreds of thousands of truckload transportation providers in the US and are critical to unlocking all available freight capacity. Previous research has focused on network and load optimization for freight brokerage firms, but not on optimizing the internal resources dedicated to booking and managing shipments. This study investigates commonalities in features between shipments that require similar amounts of resources to manage using feature engineering to quantify various shipment characteristics and unsupervised machine learning to cluster features. The results of this study found that there is overlap between the shipping cost per mile, the number of carrier cancellations, and the lead times between shipment request, shipment booking, and pickup time. Understanding how these shipment features relate to one another and contribute to overall shipment difficulty will help freight brokerages and third- party logistics providers better anticipate which types of shipments will require more the allocation of more internal resources in order to more effectively manage internal operations. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142934 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142930 Dynamic Approach to Freight Transportation Pricing Chandra, Vikas; Taibek, Maksat The tender acceptance rate significantly decreased between 2019 and 2021 for our capstone sponsor, Aqua Deal, a bottled beverage manufacturer. This poor primary carrier performance led to increased use of the spot market, higher transportation costs, and lower carrier service levels for Aqua Deal. In addition, during the COVID-19 pandemic, the truckload industry entered a tight market, where demand for trucking services outweighed the available market supply. This led to an increase in transportation costs and reduced tender acceptance rates for Aqua Deal. To address this issue, we explored the use of Index Linked Freight Contracts (ILFC). The purpose of ILFCs is to increase carrier acceptance by dynamically adjusting prices using an index. Using transactional data from 2019 to 2021, which covered both soft and tight markets, we built a logistic regression model to simulate potential carrier acceptance rate given price adjustment. The model predicted a 2% improvement in carrier acceptance but with a 4% increase in costs. We also explored other paths to increase carrier acceptance such as to avoid rushed shipments whenever possible. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142930 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142929 Consolidation for Import Distribution Optimization Shinar, Hasan; Wu, Huisi With the rapid growth of the e-commerce and the increasing awareness of environmental protection, retailers are facing new challenges to make product delivery fast and green while maintaining their profit margin. A balance between service level and cost to serve is required on both strategic and operational level. Transactional transportation decisions are usually made with transportation cost minimization as a target, due to the inefficiency of information flow in the organization. In this capstone project, we introduced a practical model to perform transactional route and load planning through quantifying the business implication of shipment delay. Export container consolidation for DC by-pass in import distribution was the use case, and analysis was performed on the historical shipment data from a global sports brand. Mixed Integer Linear Programming was used to model the problem based on the routes in the existing network. Total cost minimization was the objective. Carbon emissions for line-haul movements and delivery performance were included as planning effectiveness indicators. 30% total cost reduction and 13% improvement on delivery performance were seen with the sample data. The model is very efficient for transactional planning purpose, with 80% of the runs executed within 1 second. It is also scalable to simulate various business scenarios. We expect the findings provide directions to drive product and solution development. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142929 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142928 Inventory Management for Automated Convenience Stores in Brazil Costa, Andre Nascimento; Naithani, Sanjay Kumar Today, small retailers in Latin America account for 70% of the market share. Convenience stores play a crucial role, as people look for more convenience with modern lifestyles. Most of these stores are managed by people without experience or formal education in business management. A challenging problem for small retailers is inventory management. We developed this project for Onii, a Brazilian startup company with over 300 convenience stores in the country, run by small businesspeople in a franchise-like model. Its stores are entirely automated; thus, there are no cashiers or employees inside the store. This project aims to develop inventory management policies for Onii store operators and help them manage their stocks better. We use unsupervised Machine Learning techniques like k-means clustering and principal component analysis to identify patterns and segment stores and items. Then various inventory policies were computed to look for the lowest cost for each combination of clusters of stores and items. The best policy for the Onii store's reality is the Periodic Review model, with different period parameters (R) for each combination. At last, sensitivity analysis was conducted to determine the impacts of each parameter used in the model, such as ordering cost, holding cost, and inventory cost. The result is a robust model that Onii can apply to their current and future stores. Fri, 10 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142928 2022-06-10T00:00:00Z https://hdl.handle.net/1721.1/142921 Supply Chain Modeling for Temperature-Sensitive Pharmaceutical Goods Guadarrama, Ricardo; Gweder, Abdulrahman The pharmaceutical industry relies on cold chain supply infrastructure to preserve the integrity of temperature-sensitive products. Specifically, passive controlled temperature solutions present an increased challenge as the inherent uncertainty of ambient temperatures and process lead time variations in the delivery dramatically increase the risk of inactivating the product. Despite the sponsor company's efforts to test their package solutions in laboratory temperature-control chambers, a lack of visibility exists on the likelihood of success of the packaging solution in real-life conditions. Hence, developing predictive forecasting capabilities for their deliveries across the United States can provide significant financial and operational benefits. This research studies and compares two families of methods of predicting temperature ranges of the goods: statistical methods, Autoregression and ARIMA, and machine learning methods, K-Nearest Neighbor, Support Vector Machines, Random Forest, Quantile Regression, and Long Short-Term Memory Neural Networks. Additionally, one-step and multi-step ahead forecasting techniques were analyzed in all models to determine the best forecasting approach. In addition, the forecasting models were tested on two types of packaging solutions, one for the summer profiles and the second for the winter profile. The results confirm that one-step ahead models outperform multi-step ahead forecasting for long-term horizons when compared by RMSE and MAE. Both statistical and machine learning models accurately predicted training and test set values with relatively lower RMSE. Nonetheless, it was found that testing the models in new external temperature conditions presented contradictory results for predicting the internal temperature, mainly due to the limited data set utilized to train and validate the models. Quantile Regression, on the other hand, successfully predicted the internal temperature of the payload’s given new ambient conditions. Therefore, we concluded that a forecasting model can be implemented as part of a predictive risk assessment analysis, considering the impact of variability in both temperature and process lead times for the sponsor company’s passive- controlled temperature solutions. These models can be extended for future applications with different configurations of insulator materials, amount of gel packs, and package dimensions. Thu, 09 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142921 2022-06-09T00:00:00Z https://hdl.handle.net/1721.1/142920 Better Contract – Better 3PL Performance Kook, Tony S.; Oleksyn, Mykola Contract design and management are critical for Fast-Moving Consumer Goods (FMCG) companies. Well-designed contracts promote a value-driven relationship between the buyer and the supplier. Under the governance of such contracts, the service provider will proactively lead continuous improvement efforts in the direction that best aligns with the goals of the buyer. Such goals may include cost savings, service level, or context-dependent KPIs. On the other hand, poorly-designed contracts can lead to overspending and compromised performance. In 2017 Acme, a leading North American FMCG, designed a new contract model for managing its Third- Party Logistics (3PL) suppliers. The key differences introduced by the new contract were a stronger emphasis on cost savings, an enhanced review process of unbudgeted spending, and higher levels of risk/reward sharing. Acme is interested in learning whether the new contract created value for itself and the suppliers. To answer this question, we use the econometrics model, Difference-in-Differences (DiD). Our results show that despite the increase in 3PL spending over time, there is no evidence to suggest a causal impact of the new contract on the cost performance of the 3PLs. We then proceed with the discussion of how the new approach to governance provides some benefits to Acme since it promotes better alignment of financial incentives between the parties and protects the buyer against the risks of gross underperformance on the part of the supplier. Additionally, we develop a framework that helps the reader to understand the key structural elements of contracts that define the relationship between an FMCG buyer and a 3PL service provider. Such elements include Performance Measurements, Compensation/Incentive Structure, and Governance Process. We then describe the appropriate functional levers and the trade-offs that apply to each of the three structural elements of value- driven contracts. Thu, 09 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142920 2022-06-09T00:00:00Z https://hdl.handle.net/1721.1/142919 Spare Parts Predictive Analytics for Telecommunications Company Mamakos, Alexandros Spare parts management is the backbone of asset intensive industries such as telecommunications companies, which operate in a highly competitive environment. Network reliability is a strategic goal as it ensures high customer service level and connectivity. Although companies utilize information related to the expected life of assets and plan maintenance activities, unplanned maintenance is still driven ad hoc. This has an impact not only on the company’s operations, inventory levels and cost but also on customers’ satisfaction. This capstone studies how telecommunications companies can improve the prediction of site failures and introduces a proactive maintenance approach. Based on our sponsor’s pilot project, we apply the MIT’s digital supply chain framework to define the value proposition and use the last 3 years of data to develop predictive models for site failures. To approach this case, we start by using the k-means algorithm and cluster the sites in three groups based on variability and demand for spare parts. To predict site failures, we apply time series models (exponential smoothing, Holt Winters and ARIMA) and assess the forecast accuracy based on RMSE and MAPE. In the last stage, we use supervised machine learning classification algorithms (Naive Bayes, Decision Tree, and Random Forest) and assess the accuracy using the correlation matrix. Based on our pilot project, we found that, while time series have a high percentage of error, machine learning algorithms can predict assets failures with accuracy between 60% to 85% and drive predictive maintenance and reduction of inventory levels and ageing. Nevertheless, companies should consider high quality and real time data prerequisites for machine learning. Our findings can be useful for other asset intensive companies that currently use traditional maintenance methods and are seeking to improve their predictive capabilities Thu, 09 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142919 2022-06-09T00:00:00Z https://hdl.handle.net/1721.1/142918 Efficiency and the Mitigation of Carbon Emissions in Semi-Truck Transportation Wang, Nan; Hughes, Brody Will As the fuel economy of vehicles increases, both costs and carbon emissions can be reduced. However, understanding what drives the fuel economy of semi-trucks is a challenging feat due to the number of variables involved. This capstone analyzed 21 variables related to the efficiency of semi-trucks to determine how they interact with one another, and furthermore, to quantify and rank their respective impacts on the fuel economy of semi-trucks. To perform this analysis, a dataset comprising these 21 variables across 2,606 semi-trucks over five years of time was used. The nonlinearity of the data necessitated the implementation of machine learning methods, such as K Neighbors Classifier and Random Forest. In addition to this dataset, 12 months of data from an electric semi-truck was studied to understand the current feasibility of this technology. This capstone’s analysis revealed that miles accumulated on tires and vehicle weight have the most significant impact on fuel economy. In finding these results, the K Neighbors Classifier model achieved an accuracy of 64%. The conclusions reached through this research can be utilized by the sponsoring company, and their partners, to improve the fuel economy of their respective fleets. By focusing on these specific variables, the resulting increase in fuel economy will lead to both decreased carbon emissions and expenses on fuel. Additionally, it will lead to the greatest return on investment with their expenditures on vehicle modifications, in terms of dollars spent to miles per gallon realized. The results from the electric vehicle analysis were promising, with the technology showing the potential to increase vehicle efficiency by 35%. This discovery, coupled with the lower cost of electricity compared to diesel fuel, paves an auspicious road for electric semi-trucks if the many current infeasibilities, namely traveling distances possible and long charging times, can be solved. Thu, 09 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142918 2022-06-09T00:00:00Z https://hdl.handle.net/1721.1/142917 Using a Gaussian Mixture Model to measure transit time bimodality and its impact on inventory decisions Dai, Didi; Jayantha, Aravindan Companies make inventory decisions based on well-established safety stock methodologies. In these methodologies, a key assumption is that transit times are normally distributed. Although previous studies have shown a nonnormality in transit time distributions in ocean freight, it is still unclear how transit time is distributed in land freight and how much less inventory a company could hold if transit time estimates were more accurate. Moreover, while safety stock methodologies are accepted practice, the inputs used in them are sometimes sourced from static and unsophisticated transit timetables. To address these limitations, this study conducted a distribution analysis and hypothesis testing on geolocation data captured by the sponsoring company, project44, a supply chain visibility provider. The analysis revealed differences in day- of-the-week transit time distributions. Using a Gaussian Mixture Model, this research also studied day-of- the-week transit time bimodality in detail. It was found that the majority of the first distribution had low dispersion around the mean and the second distribution grouped all long-tail transit times, with typically higher standard deviations as a result. This trend is particularly strong in intrastate full truck load shipping. Furthermore, Monday and Tuesday transit times show lower spread in means and have less variation across transit times. In contrast, the rest of the week has considerably higher spread in transit time distributions. This study shows that the full truck load freight is bimodal. Companies accounting for day of the week and transit time bimodality could reduce safety stock and therefore lower inventory cost by up to16% through forward planning and making orders earlier in the week. Thu, 09 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142917 2022-06-09T00:00:00Z https://hdl.handle.net/1721.1/142916 Trash or Treasure: An Analysis of Airline Catering Food Waste Chen, Meiling; Hidalgo, Joaquin With air passenger traffic projected to increase significantly over the following decades, the airline catering industry urgently needs to explore the underlying causes of food waste produced in their kitchens and develop concrete strategies to manage it better going forward. Spanning over 60+ countries, operating 200+ catering units, and serving more than 700 million passengers every year, the sponsoring company for this capstone project, a leading airline catering company, is uniquely positioned to make a sizeable impact on this global issue. Considering that the in-flight catering services industry was valued at $17.7 billion in 2018, airline catering food waste was reckoned with millions of worth. In this report, we proposed innovative solutions to reduce the food waste in their catering kitchens, considering the potential financial and environmental impacts of improved food waste management. We combined data analytics and machine learning algorithms with system dynamics frameworks to minimize cost and maximize the utilization and preservation of resources. We modeled the system with a 91.12% accuracy and evaluated eight different waste management alternatives regarding the cost and environmental impact. Through our study, we provided the sponsoring company with actionable, data-driven recommendations to aid in developing their first attempt to create a global organic waste management strategy. Thu, 09 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142916 2022-06-09T00:00:00Z https://hdl.handle.net/1721.1/142914 CHEAPER AND BETTER: OPTIMIZING E-COMMERCE PRODUCT RETURNS MANAGEMENT Chen Suat Teng, Felicia; Singh, Tejinder With the rise of online retailers, people are buying products online more than ever. This has intensified the competition in the e-commerce market. Online retailers are focussing on improved efficiencies in the way they deliver the products with exceptional customer service. Over the past decade, the online retailing industry has come a long way from changing customers’ mindset to prefer online buying over buying from physical stores to the prevalent 10-minute deliveries at present. Amid all the innovations in this space, a major cost element of handling product returns usually gets neglected, despite the fact that return costs constitute 10-15% of the overall revenues. Our research is aimed at helping Lazada group, one of the largest e-commerce players in Southeast Asia, reduce its product return costs. To understand the existing process, we conducted several interviews with the Lazada team. Based on the inputs received from the interviews, we built a Python- based analytical model, encompassing all the logistics and product costs. We validated this model by comparing cost results with the historical data spanning 2021. Once the model represented the reality in terms of product returns and costs, we analysed the current product return process and identified the changes that could help Lazada reduce returns costs. To ascertain whether the recommendations would be effective, we ran several simulations on each of the recommendations, i.e. potential scenarios, to measure their effectiveness. These scenarios included varying the limit for no quality control price, varying the salvage value extracted from the returned products and changing various final decision outcomes. Although this project focuses on Lazada group, this model can be used for optimizing product returns for any online player by simulating various decision nodes and outcomes. Wed, 08 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142914 2022-06-08T00:00:00Z https://hdl.handle.net/1721.1/142913 Evaluating Supply Chain Sustainability through Triple Bottom Line Arnold, Katherine; Konopinski, Lauren Avetta, a supply chain risk management & compliance firm, is introducing a new ESG (Environmental, Social, & Governance) offering to better support clients on their sustainability journeys, as they look to increase transparency and ensure compliance across the supply chain. In support of this new program, Avetta requires an understanding of the most critical sustainability policies and practices. This research identifies and ranks the key critical success factors (CSFs) that determine supply chain sustainability performance across five key industries (agriculture, construction, extraction, manufacturing, and retail). A Triple Bottom Line (TBL) approach – encompassing environmental, social, and economic perspectives – provides a comprehensive evaluation of supply chain sustainability. A thorough literature review was conducted to collect and define common critical success factors within each of the TBL buckets: ten environmental CSFs with thirty-one subfactors, eleven social CSFs with twenty-eight subfactors, and five economic CSFs with eleven subfactors. Analytical Hierarchy Process (AHP) was employed to rank the CSFs in terms of relative importance based on the results of an expert questionnaire. Results of the AHP analysis were further supported with findings and insights from Avetta’s supplier responses to detailed sustainability survey questions. By looking holistically at supply chain sustainability key criteria and success factors across a specific selection of industries, this research provides a baseline point of reference for managing supply chain sustainability. Wed, 08 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142913 2022-06-08T00:00:00Z https://hdl.handle.net/1721.1/142912 Get Smart: Reinventing Plastic Recycling in a Collaborative, Circular Supply Chain Benitez, Pedro; Critchlow, Kenneth Plastic waste has been a growing source of environmental concern, and the CPG industry is the largest consumer of plastic while also having the highest plastic consumption to waste ratio. Consequently, under greater levels of public scrutiny, many CPG firms have publicly committed to source fractions of their plastic consumption from recycled plastic sources. Despite these intentions, current quantities of recycled plastic are substantially less than what is required to meet these goals, in part due to low recycling participation rates and inefficiencies of traditional recycling methods. To address this challenge, we evaluated three different types of circular supply chain networks, reliant on collaboration between material recovery facilities (MRFs) and Amazon last-mile delivery facilities (LMDs). These networks utilize empty backhauls of Amazon delivery vans to collect plastic waste from households, which is then consolidated at Amazon LMDs and shipped to MRFs. Furthermore, we introduce a new, theoretical facility referred to as a Smart Material Recovery Facility (SMRF), which is constructed adjacent to Amazon LMDs, and designed to process specific types of plastic material. The goal of this research is to determine the optimal network design that maximizes total system profit and quantifies the distribution of costs and profits of participating actors. To this end, we conducted a case study based in the State of New Jersey, and through mixed integer linear programming, we compared networks using no SMRFs, only SMRFs, and an unconstrained number of SMRFs with respect to optimal profitability followed by sensitivity analysis and Monte-Carlo simulation to understand network behavior and robustness to variability. In doing so, we determined that the network with unconstrained selection of SMRFs was the most profitable, presenting positive annual net profits, and a robust network of one SMRF and six MRFs that were able to capture the supply of plastic entering the network. The results of this case study present the groundwork for further network evaluations and present an opportunity for collaboration between the CPG industry, Amazon, MRFs and SMRFs in the development of these circular supply chain networks. Wed, 08 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142912 2022-06-08T00:00:00Z https://hdl.handle.net/1721.1/142911 Enhancing Supply Chain Operating Models Through Segmentation AlArfaj, Ibrahim; Arslan, Yalcin Large Food and Beverage retail chains often manage diverse sets of products and markets, where one- size-fits-all supply chain operating models are insufficient to meet their distinct requirements. In collaboration with a global retailer, the main objective of this study is to identify distinguishable supply chain segments based on product and market characteristics and design an alternative supply chain operating model (SCOM) for each segment. To achieve this, a five-step, integrated, data-driven methodology is designed. First, data is gathered and reviewed for accuracy and completeness. Second, data is analyzed to identify potential segmentation criteria and select the most relevant factors. Third, k- means clustering is applied to create the product segments. Fourth, a SCOM is designed for each segment based on the product characteristics. Finally, the SCOMs are simulated to analyze how they perform in different scenarios. Applying the methodology resulted in three segments differentiated based on the products’ demand volume, demand volatility, shelf-life, item cost, and seasonality. The three segments are slow-moving, fast-moving, and complex items. Each segment was recommended to be managed using different inventory and forecasting policies. Using simulation and scenario analysis, several service level targets were tested to show how they impact inventory costs, transportation costs, and fill rate. As a result, the SCOM for each operating model brings benefits to the overall performance. Focusing on this, slow-moving products are not delivered frequently, hence eliminating their inventories in the DCs is expected to reduce the inventory holding cost without significantly increasing the transportation cost and decreasing service levels. Disaggregating the inventory in the CDCs for fast-moving items is expected to improve service levels for these items, with a low increase in inventory costs. Lastly, aggregating the demand for complex items is expected to reduce the risks of stockout and excess inventory. The methodology in this study can be generalized to other industries with high product variety to enable them to reduce inventory, improve service level, and reduce the total distance traveled. Wed, 08 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142911 2022-06-08T00:00:00Z https://hdl.handle.net/1721.1/142910 Improving Service Level through Component Inventory Management Ochsenius, Paula; Woolley-MacMath, Liam In every company, determining an optimal inventory level balances the desired service level with the costs of holding too much inventory. For the medical device industry, the stakes are high as risks in the supply chain of devices used by hospitals and operating rooms can have a devastating impact. In this capstone, we focus specifically on component inventory sourced from a variety of suppliers that are used for assembly and finished goods production by our sponsoring medical device company. Beyond the typical safety stock formula that incorporates only consumption and lead times and a target service level, we evaluate additional planning levers that impact supplier service level. We interviewed and surveyed suppliers regarding their forecasting, production planning, and internal inventory management practices, and incorporated both qualitative and quantitative elements into our analysis of the key focus areas for improving service levels with our sponsoring company’s component suppliers. We discuss key actions that can be taken related to the sponsor’s supplier planning portal in the areas of forecasting, frozen period planning, and supplier evaluations that will reduce component service level risk in future planning periods. These actions are likely applicable to any medical devices manufacturer who experiences similar inventory challenges within their component supply chain. Wed, 08 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142910 2022-06-08T00:00:00Z https://hdl.handle.net/1721.1/142909 Integrating Safety Stock Policies into Roche’s S&OP Process Escuder Rebori, Matias; Sai Supraja, Rao Karanam Globalization, demand uncertainty, and shorter life cycles have increased the risks in pharmaceutical supply chains. To mitigate these risks, firms can carry safety stock. Classic theory on stochastic safety stock strategies assume that demand forecast errors are normally distributed with no bias or, in other words, have an expected value equal to zero. This assumption does not hold when considering over-optimistic, or positively biased, demand forecast, which is a common issue, as indicated by the prevalence of Sales and Operations Planning (S&OP) efforts. We began exploration of the biased forecast impact on safety stock for our sponsor company by understanding the managerial situation. To better frame the problem, we developed a conceptual model of the overall S&OP process based on responses to interviews with the company teams that influence the safety stock target definition. The conceptual model informed a formal model that we used to test the impact of a new safety stock formula that addresses forecast bias. Our results show that even though safety stock can be adjusted with this new approach, there are still many opportunities for improvement along this process. We conclude that in order to make the best informed decision about safety stock levels, Roche’s team should better integrate safety stock decisions into their S&OP process. Also, effort should be allocated to understanding which data is being used, what it means, and whether it is appropriately informing inventory decisions made explicitly by managers or implicitly in information systems. Finally, further analysis shows there is much greater potential to reduce inventory beyond that dictated by safety stock policy. Roche should continue working towards understanding the root causes behind their excess of inventory to achieve long-term substantial impact. Wed, 08 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142909 2022-06-08T00:00:00Z https://hdl.handle.net/1721.1/142908 DDMRP Feasibility Assessment in the Pharmaceutical Industry Lin, Jui Han; Wai-En Tchen, Michael DDMRP, a supply chain methodology introduced in 2011, aligns planning activities around incoming demand signals without the use of forecasts to predict future demand. The Demand Driven Institute (DDI) claims that DDMRP can reduce median inventory le vels by 31%, improve median service levels by 13%, and reduce the order lead time. This capstone explores the DDMRP framework to assess the feasibility and the potential value-added of adopting this methodology in an established supply chain. A simulation model was built to test DDMRP in a multi-echelon environment and quantify the impact of altering planning parameters. This simulation model was then extended to match the specifications of one of the partner company’s supply chains to compare the relevant metrics to their existing key performance indicators. The research identified several difficulties experienced when DDMRP is adopted in this simulated complex and highly constrained supply chain. These issues must be taken into consideration before full-scale implementation. Tue, 07 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142908 2022-06-07T00:00:00Z https://hdl.handle.net/1721.1/142907 Designing Inventory Management Strategy for a Fill Rate of 98% Sia, Deviana; Chhabria, Ashish Order fill rate is a critical performance metric in retail supply chain operations. Retailers use it to ensure deliveries received from their suppliers are in full quantities as per order. The retailers levy fines on suppliers that fail to comply with the metric. C.H. Robinson has a division that provides retail consolidation services to multiple suppliers. It arranges consigned inventory from multiple suppliers, stores it, and ships it to retailers in full truckloads as per order demand. They are interested in designing an inventory strategy that ensures a 98% order fill rate, thereby minimizing fines charged by retailers. An inventory strategy is focused on three key aspects i.e., optimal review interval, order quantity, and safety stock requirement. This project uses historical order and inventory data provided by C.H. Robinson to design an inventory strategy. The methodology taken is to narrow the focus down to 50 top-selling SKUs out of a total of 3,769 that consistently represent a significant share of the total shipments out of the distribution center. Upon identification of top-selling SKUs, two steps are taken to build a strong foundation before creating an inventory strategy. A forecast is built using techniques such as autoregressive integrated moving average (ARIMA) and error trend and seasonality (ETS) to ascertain the historical volatility in demand. After which the research uses the forecast accuracy to build optimal inventory levels required to achieve order fill rate targets. Furthermore, SKUs that show similar characteristics in terms of fill rate, volatility, and forecast accuracy are segmented into three clusters using k-means clustering. Thereafter, a periodic review inventory control system is used to obtain the optimal review intervals, order quantity, and safety stock levels for each of the three clusters. The research paper suggests an optimal amount of inventory that C.H. Robinson should hold in its DC to ensure an order fill rate of 98%. It also compares it with existing inventory levels maintained at the DC for each cluster, and the corresponding fill rate performance for each cluster. Ultimately, the research paper explores the trade-off of higher inventory holding costs associated with maintaining inventory levels geared towards achieving a 98% order fill rate performance. The research paper also provides C.H. Robinson with a framework they can use to make the best financial decision, given the trade-off mentioned above. Tue, 07 Jun 2022 00:00:00 GMT https://hdl.handle.net/1721.1/142907 2022-06-07T00:00:00Z https://hdl.handle.net/1721.1/131055 Supply Chain Simulation for Production Strategy Evaluation Ballali, Catherine; Tan, Rui Yin The Consumer Product Goods (CPG) industry such as the bottled water business is subject to bottlenecks, due in part to both product characteristics, stochastic nature of the demand of products, and customer lead time volatility. Nevertheless, CPG companies are expected to be able to serve customers that rely on their products, even as demand can be unpredictable and erratic. In CPG companies, where the multi-stock keeping units (SKUs) and multi-period nature of manufacturing systems are taking place, finding the right balance between Make-To-Order (MTO) and Make-To-Stock (MTS) production strategy proves difficult. To ensure customers' demand is fulfilled, this capstone analyzes the current production strategy of the capstone sponsor, a bottled water company, and incorporates the dynamic market demand and customer lead time volatility to determine the best production strategy that will be capable to meet 90% fulfilment rate In this capstone, we developed a System Dynamics (SD) and Discrete Event Simulation (DES) to understand the overall drivers of supply chain and production strategy that minimizes the total relevant costs (inventory holding and change over costs) whilst producing the highest fulfilment rate. We analyzed live orders, forecast orders, economic production quantity (EPQ), safety stock (SS) of 10 key SKUs and ABC SKU segmentation of 1300 SKUs for one production plant over the last year. Scenarios of demand, forecast and lead time uncertainty were simulated to provide insights into key drivers of the model behavior and guide insights into useful production policies. Our findings demonstrate that in manufacturing systems characterized by stochastic demand and volatile lead times, understanding SKU characteristics (EPQ, SS, and Inventory levels) is critical to meet market demand with the optimal cost more so than the order patterns. Mon, 28 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/131055 2021-06-28T00:00:00Z https://hdl.handle.net/1721.1/131054 Demand Forecasting for Food-Rations at the United Nations Darfur Mission Hollingsworth, Langdon (Landon); Xiang, Junlin (Shawn) Demand planning is a challenging component for organizations across a broad spectrum of industries. A key element of a successful demand plan is accurate forecasting, due in part to the operational decisions that are made based on the results of forecasting models. This is what our capstone project sponsor, Agility, has come to realize during their time-sensitive operations. Agility supplies food rations to the United Nations (UN) peacekeeping missions around the world. One particular mission, the mission in Darfur, or “UNAMID”, has a unique problem related to inaccurate forecasting. UNAMID places orders for food rations approximately 80 days in advance of when they are needed, but the lead time for Agility to source and deliver these items often exceeds 150 days. Therefore, forecasting is required to ensure that food rations can be procured and delivered on time. Currently, Agility uses a simple three-period moving average forecasting method, also known as MA(3). Due to frequent errors in the order quantity forecasted using this method, Agility often incurs stiff penalties from the UN for delivering too little or too much food. This study explores how sophisticated forecasting techniques can be applied to reduce penalty costs. First, we segmented the historical order quantity data to isolate the most important SKUs. Second, we tested various forecasting methods against the currently used MA(3) to determine if a more sophisticated model would produce better results. Third, we applied the Holt-Winters Forecasting model and optimized the parameters using non-linear optimization to maximize statistical accuracy. Fourth, we added penalty costs to the model and re-optimized the parameters to minimize the projected penalty cost. Fifth, we provided a set of strategic recommendations for how Agility can use the results of this study to realize these cost savings. We found that by using our optimized Holt-Winters forecasting model, Agility could likely save at least $25,000 per year in penalty costs at UNAMID. An additional study is recommended to explore how this model can be applied to further increase cost savings at other UN peacekeeping missions. Mon, 28 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/131054 2021-06-28T00:00:00Z https://hdl.handle.net/1721.1/130988 Digital Transformation for Flexible Last Mile Distribution Kuppuswamy, Krishna V Flexibility in last-mile distribution has become a key differentiator for companies that are obsessed with driving up customer experience. For most fast-moving consumer goods (FMCG) companies like BeverageCo, it is crucial to succeed in fast-growing emerging economies which are dominated by the traditional retail channel, characterized by small stores. With this channel contributing to more than 90% of BeverageCo’s customer base, and its tremendous growth potential, digital transformation to drive flexibility in last-mile distribution is no longer a choice but a mandate. However, the path to digital transformation and the metrics to measure the ‘value’ behind flexible last-mile distribution remains a challenge for BeverageCo. With ‘value creation’ at the core of this transformation, this project implements a Value Stream Mapping (VSM) methodology to map the current and future state of distribution value stream. VSM helps identify the gaps, system inefficiencies, and the metrics impacted in the current state; leverages simulation-based future state design and a multi-criteria-decision-model to assess the digital capabilities required for the transformation. This approach resulted in the design of a 4 to 6-hour distribution model (as against the current 31-hour distribution), with minimal changes to the processes and investments required to run the operations. With clear definition of metrics to measure ‘value’ behind flexibility, the outcomes suggest that more than 90% of the customers will benefit from this flexible and expedited distribution, at a high value-to-cost ratio. This project unlocks the value of digitalization with a frugal investment of time and money versus conventional large scale business transformations. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130988 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130987 Optimal Production Planning Strategies for Global CPG Company Sakr, Omar Mahmoud Companies in the Consumer-Packaged Goods Industry are faced with a chronic dilemma: efficiency vs. agility. Companies must find the balance between scale, which creates cost-saving opportunities, and flexibility, which often incurs incremental costs. The main questions addressed in this capstone pertain to manufacturing and logistics decisions. Simply put, how much should the company produce, when and how, and therefore how much inventory should they hold? Following extensive cost structure analysis and mapping of the company’s supply chain network, a comprehensive Mixed Integer Linear Programming model was created. The model’s objective function is cost minimization, which it must attempt considering “current-state” inputs as well as multiple operational constraints. The results suggest that a hybrid production planning strategy between “level-production” and “demand-chase” is preferred, and can generate significant cost savings across the supply chain. In summary, this strategy can help companies enhance their efficiency and reduce costs when attempting to optimize total end-to-end supply chain costs, instead of using department-based budget management. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130987 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130986 Identifying the Root Causes of Stockout Events in e-commerce Using Machine Learning Techniques dos Santos Izaguirre, Federico Guillermo; Chao, Tzu-Ning The year 2020 marked an unprecedented worldwide growth in e-commerce driven mainly by the COVID- 19 pandemic. The lockdown restrictions created significant spikes in the demand for several products causing severe disruptions throughout the supply chains. The pandemic created significant challenges for companies to maintain efficiency in the supply chain and product availability on the digital shelves. Stockout events rose considerably in the online platforms, and companies across industries needed to find ways to address the problem. The focus of this project was to identify the main reasons that lead to stockouts for the sponsoring company to a major online retailer and to develop a model to predict the stockouts. Using supervised machine learning models, we developed a model that predicts the missing order quantities for every specific order. The analysis shows that the variables associated to the demand such as order quantity have a higher impact than variables associated with the supply, such as inventory on hand. Additionally, the product categories and brands associated with each category play an important role in the stockout prediction. With the continued growth of e-commerce and customers changing their shopping preferences, our predictive model will help the sponsoring company analyze the orders and make informative decisions to predict the stockouts and improve the inventory allocation. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130986 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130985 The Impact of Trade Credits in Nanostore Distribution Mogollon Linares, Marcos; Stimpson, Blake In developing countries, small mom-and-pop grocery stores called nanostores are one of the main grocery market channels. Cash constraints are a severe issue that affects these small businesses, preventing them from buying enough inventory to meet client demand, and forcing them to reject orders from suppliers due to a lack of cash to pay the supplier on delivery. These cash constraints also impose challenges for the suppliers of these stores, by extending the duration of individual visits to nanostores as a result of cash handling, increasing product rejections and reducing the service level consumers experience. Our research explores the effects of relieving these cash constraints via trade credits, using historical data from the sponsor company and a variety of econometric techniques. Our analysis indicates that a supplier trade credit policy, where nanostores are granted short-term deferral for product payments, can significantly boost revenue and generate logistics cost savings. As a result, the return on investment of this policy is positive as early as the first month of implementation. In addition to the clear benefits for the business, this policy can help the traditional nanostore grocery channel remain competitive in developing countries. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130985 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130984 Increasing Resilience Through Advanced Analytics in a Pharmaceutical Company Chen, Danning; Anzola, Valentina COVID-19 was a major pandemic that struck the world at the beginning of the year 2020. Many companies suffered sudden disruptions in their manufacturing operations, logistics and even in their capacity to reach their customers. This capstone project addressed the need of a global pharmaceutical company to understand what digital capabilities were required to be more resilient. The research team conducted in depth interviews and reviewed the literature on resilience: it was identified that transparency and advanced analytics are the main digital capabilities that can increase resilience. Then the research team implemented machine learning techniques to demonstrate how the utilization of advanced analytics can improve resilience. For this analysis, the research team implemented decision trees and random forest in two different datasets from 2019 and 2020 to draw conclusions about what influenced the company’s ability to fulfill orders under a normal state and a disruptive state, as a measure of resilience. The results of this analysis showed that order quantity, location population, and the category of products by level of sales are features that help determine the potential disruption of fulfillment orders; this knowledge can help increase resiliency. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130984 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130983 Portfolio Modeling and Forecasting of Single-Use Rare Disease Treatments Leising, Jordan Michael; Goldman, Olivia Claire Biopharmaceutical companies are increasingly exploring cutting-edge novel gene therapies (GTs) in an effort to cure rare diseases. This capstone develops and tests a practical forecasting framework for sharing capacity across Roche’s evolving GT portfolio and driving strategic global supply chain network design. Our problem is challenging, even by the highly regulated pharmaceutical industry standards, with: (1) substantial R&D and mergers and acquisitions investments, (2) some of the world’s smallest disease populations, (3) one-time patients, (4) lacking commercial infrastructure, and (5) scarce historical or long- term pipeline data. We created three forecast types based on the target disease state knowledge available to predict an asset’s prevalence and incidence patient adoption curves. The resulting asset forecasts are also aggregated into a comprehensive portfolio dashboard. Our user-friendly point model enables stakeholders to market size the prospective current pipeline and risk pool portfolio capacity by clinical phase. We then applied simulations to illustrate long-term product launch scenarios. These tools cater to various stakeholders helping address the key GT production planning and asset targeting problems. Roche has already began utilizing our capstone to methodically consider unknown future assets, with unknown orphan disease severity or populations, in their strategic make vs. buy GT network design decisions. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130983 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130982 Tradeoffs in Strategic Capacity Planning under Demand Uncertainty Rose, James William; Stolz, Matthias Companies are often faced with the need to make supply chain investment decisions to support new product introductions while there is still significant uncertainty in the demand expectations. This situation forces tradeoffs in supply chain capacity investments versus the risk of lost sales. To address this tradeoff, we have proposed a methodology that leverages expert input for quantifying demand uncertainty assumptions, simulation to develop demand scenarios that combine existing products and new product introductions, Mixed Integer Linear Programming optimization to determine the most efficient investment strategy for each demand scenario and visualizations to enable the practical exploration and comparison of a large number of optimized scenarios, uncovering more profound insights into the tradeoffs between supply chain investments and the risk of lost sales. Our work shows that incorporating demand uncertainty into a robust scenario analysis can reveal the capacity bottlenecks and available opportunities of a companies’ supply chain to meet increasing demand. Furthermore, we discovered that the most cost-effective strategies for covering demand are not always intuitive. Lastly, we demonstrated the power of visualizations in identifying the less intuitive tradeoffs that cannot be identified with more simplistic tools. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130982 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130981 Leveraging Predictive Analytics to Assess Operations Metrics Kong, Chiwei; Artman, Nicholas Charles Samuel Many firms rely on key performance indicators (KPIs) to manage their business. Though countless metrics exist, it can be difficult for companies to identify which metrics are driving their performance. This is problematic within industry, as insignificant KPIs can lead to misguided management insights. This research analyzes how companies and organizations can assess operational metrics utilizing predictive analytics. Additionally, it shows how firms can leverage their metrics to prepare for future objectives and identify key predictive indicators. This analysis comprises an assortment of predictive modeling techniques to evaluate manufacturing and inventory metrics for a firm identified as Company XYZ. These modeling approaches include multiple linear regression, random forest, LASSO regression, and backward elimination. Our analysis found four of the ten performance metrics reviewed to be significant in predicting a production efficiency metric. Using these four metrics, we applied a multiple linear regression model to assign coefficients that could be leveraged for sensitivity analysis. Our results ultimately identified key predictive indicators and created sensitivity analysis to help management teams prepare for future endeavors. This research demonstrates that predictive analytics can be used as a fast and cost- effective approach for companies to review their performance metrics. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130981 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130980 Supply chain segmentation in the apparel industry Ptok, Fabian Lucas; Camargo Henao, Jonathan Eduardo The nature of the apparel industry is enigmatic. Customers want differentiated products and agility, yet apparel is mainly produced in low-income countries with long lead times to compete on cost. This mismatch leads to poor planning, simultaneous over- and understocking, resulting in markdowns and lost sales. Supply chain segmentation has been widely used to combat this problem. Companies differentiate based on fashion and basic items and use agile and efficient supply chains, respectively. In this capstone project, we used segmentation to redefine the supply chain strategy for the company BAGSZCORP, a leading backpack manufacturer in Europe. In 10 years, BAGSZCORP has grown from a startup producing backpacks for school children to a portfolio of multiple brands, with numerous additional categories such as footwear, and an omnichannel retail strategy. This growth, however, has been fueled by a one-size-fits-all supply chain strategy. As a result, the company has seen operational inefficiencies and low inventory turnover despite their enormous revenue growth. By segmenting products and customers based on demand and variability, we discovered an inventory reduction potential of 6,2 million Euro for BAGSZCORP. Various strategies including production in Europe and increased safety stock of raw materials, are discussed in context of the apparel industry in general. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130980 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130979 MIT Campus PPE Demand Planning Sorel, Kelly; Gao, Song The COVID-19 pandemic created a global shortage of personal protective equipment (PPE) and cleaning supplies. This environment motivated the Massachusetts Institute of Technology (MIT) to reconsider its decentralized procurement model and develop a temporary, centralized, and more sustainable model for the sourcing and distribution of these items to enable safe on-campus activity. However, the determination of demand needed to inform this procurement model was not uniform, consistent, or based on actual need. This capstone identifies and outlines the strategies for requesting, receiving, and using PPE and cleaning supplies at the department level, and seeks to help departments estimate when and how much PPE and cleaning supplies to order. To achieve these objectives, we analyzed order history data and interviewed key stakeholders on campus. From the qualitative and quantitative data collected, we ascertained and mapped different strategies departments utilized to manage their PPE and cleaning supplies needs, gained further insight into the motivations and purchasing behavior of different departments, and assessed how the new, centralized model performed in fulfilling demand. Finally, we utilized data in developing a PPE calculator and proposing a demand plan to support the new, centralized process. This demand plan, along with the departments’ use of our PPE calculator, contributed to development of a strategy and framework to guide more sustainable campus PPE and cleaning supplies procurement and allocation. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130979 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130978 Optimizing the Logistics Network for Pipeline Inspection Scutari, Alessandro; Kosansky, Aviva Tova The sponsor company for this capstone has a complex logistics network due to the fact they use a service model for their products. Their products use advanced technology to service water, wastewater, oil, and gas pipelines across the world. This requires managing the movement of these products from their inventory holding locations to the customer site in both the forward and reverse directions, as well as for maintenance operations. The goal of this capstone is to assess a supply chain network and make recommendations for the sponsor company to minimize logistics costs while maintaining high service levels and high product utilization rates. We optimized the current network using the uncapacitated Facility Location Problem. This allowed us to identify the number of facilities required to serve all the North America projects and cluster the demand geographically. Based on historical data, we then used forecasting techniques to establish an inventory policy for the product families under analysis for every inventory holding location. The proposed optimized network could lead to a total mileage reduction of 20%, reducing cross-border shipments and streamlining operations. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130978 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130977 Improving survival of micro & small firms in Latin America during COVID-19 via SRM and CRM strategies Illipronti, Rafael Grillo Apart from health impacts, the coronavirus pandemic brought many economic challenges for mi- cro and small enterprises (MSEs), especially in Latin America, where they account for most of the firms. For these cash-constrained companies, the combination of lower sales, higher costs of supplies, and ad- vanced payment consumes cash and diminishes their chances of businesses continuity. We approached the problem of how to increase MSEs’ chances of survival from a supplier relationship management and customer relationship management standpoint. Our goal was to determine the most effective time to pay suppliers and collect from customers, and what types of relationships could achieve those times. We mod- eled the cash flow between supply chain echelons to evaluate different payment-term configurations and identify trade-offs and optimization opportunities. We found that via collaboration with vendors and cus- tomers the times to collect cash can be modified in MSEs’ favor. Increased time to pay suppliers frees up cash, which MSEs can reinvest to purchase more materials and grow sales. When accompanied by an in- crease in sales beyond a breakeven point, the payment time increase supports a win-win situation: suppli- ers see a net-zero or net-positive impact, and MSEs can expect value creation of up to 17% from working capital reduction and profit growth. Therefore, the adoption of collaborative relationships with suppliers and customers may increase the likelihood of business continuity—not only during times of crisis but also in periods of relative normality. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130977 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130976 Carbon Efficient Network Design: Evaluating The Trade-Offs Between Carbon Emissions, Transportation Cost and Delivery Time For a Middle-Mile Distribution Network Alamsyah, Ars-Vita; Purevdorj, Namuun The transportation sector is the third largest contributor to greenhouse gas (GHG) emissions, accounting for over 28% of total U.S. emissions and over 14% of global emissions in 2018. As climate change and its negative effects have grown significantly, efforts to reduce GHG emissions have become an important objective on a global, national, and corporate scale. One approach is to design a distribution network that minimizes transportation carbon emissions while meeting its primary Key Performance Indicators (KPI). It is important for companies to understand the trade-offs between those KPIs, which may include minimizing the carbon footprint, minimizing transportation costs, and meeting the network's delivery time requirements. This research introduces a multi-echelon Green facility location problem (FLP) that focuses on the middle-mile. It incorporates intermodal and alternative-fuel transportation modes and optimizes for the tri-objective of variable transportation cost, delivery time and carbon emissions. By using the ɛ-constraint method, Pareto frontiers for carbon emissions vs. cost and carbon emissions vs. delivery time were plotted, providing the trade-offs between objectives. Optimal scenarios on the Pareto frontiers were identified to align with 1.5oC and well-below 2oC global climate scenarios according to the Science-Based Targets initiatives. The research team found that the trade-offs between carbon emissions vs. delivery time are non-linear and much more significant than carbon emissions vs. transportation variable cost. In order for firms to reach Science-Based Targets, it is necessary for companies that have transportation heavy operations with short delivery timelines to shift all transportation to vehicles powered by lower carbon fuels. This research informs the approach to start incorporating environmental considerations in the strategic decision-making process for supply chain network design. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130976 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130975 Developing a Digital Solution to Container Triangulation in China Feng, Jieming; Moreno Sanchez Briseno, Mauricio Improving efficiency and sustainability in logistics and transportation has been a strategic priority for companies and countries as they compete in the era of globalization. However, how to optimize the container transportation and improve container turnaround has become an increasing challenge for the industry, especially in the growing trade imbalances and more frequent disruptions. To overcome this challenge, container triangulation offers remarkable opportunities to the carriers to reduce the transportation of the empty containers, and therefore, improve the turnaround. Container triangulation can be identified as the reuse of the import containers for export. Despite a number of potential benefits of container triangulation may offer, it is challenging to scale-up in China due to the fragmented market and the lack of accurate location data. To focus on this challenge, this research investigates the digitalization of container triangulation as an alternative solution, where matching decisions are automated in a digital platform. This research examines the current process and challenges of automating container triangulation in China for Maersk and explores how to optimize and accelerate this solution. With this motivation, we conducted expert meetings, analyzed data, and applied machine learning algorithms and mixed-integer linear programming to enable container triangulation routing optimization on the company's digital platform. The result showed a trucking cost savings from 11% - 14%, a transportation lead time reduction from 8% - 10%, and a reduction in CO2 emissions from 8% - 10%. However, the savings would be further reduced with more restrictive conditions for execution. To scale up the solution, we recommend the cooperation of different parties of the container transport industry to share the incentives and adopt the digital solution. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130975 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130974 Power Influence in Horizontal Collaboration Relationships Suarez Moreno, Juan David Supply chain horizontal collaboration has captured the attention of many researchers and practitioners. Horizontal collaboration offers multiple benefits in creating competitive advantages for companies and leveraging their sustainability in the long term. Although collaboration creates value for the supply chain, there is no evidence of what makes companies adopt these schemes since many of these initiatives fail to deliver the expected outcomes. In the core of the collaboration process lies power as an enabler since collaboration relationships arise from the inter-dependency between companies. This research explores the influence of power in the performance of horizontal collaboration. Using data from the Colombian Ministry of Transportation, a set of 3,276 dyads and 1,095 single companies were identified as performing consolidation during the year 2020. Three different power asymmetries were built to characterize power among these dyads: income, cargo, and network asymmetries. The effect of power asymmetries was evaluated on two outcome variables: the number of consolidated shipments and the shipping cost per kg. To do this, the augmented inverse propensity weight estimator method (AIPW) is used to analyze the average treatment effects empirically. A set of 16 experiments were conducted to understand the influence of the different asymmetries in the horizontal collaboration performance. The statistically significant results show that power asymmetries have a negative effect on the number of consolidated shipments, reducing them. However, different effects are account for the shipping cost per kg. Income and Network asymmetries have a positive effect, reducing the shipment cost. Cargo asymmetry has an opposite effect regarding the shipment cost as it is increased when asymmetry is increased. Significant results are found for network and cargo asymmetry on reducing the number of consolidated shipments. No significant effect is observable on the shipment cost when looking at the asymmetry in isolation. However several moderator effects were also tested under the different treatments. Better performance was achieved for those dyads with low network asymmetry, a greater shipped volume, a broader collaborative network, and industry compatibility. The different experimental settings demonstrate that power effect on the performance depends on the dyad’s relationship-specific features. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130974 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130973 Dynamics of Supply Chain Sustainability Pang, Jason Supply chain sustainability has grown steadily in the last half century. Companies today are setting more ambitious goals and investing greater resources. With this accelerated pace come new questions regarding the transparency of sustainability on an aggregate level. How and why have sustainability practices changed from 2020 to 2021? How has the COVID-19 pandemic impacted sustainability commitments by industry? Are there industry characteristics that can explain the differences in sustainability practices? To address these questions, our analysis used a combination of quantitative survey data and qualitative interviews with sustainability executives. We used non-parametric Mann Whitney U tests of Likert scale data to quantify the change in supply chain sustainability initiatives. To understand the COVID-19 impact, we visualized summary statistics with Tableau dashboards. Then we ran non-parametric Kruskal-Wallis ANOVA tests to determine if industries differed in their sustainability commitment due to COVID-19. Lastly, we ran unlabeled k-means clustering on the Likert scale data to establish new profiles of companies based on their unique levels of sustainability behaviors. We determined that supply chain sustainability on an aggregate level has continued to grow in 2021. Businesses doubled down on the issues most impacted by COVID-19: significant growth was seen in employee welfare and safety, human rights protection, and renewable energies. The fear of a sustainability retraction due to COVID-19 was unjustified. Both our executive interviews and empirical research showed that there was either no impact or an acceleration in sustainability promises. Our clustering identified six classes of industries based on their varying levels of sustainability commitment: Leaders, High Effort, Standard, Compliant, Dreamers, and Low Effort. The largest differentiators between the leaders in sustainability and those that lagged were company size and type. These clusters could be used in future research to understand the motivations for a company to adopt high standards in sustainability commitments. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130973 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130972 Micro-Fulfillment Feasibility for Metro Trade Area Transformation Zhu, Feng; Jarugumilli, Sai Priyanka The world’s largest coffee chain is looking to cater to different patterns of customer demand especially in metro areas that are densely packed with their stores. With more of the demand becoming mobile and customers gravitating towards curbside pick-up of orders, the leading coffee retailer is looking to redesign their cafes to cater exclusively to this segment of demand. To accommodate these redesigned smaller stores, the retailer now seeks to enable more frequent inventory distribution that can allow stores to meet the consumer demand while storing less inventory on hand. In this project, we investigated a new network design where the concept of micro-fulfillment centers is added to the existing network to enable this frequent distribution. By establishing Urban Distribution Centers (UDCs) in the metro markets, stores have the flexibility to place small orders and receive unit product deliveries more frequently instead of bulk deliveries. This project simulated four different scenarios while varying the number of distribution centers and proposed the use of larger stores as alternative distribution centers. We first performed a greenfield analysis to decide where and how many distribution centers to build. Following this, we simulated the scenarios to mimic the costs incurred for implementing micro-fulfillment. Finally, we performed a sensitivity analysis to understand the impact of delivering more than once a day and delivering different products at different times of the day on different cost parameters. As a result, we observed at least a 40% increase in total cost across all scenarios, predominantly due to investment in new infrastructure for UDCs. Despite the micro-fulfillment strategy requiring an initial monetary investment, it would enable the retailer to open distribution centers located closer to the stores. This strategy helps facilitate multiple deliveries of inventory to the stores while covering lesser total distance travelled and powers Starbucks with a flexibility in delivery to cater to urgent store orders. Finally, micro-fulfillment further paves the way for the retailer to transform its conventional cafes and open newer, smaller stores, delivering significant savings in rent and labor costs. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130972 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130971 The Secret Recipe for Modeling Warehouse Throughput DeSutter, Dana; Gao, Sherry Throughput is a critical performance metric for warehouse operations in the food industry. Accurate throughput estimations are necessary for effectively planning replenishments, inventory levels, and labor resources to meet the needs of customers. General Mills, who manages a large variety portfolio with different packaging, demand volatility, storage requirements, and outbound weight requirements, is interested in throughput estimation at their existing warehouses, also called Customer Service Facilities (CSFs). This project utilizes data collected from various data sources at General Mills to understand the factors that influence throughput. After interviewing key company stakeholders to learn more about warehouse operations, we collected and analyzed data. We developed a linear regression model, using machine learning to predict throughput. Ultimately, the analysis demonstrated that warehouse throughput at General Mills is not only impacted by internal factors, such as labor and product mix, but it is also impacted by external factors, such as day of the week, and higher demand requirements near quarter-end. With less than a year of data, the model still achieved a low mean absolute percentage error (MAPE) around 10%, implying highly accurate results. The strong forecast accuracy allows General Mills to create strategic plans to manage their labor constraints and improve the predictive performance of their throughput estimations. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130971 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130970 Inventory Management for Slow Moving and High Volatility Items Efendigi, Esat; Cameron, Kristin Katharine Inventory management is an essential operation in the supply chain, owing to its strategic importance in supporting item availability and business continuity. The demand for slow-moving items is fundamentally ambiguous compared to demand for traditional fast-moving items due to the irregular demand pattern of slow-moving items, which causes forecasting problems. Under these circumstances, companies choose to stock more inventory than needed to mitigate the risk of insufficient inventory levels for business continuity and the high service level requested by the customers. Our capstone sponsor Optimas, a distributor of fasteners, requires an inventory policy playbook for low-volume, high-volatility items for its customers with a high service level. Higher inventory levels cause unnecessary spending of working capital. We aimed to define the best inventory level for each active, slow-moving item with this capstone project after analyzing the intermittent demand of the last four years. The whole slow-moving portfolio was categorized according to order quantities per year. We used Croston’s Method and hybrid periodic review (R,s,S) policy for items in Class A, which are the most frequently ordered within the past year. For Classes B and C, we used statistical methods and periodic review (R,S) policies. The output of this process is a list of items along with the recommended inventory level, the current inventory position, and the quantity to order per item. The results show that using these recommendations, Optimas can save up to 50% of their total inventory cost while maintaining their customers’ required service level. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130970 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130969 Delivering Locally Sourced Nutritious Food to Indian Households Das, Sanchita WHO reports that in the South Asian region, the number of undernourished has hardly decreased in the last decade. This situation calls for a concerted effort to combat malnutrition the world over. The effort must be grounded in nutrition and executed through a robust distribution mechanism to reach all segments of the society. In this thesis, we take a step in that direction by combining expertise from the supply chain and nutrition areas to address protein-energy malnutrition among poor households in India. While the country is the largest producer of pulses, milk, and other dairy products, and many food grains, Indian diets are traditionally low in protein intake, especially among the poor. Within our scope of the problem, we target the poorest households in India, which currently hold the Antyodaya Anna Yojana ration cards from the Government of India. We develop a framework to improve their diet diversity nutritionally. We propose matching the demand of food (as recommended by Indian Council of Medical Research for a balanced diet) with locally available, culturally preferred supply by designing ‘customized food baskets’ for different consumer clusters. We suggest distributing the proposed food baskets at scale to all target households via the government Public Distribution System mechanism operational in India. We use PCA and K-means clustering to segment the customers, create a food basket model inspired by the knapsack problem, and use a Mixed Integer Linear optimization program to solve the distribution problem. The key contribution of this thesis is a framework of basket assortment and distribution. The approach is generalizable and can be used on many different customer types and (public or private) distribution channels to match demand with supply of nutritious assortments and enable delivery at scale. We can serve 65 to 75% of recommended daily quantity of cereals and pulses to our target households via the proposed framework. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130969 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130968 Advancing the Circular Economy of Plastics through eCommerce Backstrom, Jacob; Kumar, Niranjini The production growth and short life cycle of plastics has created concerns around its waste management process. Despite efforts to increase recycling over the years, the rate of recycling does not come close to the rate of plastics production. This capstone project develops an innovative and convenient business model that leverages the existing eCommerce logistics network to facilitate a closed-loop supply chain for recycling plastic packaging. We design the model based on the Extended Producer Responsibility (EPR) framework, to identify the key stakeholders and define their roles and responsibilities throughout the process. Stakeholder interviews were conducted for validation and feedback, and are incorporated to further refine the model. We develop a universal, adaptable, and scalable financial analysis tool to evaluate the economics of the model. Finally, we provide recommendations on a pilot test to launch, validate, and improve the developed plastics closed-loop supply chain model. Our results highlight that a multi- stakeholder coalition, with a high level of integration and engagement among all the stakeholders, is necessary to make this model a success. Through a collaborative approach, stakeholders can significantly increase the amount of plastics recycled, divert millions of metric tons of plastics from landfills and ocean waste, reduce the need for production of new plastics, and thus, promote sustainability. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130968 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130967 Fuel Efficiency and Safety in Coca-Cola FEMSA Last-Mile Logistics Torres Arpi Acero, Arturo; González Gil, Fernando Across industries and supply chains, the safety of drivers and efficient use of fuel by truck fleets are an increasing concern. This project focused on understanding driving styles, understanding the tradeoffs between safe and efficient driving styles, and finding the highest levels of safety and fuel efficiency. We worked with Coca-Cola FEMSA to analyze one year of telematics data from over 3,000 vehicles. To analyze the data, we employed a methodology that involved multiple machine learning and analytical techniques, including multiple regressions, a random forest classification algorithm, Bayesian Gaussian Mixture Model for clustering, what-if simulations, and the use of interactive data visualization tools. These techniques were used first to understand the main fuel efficiency drivers, then to understand the drivers of safety, and finally to understand the trade-offs between fuel efficiency and safety with respect to different driving styles. Our results show that significant gains can be achieved in terms of fuel efficiency by changing driving behaviors. Results from the regression and simulator show that average speed, acceleration events and maximum RPM are the 3 most important variables for fuel efficiency. With small changes like increasing speed by 1km/h, reduce acceleration events in 5% and reduces maximum RPM by 5% fuel efficiency can be increased by 6%. We also demonstrate the main factors defining safety and their relative importance. Finally, we cluster driving styles and suggest good practices to replicate the best driving styles between different driving style clusters. Through a change management framework, we propose how some drivers could improve Coca-Cola FEMSA’s safety proxy by 34% without sacrificing fuel efficiency. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130967 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130966 Channel Flow Optimization for Product Allocation in Grocery Retail Singh, Abhijeet; Fang, Yixuan Supply chain networks in the retail industry have become increasingly complex, giving rise to challenges in product flow from distribution centers to fulfillment locations. Product allocation, in particular, has emerged as a critical challenge for grocery retail companies as it helps them determine the shipping location and the shipping type for every product in their portfolio. The sponsoring company for this project wants to validate their current product allocation model by determining whether the input values used for certain key factors in their algorithm are correct or not. Further, the company wants to identify whether any other factors need to be incorporated into the model. To address the problem statement, our project is focused on simulating the product allocation model with a range of different inputs and layering in relevant cost components. This helps us identify the least cost scenario and the corresponding values for the input factors. We recommend these values for running the model in the future. Using the recommended solution can result in ~12% savings in logistics cost. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130966 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130965 Network Optimization: International Inbound Logistics Kumari, Lipsi; Sladecek, Scott Optimizing the flow of goods across the globe is incentivized by logistics savings, amplified by an enterprise’s economies of scale. Waters’ international shipments are majorly carried out via air freight and are exclusively performed by three main carriers: Expeditors, FedEx, and UPS. The specific problem addressed in this capstone is finding the best solution to systemically reduce the overall inbound international logistics costs for Waters Corporation, which have been flagged as higher than necessary over the last two years. The project methodology followed three main steps: receiving raw data, analyzing the excel spreadsheets, and finally providing outputs of findings. The different sets of raw data were bucketed into two main categories: historical shipment level detail (SLD) and Waters negotiated rates (rates). The carrier-selection cost savings are estimated at 13% of Water’s total international logistics costs into their three DCs. There is a growing opportunity to expand that savings target by reducing the number of annual shipments. The estimated 13% savings can be materialized through a decision-making tool allowing automatic selection of a carrier that ensures lowest shipping costs. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130965 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130964 E-commerce Business-to-Business (e-B2B) Distribution Strategy and Network Design for Nanostores Ahmed, Syed Tanveer; Saragih, Austin Iglesias With 50 million nanostores globally, nanoretailing is the most important retail channel in developing countries. In India, these stores are called kiranas, and they are the backbone of India's retail market. Unfortunately, the distribution to this massive retail segment is fragmented. Current fragmented channels of exclusive distributors, wholesalers, and stockists are costing kiranas more than 50% of their potential margins and impacting their business viability. A non-exclusive e-commerce business- to-business (e-B2B) distribution strategy can serve as a promising solution to reduce fragmentation, cost-to-serve, and increase potential margins in the industry. This research formulated a non- exclusive e-B2B distribution strategy at the lowest cost-to-serve and identified the essential urban network design factors for e-commerce platforms to consider. We extended the Two-Echelon Capacitated Location-Routing Problem (2E-CLRP) with the augmented route-cost estimation to consider wallet share, market penetration, frequency, drop size, and urban circuity factors. We also computed the Road Network Circuity Factor (RCF) values of several Indian cities. Our results indicate that, through e-B2B distribution, companies can achieve most of their cost savings and profitability when they reach certain threshold of wallet share and market penetration. Furthermore, geographical circuity constraints and increasing frequency of deliveries do not significantly increase logistics costs. In summary, we recommend that companies reach their threshold of wallet share and penetration to reduce costs optimally. Key initiatives to reach this target include sharing cost savings back to the nanostores, develop free shipping options and loyalty programs, increasing delivery frequency, and expanding to new service regions. Moreover, companies should not be afraid to increase their delivery frequency or open service in promising regions as these factors only slightly increase cost to serve. Although this project focuses on India, our findings are also applicable to other developing countries. A non-exclusive e-B2B distributor improves adaptability and affordability of nanostore supply chain operations and provides ample opportunities to for further research. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130964 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130963 Innovative Consolidation Techniques for Improved Transportation Efficiency Piechnik, Daniel; Schaufenbuel, Olivia Many trucks in the US travel well below permitted cube and volume utilization, so consolidating two or more shipments onto a single multi-stop truckload (MSTL) route can increase truck utilization and decrease transportation costs. Many shippers and carriers lack sufficient network volume, transportation analytics capabilities, and supply network influence to successfully consolidate these loads. However, digital freight brokers often have large network shipment volumes and more advanced transportation analytics capabilities, which positions them to provide value to shipper and carrier partners in their network by identifying and realizing efficiency opportunities. This capstone uses a set-covering formulation of the Practical Pickup and Delivery Problem (PPDP) in combination with clustering and an adapted column generation approach to identify consolidation opportunities. The set covering problem, which is optimized to minimize cost using a mixed integer linear program solver, identified network cost reductions of 11% and a total network route count reduction of approximately 5%. Freight brokers and large transportation operators can use this approach and methodology to identify multi-stop truckload consolidation opportunities that increase transportation network efficiency by eliminating unnecessary truck routes. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130963 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130962 Automation of Warehouse Decision Making Marino, Roogers; Wu, Zeyu This capstone addresses how an FMCG company should go about automating inbound decision making in one of their warehouses. The capstone focuses only on the inbound side of the distribution center because it was determined that it was an area with big opportunities for improvement. Efficiency inside a warehouse depends greatly on the correct decisions being made. Currently, the decisions being made inside the sponsor’s warehouse are not consistent because they vary between shifts, and it is hard to make the correct decision because there are many variables that must be considered. To address this problem, this research project developed a simulation model that replicates the current inbound state of the warehouse. The model was used to create and compare different scenarios and unloading policies. For example, it was found that the prioritization of trucks that are more time-consuming to process in some cases has an impact on overall drop-lot waiting time. The project shows how the unloading policies affect the average waiting time of trucks. Replicating this process for all the decisions led to determining what are the best policies for what scenarios. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130962 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130961 Maximizing Profits in a Warehouse and Distribution Business Using Segmentation Analysis Darmesh, Aidar; Mantellini Bracho, Ramón Alberto In 2019 most of sponsor company’s transactions came from its freight forwarding service. Due to strategic alliances between retail giants and freight carriers, the profit margins of independent freight forwarders are being squeezed. To survive the company needs to ensure that its warehousing and distribution services are sufficiently profitable. However, company’s existing accounting methodology does not show profitability by operations. This project develops a transaction-level cost and revenue allocation model and applies a profit-mapping technique to identify profitability of the business units, customers and services. A more granular analysis revealed the key profit levers which can be deployed to grow company’s profits. The profit mapping showed that most customers responsible for higher-than-average gross margins do not have higher profits due to intensive use of fixed-cost resources. To maximize profits the company should target reduction of specific cost items, bundle unprofitable services with profitable ones, and exercise caution in pruning customers. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130961 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130960 Measuring Disruption Indicators in Food Service Delivery Supply Chain Schwendenman, Amy; Li, Teng Yi The food-service industry in the United States is worth approximately $300 Billion annually and supports 1 million jobs across the country. The sponsoring company is a major distributor in the United States for different categories of restaurant chains, ranging from counter-only-service to full-service. The key products in their supply chain include meat such as poultry and beef, which are vulnerable to both supply and demand shocks, and could have significant impact to their operations. While they have some visibility downstream to understand causes of demand shocks, there exists an information gap upstream to understand supply shocks. This project aims to connect various external data sources to internal data to 1. identify what supply shocks looks like; 2. find lead indicators of supply shocks in the external data; and 3. quantify their impact on the sponsoring company in order to improve operations planning and contingency planning. The models we built predict instances of expedited shipments and delayed shipments as they relate to macro factors, such as severe weather, wholesale prices, and national slaughter rates. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130960 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130959 Network Design for Two-Day E-Commerce Fulfillment Valentino, Cosmo; Wilson, Ryan With the fast growth of e-commerce, online shoppers are becoming accustomed to free and fast delivery. Small and medium-sized businesses are experiencing rising transportation costs as they are having to switch from slower methods of shipping to fast delivery options (e.g., next day or two-day) to cater to customer demand. Our sponsoring company is a 3PL providing services for these types of businesses and is currently operating five fulfillment centers. They are looking to propose a two-day delivery service to their e- commerce customers. To do that, the company requires an optimization model that recommends the location and the number of fulfillment centers to activate for a given client to meet demand within a two- day window and with a high on-time performance, while minimizing the total logistic cost. The model also balances the tradeoff between inventory and transportation cost. In this capstone project we elaborate such an optimization model and apply it to two customers with different shipping profiles. The results show that the outbound transportation was identified as the most significant portion of the overall logistics spend. This was especially true when the item being shipped was dense causing a higher rate per shipment. Inbound and inventory cost became more significant with low-velocity items and regions with low demand. By opening five fulfillment centers, Customer 1 would save 8.4% and by opening four fulfillment centers, Customer 2 would save 36.1%. A 3PL can use this model to balance the inventory savings realized by a centralized network with the transportation costs savings achieved by a decentralized configuration. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130959 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130958 Diving Deep into the Determinants of Driver Dwell Sauter, Leora Reyhan; Roy, Michelle Catherine Since 2005, the American Trucking Associations has consistently asserted that trucking firms face a shortage of truck drivers. This has become a narrative with which even those with no ties to the 6.5 hours driving time per day; a daily shift is considered 11 driving hours within a legally mandated 14-hour total workday. The core of our research addresses the following question: what opportunities exist to increase U.S. Xpress truckers’ average driving time in a daily shift? Addressing this question required us to identify the factors that cause driver dwell, and understand each factor’s contribution to drivers’ non- productive time. We tested four hypotheses, related to shipper/receiver node location, type of load/unload conducted, driver familiarity with nodes, and driver demographics. We used data provided by U.S. Xpress from electronic logging devices and transportation management systems, spanning from June through November 2020; this data was processed using Python and tested with regression. Our results determine that the biggest factors impacting driver dwell are driver familiarity with a shipper or receiver location, and time of day the driver arrived at the location. Interestingly, driver demographics did not demonstrate significant impact on driver dwell. This suggests that the power to increase driver utilization lies mainly with dispatchers and shipper/receiver node staff - not with drivers themselves. Since U.S. Xpress has the ability to drive change among their dispatchers, efforts should be focused there. Our results show that changing dispatcher behavior - and, if possible, changing behavior among node staff - will improve truck driver utilization. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130958 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130957 Potential Benefits of Drones for Vaccine Last-Mile Delivery in Nepal Vongasemjit, Ornipha; Lembcke Berninzon, Adriana Nepal’s immunization coverage hovers around 78%, and 59 out of 77 districts have not yet been fully immunized. The objective of this study is to review the vaccine last-mile distribution to find a cost-efficient solution using a combination of modes of transportation to improve vaccine availability. This study determines which districts could use drones for last-mile delivery, quantifies the benefits from drone implementation, identifies locations for setting drone bases and recommends appropriate drone types. First, a replicable district classification framework was used, from which six potential districts were selected for drone last-mile delivery. Then, an optimization model was created for two of these selected districts, analyzing parameters such as drone payloads and ranges, vaccine shipment sizes, and costs for each mode of transportation. This research demonstrates that implementing drones is suitable mainly in rural service points of the mountainous regions of Nepal. However, the implementation provides cost benefits only when start-up costs are subsidized or when the drone operation is outsourced by lower than $0.10 USD/dose. Addressing the problem of low immunization coverage could help reduce the mortality rate of children. Our solution could be expanded to vaccine distribution during the COVID-19 pandemic or even in disaster relief scenarios, when roads are inaccessible due to flooding or earthquakes. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130957 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130956 Vessel Network Optimization in the Great Lakes Region Perk, Sena; Ahmadov, Yashar The Great Lakes region is a convenient transportation route for the suppliers and the customers in North America. Various types of goods are being transported in the region including dry bulk commodities such as iron ore, coal, grain, and salt. The sponsor company for this project is a provider of dry bulk shipping services throughout the Great Lakes. This project has two main deliverables. The first one is to assign vessels for each trade lane for the shipping season of nine months. The second one is to find the optimal sequence of trips for each month in the shipping season. A two-phased optimization model was developed to satisfy these deliverables. In Phase 1, the optimal allocation of vessels was found with the objective function of minimizing the transportation cost. In Phase 2, the optimal sequence of trips was found with the objective function of minimizing the ballast time. Results of the optimization model provided 20% reduction in the ballast ratio per ton for the shipping season of nine months. The optimization model will be a used as a planning tool by the sponsor company. The model can also be applied on similar pickup and delivery problems in the transportation industry. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130956 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130955 Should Shippers Be Afraid of Ghost Freight? An Empirical Analysis of a Customer Portfolio from TMC, a Div. of C.H. Robinson Liu, Yu Xuan; Miller, Alexander Clayton Over the past several years, there has been severe market volatility in the truckload industry leading to cost increases and efficiency losses for shippers and carriers. Previous research has investigated the many factors that contribute to such market conditions. One topic that has yet to be analyzed is “ghost freight.” Ghost freight occurs either when no volume materializes on a lane (origin-destination pair) that was previously awarded to one or more primary carriers (a “full ghost” lane), or when the shipper tenders to only a subset of awarded primary carriers (a “partial ghost” lane). Our research leveraged five years of truckload market transactions for 15 shippers and over 300 carriers to conduct our analysis. We utilized Python and Tableau to identify and visualize the frequency of ghost freight across the market along with the types of lanes that tend to become ghost lanes. In addition, Ordinary Least Squares (OLS) regression was used to determine the impact of ghost freight on carrier performance. This research found that both full and partial ghost lanes occur very frequently in general each year, however there is a lack of pattern with respect to individual shipper behavior. The regression models did not show a clear impact of ghost freight on acceptance rates or prices. This may be the case in part because full ghost freight occurs overwhelmingly on low-volume lanes, which are traditionally not a capacity planning priority. That being said, we found that partial ghost lanes tend to occur on lanes that are often medium-to-high volume. This finding may be a topic of interest for carriers for future capacity planning. Further, although shippers do not appear to face direct financial repercussions resulting from carriers, it is ultimately inefficient to spend time and money awarding lanes that are never tendered to. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130955 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130954 Adaptability of Manufacturing Operations through Digital Twins Reyes, Maria Fernanda; Garg, Sachin Manufacturing companies are under pressure to build faster and more efficient decision-making capabilities due to the rapidly changing customer demand and expectations. The conventional analytical models are no longer sufficient to capture the complexities of the supply chain. Companies are looking to embark in a digital transformation to address these challenges. One of the digital technologies that offer manufacturers a way to navigate this journey is digital twins, a virtual replica of an object, process or system. Our project focused on studying how digital twins can react to a complex and dynamic environment to create an adaptive mechanism and how can digital twins add value to increase operational efficiency. To answer these questions, we created a conceptual framework of digital twin, AI model and developed a learning feedback loop between simulation and artificial intelligence algorithm. We modeled the supply chain network by using data from a beverage industry and created what-if scenarios that involved varying customer demand and lead time through discrete-event simulation. The output of the simulation was fed into the AI algorithm. The AI prediction was simulated again and results were analyzed. Our research provides insights and discover value associated with adopting these technologies for better decision making. Our recommendation from this study will help supply chain managers understand that a digital twin and AI model framework can be developed, and can be utilized to foresee patterns in supply chain, and proactively take actions to resolve any bottlenecks and constraints. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130954 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130953 Defining and Detecting Churn in Truckload Transportation Jungsakulrujirek, Kawin; Rehan, Saad The truckload transportation industry is an established industry, in the US, with annual revenue for for-hire truckload reaching greater than 300 billion dollars in 2019. A major problem encountered by for-hire truckload carriers is a sudden, unexpected and sustained reduction in shipment volume over lanes referred to as ‘churn’. Churn leads to a significant problem in the balance in the carrier’s network which, in turn, drives up costs, reduces revenue and decreases driver satisfaction. In this capstone, which is a first-of-its-kind study within the truckload industry, we leverage data from our sponsor - a large national trucking firm - to formally define churn using three parameters: base, drop and duration. Based on this definition we identify churn by origin within the carrier’s network and then establish correlations between the characteristics of an origin and the likelihood of churn at that origin. This framework allows carriers to quickly detect churn before it materializes and take proactive steps to mitigate its negative impact. Our research on churn opens up avenues for further study in this area, within the TL industry, including studying churn at a larger scale to develop more widely applicable ways of defining, identifying and detecting churn. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130953 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130952 How Postponement Strategy Can Reduce Cost and Lead Time for Pharma Supply Chains Ploszczuk, Lukasz; Nolan, Rebecca Nowadays end customers and shareholders are setting high expectations, putting pressure on companies to do their best not only to meet their needs but also deliver shareholder value at the same time. In order to be both competitive and customer-centric, firms are increasingly focused on their supply chains as one of the areas for future improvements. Some industries (e.g., rare medicines) are even more complex, as demand is stable over time, so the only way to increase profitability is to constantly focus on cost reduction. In order to be more competitive and meet customer demand, our sponsoring company must ensure that its supply chain is agile enough to provide its customers with life-saving medicines quickly and without delays. These might be achieved by switching to late product customization at a third party-logistics-provider's location. The goal of the model is to provide the proper tools and information necessary for our sponsoring company to use when evaluating whether to put in place a postponement strategy. Thus, in order to capture the entire end-to-end changes in the supply chain, we broke it out into three separate models, including a materials model, “naked vials” (unlabeled) model, and finished goods model. This allowed us to process the information in separate parts and capture all costs at the level they were acquired. A comparison of the base scenario to different customization scenarios is also included in order to understand potential cost benefits. Based on the sensitivity analysis, it makes sense for the company to implement a customization strategy for almost all analyzed scenarios, as in 84% of cases the company can deliver cost savings. Overall, this model reduced the information gap within the sponsoring company by providing them with the proper tools needed not only to evaluate their current inventory strategy but also as a tool to use when negotiating with their third-party providers. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130952 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130951 Goldilocks and the Three Dispatchers: Quantifying the Impact of Dispatcher Management on Truck Driver Performance Procter, Danielle; Sousa, Paulo Jr. Though critical to the US economy and moving the majority of US freight, the American trucking industry faces three compounding challenges: driver shortage, low driver utilization, and high driver turnover. Previous studies have found that, though scarce, drivers are underutilized and prone to frequent employment changes, further exacerbating the shortage problem. To identify the root causes and offer potential solutions, this study investigates the impact of carrier dispatchers on truck driver performance. This performance was measured by three key metrics: Hours of Service utilization, average miles driven per day efficiency, and employee retention. ELD and TMS data from a midsized carrier was run through regression and clustering machine learning algorithms to evaluate the features impacting these metrics. It was found that dispatchers indeed impact driver performance and have at least three managerial levers that can be used to improve fleet performance, including the weekday a driver works, the equality of distribution of freight plans, and the size of the team a dispatcher manages. With these levers, freight carriers can themselves mitigate the impact from the challenges facing the American freight industry today. Wed, 16 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130951 2021-06-16T00:00:00Z https://hdl.handle.net/1721.1/130925 Potential Benefits of Drones for Vaccine Last-Mile Delivery in Nepal Lembcke Berninzon, Adriana; Vongasemjit, Ornipha Nepal’s immunization coverage hovers around 78%, and 59 out of 77 districts have not yet been fully immunized. The objective of this study is to review the vaccine last-mile distribution to find a cost-efficient solution using a combination of modes of transportation to improve vaccine availability. This study determines which districts could use drones for last-mile delivery, quantifies the benefits from drone implementation, identifies locations for setting drone bases and recommends appropriate drone types. First, a replicable district classification framework was used, from which six potential districts were selected for drone last-mile delivery. Then, an optimization model was created for two of these selected districts, analyzing parameters such as drone payloads and ranges, vaccine shipment sizes, and costs for each mode of transportation. This research demonstrates that implementing drones is suitable mainly in rural service points of the mountainous regions of Nepal. However, the implementation provides cost benefits only when start-up costs are subsidized or when the drone operation is outsourced by lower than $0.10 USD/dose. Addressing the problem of low immunization coverage could help reduce the mortality rate of children. Our solution could be expanded to vaccine distribution during the COVID-19 pandemic or even in disaster relief scenarios, when roads are inaccessible due to flooding or earthquakes. Wed, 09 Jun 2021 00:00:00 GMT https://hdl.handle.net/1721.1/130925 2021-06-09T00:00:00Z https://hdl.handle.net/1721.1/128251 Closing the Food Access Gap in American Underserved Communities Taylor, Jamal; Baretto, Luiz Paulo Silva Malnutrition is a global issue that affects millions of people across the world. Malnutrition is not just the lack of food, but also consists of the overabundance of unhealthy food due to a lack of healthy food. This instance of malnutrition is particularly troublesome for cities in the United States. In the U.S., there are many people who simply do not have access to healthy food options. Many of these individuals live in “food-deserts” or areas where no grocery stores that sell fresh produce exist within a 1-mile radius. In low-income areas where many people do not have access to a car, residents of food-deserts may have no way of accessing healthy food options. One way to combat the problem of food-deserts is to supply these areas with healthy food options. This research is centered on answering two research questions: 1) What food supply chain model (grocery delivery, ride share, veggie-box) would residents of low-income areas prefer? 2) What is the feasibility of implementing this food supply chain model to increase healthy foods in low income areas? This research was conducted by surveying residents of Somerville, MA, and also interviewing stakeholders within the potential supply chain for sourcing food-desert neighborhoods with fresh produce. These data were analyzed using a series of logistic models regressions, which resulted in 82.7%, 75.2%, and 89.5% prediction power for the ride share, grocery delivery, and veggie box supply chain models, respectively. The research shows that residents preferred the veggie-box model and that this model was also feasible in supplying neighborhood markets within food-deserts with fresh produce. Thu, 29 Oct 2020 00:00:00 GMT https://hdl.handle.net/1721.1/128251 2020-10-29T00:00:00Z https://hdl.handle.net/1721.1/126503 Using Machine Learning Approaches to Improve Long-Range Demand Forecasting Nowadly, Katherine; Jung, Sohyun Achieving an accurate long-range forecast is a challenge many companies face due to the uncertainty in anticipating demand several years out. Since companies make strategic decisions based on these forecasts – such as long-term investments and supply and capacity planning – it is critical that the long-range forecast be as accurate as possible. For a large player in the pharmaceutical industry like our capstone project sponsor, an improvement in its forecasting process could have significant financial and organizational benefits. While traditional statistical methods have been extensively used in demand forecasting, due to technological developments, machine learning approaches have been widely studied and increasingly applied in forecasting. Since machine learning has shown improvements in forecasting, especially in short-term forecasting, could machine learning be applied to improve long-range forecasting? This study explores this question by testing several machine learning methodologies and approaches. First, we used support vector machine (SVM) to determine relevant features. Next we used two types of approaches, direct and recursive, to develop one-step and multi-step long-range forecasting models. We developed forecasts using four machine learning algorithms: random forest (RF), artificial neural network (ANN), linear regression (LR), and support vector machine (SVM). We found that RF, ANN, and LR produced relatively accurate results in the one-step models. However, when extending the forecasting horizon using a multi-step forecast, the accuracy declines. By observing the results of the feature selection process and comparing the results among our forecasting models, we determined which features are critical when forecasting long-range demand for certain drugs. Additionally, we found that the machine learning model performance differed greatly based on data availability, forecasting horizon, and individual product. The biggest challenge in pursuing the application of machine learning approaches in long-range demand forecasting is data management. Given that using a machine learning approach in long-term forecasting has inconclusive performance, and creating a data management program would require a large up-front investment, a detailed cost-benefit analysis along with internal discussion is advised before pursuing further applications of machine learning in long-range demand forecasting. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126503 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126502 Right Sizing Safety Stock and Effectively Managing Inventory Using Forecastability Pan, Ni; Sweeney, Jamie In a commodity consumer product business, such as bottled water, the customer has the power. Therefore, the business incurs whatever cost necessary to meet demand. To reduce the cost of fulfilling demand and of stockout, businesses must thoughtfully set inventory safety stock levels to compensate for potential spikes in demand. The purpose of this capstone is to analyze the current inventory strategy and its effectiveness of the sponsor, a bottled water company. The team worked to explore the drivers of supply and demand variability to identify potential improvements in inventory management, which could reduce cost while maintaining service levels. The team analyzed the customer demand, production demand, strategic forecast, and inventory on hand for over 100 stock keeping units (SKUs) in a specific geographic region over the last three years. As a result of the analysis, the team proposes SKU segmentation by forecastability and appropriate safety stock calculation using the standard deviation of forecast errors. This method of calculating safety stock, as compared to the sponsor’s current approach, reveals a clear opportunity to reduce the inventory by 28% for SKUs with predictable and positive demand. Another key finding is the opportunity to reduce the order quantities when the annual forecasted demand of a SKU is below an identified threshold. Lastly, the team recommends increasing the inventory level kept at supplier consignment to further minimize the risk of stockout at low cost due to consignment agreements. To further and continuously improve inventory position and service levels, the team recommends a quarterly strategic inventory review to adapt strategy as business needs and requirements shift. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126502 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126501 How to Plan and Schedule for Profit: An Integrated Model and Application for Complex Factory Operations Silvestro, Alessandro In the manufacturing industry, short-term production planning and scheduling requires multiple trade-offs to account for service targets, capacity utilization, setup, on-time delivery, costs and profit. If many SKUs flow in the same production line, the challenge is how to plan and schedule in such a way that an optimal trade-off between customer service, operational performance, and cost of goods sold can be achieved while maximizing gross profit. This research project provides a novel mixed integer linear model formulation that optimizes lot sizes in a CG factory such that manufacturing capacities and efficiencies, production, inventory, holding and setup costs are considered simultaneously while maximizing the expected profit. The model solves a multi-echelon production and inventory network and quantifies the advantages by comparing different baselines. The model application evaluated against the simulated Sponsor Company reference baseline proves to be on average 4% more profitable every week, in a quarter of a year period, in the most conservative scenarios. The scenario analysis provides interesting managerial insights into what to expect when improvement efforts focus on minimum production lots, decoupling buffers or less-than-full deliveries and how they increase even further the overall profitability. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126501 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126499 Inbound Logistics Optimization Hu, Xuefang; Weisel, Ezra The intrinsic competitive nature of the fast-moving consumer goods (FMCG) industry have made it a priority for companies to maximize profitability by aggressive cost-cutting measures in the context of growing material cost, surging labor expenses and increasing demand for product customization. While exploring optimization opportunities in outbound logistics management, which mainly focuses on delivering goods and services out of a business entity, many market players shifted gears to delve into inbound logistics operations, which center on the management of materials and finished goods into a facility. This project unlocks cost saving opportunities in the inbound logistics system of a consumer goods company by answering two questions: What is the optimal minimum production quantity for finished goods? What is the appropriate minimum order quantity for packaging materials to minimize delivery and storage cost? Multiple machine learning techniques are utilized throughout the research: clustering techniques are used to identify MPQ, and a cost minimization model in Microsoft Excel and Python is developed to compare current cost with simulated cost. It is estimated that 16% cost savings can be obtained by optimizing MPQ and MOQ. Additionally, the models are highly replicable to other manufacturing sites of the CPG company to generate greater operational efficiency. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126499 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126496 Evaluating Inventory Risk Pooling Strategy for Multi-Echelon Distribution Network Sharma, Hari; Bojorquez Aispuro, Angelica Rising inventory costs is an ongoing challenge for any firm. These costs are of special significance to retail firms like Coppel, whose inventory investments are typically high and margins are slim. Inventory risk pooling is a strategy that is often ignored but can help bring significant cost reduction without affecting service levels. Such inventory decisions are generally considered tactical and are often constrained by the strategic network design decisions that precede them. This siloed approach leads to sub-optimal decisions. The objective of this study is to integrate the two to overcome this inflexibility and evaluate whether the existing distribution network can be profitably reconfigured to introduce risk pooling. This paper develops a decision support model for Coppel to identify the best location for pooling inventory while minimizing total supply chain costs. Integrating inventory and location decisions represents a Location-Inventory Problem (LIP), which comes with inherent modeling and computational complexities due to increased problem size and non-linearity. Evolving solution techniques and improving computing power now make it feasible to solve LIPs efficiently. We develop a Mixed Integer Nonlinear Programming model that follows a Guaranteed Service Model approach to solve this integrated LIP in a multi-echelon multi-product supply chain environment. Due to the non-linear nature of the model, we deploy piecewise approximation methods to first linearize the function before solving. Our research demonstrates that reconfiguring the existing network to introduce risk pooling could reduce the supply chain costs of major product classes by 15%, without affecting their service levels. This is a common challenge across industries. Therefore, the benefits of this research extend beyond Coppel and retail industry. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126496 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126495 A Forecasting Face-Off for Oil and Gas Spare Parts Serry, Mahmood; Vasa, James Spare parts demand forecasting is a key activity for asset intensive industries, but it is challenging due to the underlying demand characteristics. Demand is characterized by periods of zero demand arrivals; and the size of the order is variable with large, unexpected spikes. Schlumberger, an oil and gas service manufacturer, is facing the issue of low forecast accuracy for its spare parts, and has challenged the team to improve it. This research uses machine learning techniques to improve demand forecasting accuracy of spare parts for Schlumberger. The methodology of the research starts with classifying the parts into four classes namely: smooth; intermittent; erratic; and lumpy. Then, we apply recommended time series based on the literature for forecasting four classes. The time series forecast was then fed as features along with judgmental forecast and the demand parameters into two different machine learning algorithms, namely Classification and Regression Trees (CART) and Random Forests. Both models showed more than 75% improvement in accuracy over conventional demand forecasting methods when measured by Root Mean Squared Error. This improvement shows the potential benefit of adding human judgement as a parameter into machine learning algorithms when forecasting spare parts. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126495 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126494 Eliminating Last-Mile Inefficiencies in the Trucking Industry From, Kristian; Mangan, Katharina Pilot Freight Services, traditionally a bulk cargo freight forwarder in the US, is in the process of expanding their business to provide last-mile delivery (LMD) services. This capstone project helps Pilot improve the performance of their LMD operations through higher visibility and elimination of efficiencies. First, an understanding of Pilot’s current LMD operation is established. Next, a performance metric framework is defined, with two performance dimensions: (1) service level and (2) efficiency. Guided by the framework, the performance of Pilot’s LMD operations is assessed by analyzing descriptive statistics. A visualization tool is built in Tableau, allowing Pilot to continuously assess their own performance. Finally, machine learning is used to identify parameters that affect performance and predict their impact. The parameters identified as having the biggest impact on stop time duration are: volume delivered, population density, quantity pieces delivered, stop number, time of day, and peak day. For drive time duration, the single most relevant factor is mileage. For each of the locations analyzed, coefficients are calculated and made available to Pilot’s planners to predict stop and drive time based on the parameters. Planning accuracy, in terms of MAPE, is for stop time improved from about 85% to about 55%, and for drive time from about 45% to 25%. The insight provided by this capstone will allow Pilot to better understand and assess the performance of their LMD operations and help identify areas for improvement. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126494 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126493 Facility and Routing Decisions in Truck-Drone Distribution Doan, Vu Bich Na; Taiyeb, Amr Mohammad Drones have the potential to be major players in the field of last-mile delivery, especially with the rise of e-commerce. However, they face several technological and regulatory restrictions for large-scale implementation. Combining drones with traditional ground delivery vehicles can bridge this gap while achieving significant improvements in distribution cost and speed over vehicle delivery alone. This research project focuses on modeling and solving the Location-Routing Problem with drones as an ancillary mode of delivery. The goal is to develop a model that identifies the optimal locations for the distribution facilities, as well as the set of combined routes that the trucks and drones will follow to deliver parcels to customers. To solve this problem, a two-steps metaheuristic approach is developed and implemented. The customer locations are first grouped into clusters with centroid positions where trucks would park, dispatch, and retrieve the onboard drones that perform the last step of the delivery. Once the optimal truck parking locations are identified, the selection of the optimal distribution facilities and the truck routes are determined simultaneously, by implementing the Multiple Ant Colony Optimization algorithm. The validation of the model revealed high reliability with a 1% average optimality gap from the exact solution. When applying the model to a real road network, with 200 customers and 5 candidate depot locations, the model confirmed a 24% saving in daily distribution costs from adding 3 drones to every delivery truck. The savings opportunity is less sensitive to the number of drones per truck and the drones’ speed, and more sensitive to the trucks’ speed and the drones’ traveling cost per mile. The analysis reveals that adding drones will generate savings as long as the traveling cost of drones is lower than that of trucks at around $4.00 on average for last-mile delivery. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126493 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126491 CO2 Emissions of Innovative Last Mile Delivery Solutions Egorov, Fedor This work studies the impact of innovative last mile delivery solutions on the amount of produced CO2 emissions. An innovative last mile delivery solution is defined as a truck that carries a drone and together in tandem the truck and drone deliver parcels to the set of customers. The problem of finding the optimal route to serve customers by the truck and drone tandem is known as Travelling Salesman Problem with Drone (TSPD). However, all previously developed approaches for solving TSP-D do not consider the time dependent nature of the problem when the speed of the truck is affected by traffic congestion, infrastructure constraints, etc. To address this task, the genetic algorithm for solving a time dependent TSP-D was developed. The input information for the time dependent TSP-D algorithm is derived through “Simulation of Urban Mobility" (SUMO) software. SUMO allows realistic simulation of the various infrastructure constraints and calculation of the information required for the time dependent TSP-D algorithm. The computational experiments show that the truck and drone tandem can significantly (more than twice) shorten the delivery time in congested urban areas. The sensitivity analysis reveals that drone speed does not considerably affect delivery time or the amount of produced CO2 emissions. Ultimately this study demonstrates that using the truck and drone tandem contributes to shorter delivery time and less CO2 emissions and provides the model for assessing these benefits. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126491 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126490 IoT-Based Inventory Tracking in the Pharmaceutical Industry Kerr, Andrew; Orr, Anthony Inventory visibility has been a primary concern for corporate supply chains for decades. Utilizing inventory location and time data is particularly important for pharmaceutical companies, as is the sponsor, that have an ethical and legal responsibility to protect consumers from risky pharmaceutical products in the market. Until recently, pharmaceutical companies have had to rely on archaic, cumbersome methods to track or count inventory units. These processes created inaccuracies and mismanaged inventory, leading to unnecessary product waste, returns, and consumer risk. However, technological advancements have created platforms to track physical goods characteristics using wireless network systems in real-time. This technology, commonly referred to as the Internet of Things (IoT), provides a potential solution for pharmaceutical companies to manage and protect pharmacy inventory levels, while maintaining consumer protection and brand integrity. This study analyzes the economic and practical implications of implementing an IoT inventory visibility solution within the sponsor’s supply chain to mitigate consumer risk and existing corporate financial waste streams. Through existing technology research, real-world device experimentation, and cross-functional supply chain analyses, the team proposes a Bluetooth technology IoT network infrastructure and business implementation approach for the sponsor’s inventory visibility needs. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126490 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126489 Optimizing Satellite Locations for a Multi-Echelon Last Mile Distribution Network to utilize Alternative Delivery Vehicles for Last Mile Delivery Goyal, Abhinav The growing urban population over the past few years has created many challenges for last mile distribution, such as traffic congestion, pollution, and lack of parking space availability. With the advent of e-commerce industry, the volumes for last mile delivery are growing consistently. As firms struggle to provide competitive delivery commitments to the end customers, they are exploring alternative delivery methods, such as drones and e-cargo bikes to navigate urban areas efficiently while addressing pollution and traffic concerns. However, range and capacity constraints associated with such alternative delivery modes restrict the operations that can be carried out with such vehicles. Hence, firms are re-designing their last mile distribution strategies to adapt to the constraints posed by these delivery modes. One such strategy is to deploy a multi-echelon distribution network, using satellite nodes near customer locations that allow for transshipment. The large conventional trucks deliver parcels to a satellite, from whereon the parcels are cross-docked into lighter vehicles (such as e-cargo bikes) which perform the final delivery. This project introduces a mixed integer linear programming model for a two-echelon delivery network, to determine the optimal count and locations of satellites for a large parcel company. The transactional data for deliveries and pickups associated with one parcel center has been used to develop and test the model. The first-tier transportation, that is from parcel center to satellites, has been designed as a location routing problem. The second-tier transportation, that is from satellites to customer delivery points, has been designed as an allocation problem. The model tries to minimize the associated costs with the satellite operation, optimizing the fixed cost of establishing a satellite against the cost of distance travelled and transit time. Traffic considerations and road network distances have been accounted for by using real road network data for distance calculations, and transit times adjusted for traffic conditions across multiple hours during the day. The model returns the count of satellites to be established, along with their respective locations and vehicle routes for first tier transportation. Finally, the model maps all the customers to their respective satellites to achieve an optimum distribution cost. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126489 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126487 Manufacturing Digital Transformation Strategy for FMCG Gallo Orjuela, Sara; Ortega Camacho, Anais Manufacturing companies are facing serious challenges to survive and succeed in the market in this ever-changing economy. The FMCG industry is not unaware of these challenges, and therefore, many leading manufacturing companies are creating initiatives of Smart Manufacturing or Industry 4.0. However, the literature on Digital Manufacturing mainly addresses technical aspects of the implementation and not the design of a complete strategy, which involves not only technologies but also the organization and external components like customers or suppliers. Our project aimed to close the gap between the technological components of a Digital Transformation and the human factor. Therefore, this project could be considered a multiple methodological approach. On one side, the study is based on the collection and analysis of data obtained from the ERP System of the company. On the other hand, the project relies on a survey to discover the digital maturity of the bottling plants to include the human factor. Thus, the sponsor company will have the base information to implement a Digital Manufacturing strategy. With the information captured from the ERP System, we performed a cluster analysis to group the bottling plants into smaller groups that have similar performance characteristics. Moreover, with the results of the surveys, we examined the perspectives of the operational team from all bottling plants. Consequently, plants can be group depending on their operational performance and digital maturity of their organizations. Finally, managerial recommendations for all clusters were provided. In some cases, where digital technologies are more advanced, the goal is to exploit this competitive advantage and introduce more sophisticated methodologies to analyze the data and create value-driven decisions. In other cases, before starting with the implementation of new digital technologies, employees must be prepared to receive new technologies and learn how to work in a digital world. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126487 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126486 Dealing with Complexities in Digital Supply Chain Brillante, Jamica; Lee, Yoon-Joo The digital supply chain is rapidly evolving, putting greater pressure on suppliers to adopt to the dynamic demands of the buyers. This paper explores how supply chain complexities and buyer-supplier relationships as a complex adaptive system interact with an integrated and enacted external environment and drive the key supply chain performance of the supplier. Econometric modeling through fixed effects panel data analysis and seemingly unrelated regression analysis were conducted to analyze the impact between supply chain complexities, buyer-supplier relationship attributes, and supply chain performance through time. Consequently, moderation analysis and sensitivity analysis were conducted to determine the effects of the interpreted and enacted environment perspective. Results show supply chain complexities and buyer-supplier relationships have different impact on service level and sales that changes in degree and significance through time. Moreover, uncertainties and external attributes as the model representation of the interpreted and enacted environment have notable influences on the emergent system properties of the system. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126486 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126485 Achieving Sustainable Growth at Uber Freight Raman Grubbs, Elizabeth; Shathi, Sadia Rahman The freight industry creates 8% of the world’s greenhouse gas emissions through shipping. If companies measure precisely, they can begin the benchmarking process toward improvement. The Global Logistics Emission Council (GLEC) provides a current consolidated Framework for calculating carbon emissions for freight transportation. Uber Freight, a third party software platform based trucking logistics service provider, requested a process to calculate and reduce their carbon emissions. After creating a calculation and forecast for carbon emissions, we complete an in-depth analysis of key lanes and activities to target for improvement. For long-term reduction, we present a projection of emissions through 2050 based on current activities, along with a Science Based Target that Uber Freight can use to set a climate goal. We provide Uber Freight with a strategy and method for measuring, tracking, and reducing their overall company environmental footprint along with the tools to enhance the environmental footprints of their shipper and carrier partners. For future accuracy improvement, Uber Freight can collect data on carriers’ specific CO2e per tkm and equipment type to avoid using general factors. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126485 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126484 A Predictive Model for Transpacific Eastbound Ocean Freight Pricing Huang, Yan The containerized ocean freight market has been very volatile due to overcapacity and several disruptive changes. As a global ocean freight forwarder, C.H. Robinson hopes to improve the predictability of the spot ocean freight rates, especially on the Transpacific Eastbound (TPEB) lanes which represent its largest trade volumes. Therefore, this research aims to build a predictive model for the TPEB spot freight rates using publicly available economic indicators and carrier data sources. Two predictive models corresponding to the US East Coast (USEC) routes and the US West Coast (USWC) routes are developed. The models for the China-origin routes are able to capture 69.0% of the variances in the USEC spot rates and 55.4% of the variances in the USWC spot rates. As an initial exploration, this research identifies 6 sets of critical economic indicators and unveils their effects on the TPEB spot rates. It also highlights the impact of 3 disruptive events and points out a few promising directions for future study on the ocean freight dynamics. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126484 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126483 A Time Series Model for the China-to-U.S. Ocean Freight Pricing Cao, Yuchen Yvonne Ocean freight forwarding on the China-to-U.S. lane is a key service that C.H. Robinson, the sponsoring company, offers to the firm’s international clients. The rates on that lane have experienced volatility in the past few years which led to uncertainties to the future pricing trend. A statistically predictive model that forecasts the future pricing trend can help to resolve this challenge. This capstone studies two approaches: a time series forecasting model, and a time series forecasting model with exogenous factors. These models are used to build a predictive model to forecast the future ocean freight rate. Economic indicators are selected as the independent variables in this research. After comparing 14 time series models including Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing model, the results show that a Multiplicative Seasonality (with no trend) Exponential Smoothing Model provides the best-fit forecasting metric. We also discovered that the best-fit model is less sensitive to error when the analysis assigns more weight to the most recent observation and the error rate increases rapidly right after the model experiences a sharp drop in the historical data rates. Some economic indicators show a correlation with the historical ocean freight rates; however, they do not improve the accuracy of the model with or without lags in the period. Therefore, we concluded that a multiplicative seasonality (with no trend) exponential smoothing model can best predict the future pricing of the China-to-U.S. ocean freight rates. Thu, 06 Aug 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126483 2020-08-06T00:00:00Z https://hdl.handle.net/1721.1/126451 Improving the Cash Availability of Small Firms in Latin America via Better Inventory Management Thompson, Trevor Nathan; Cabrera Hernández, Analiz In Latin America, micro, small, and medium firms represent about 99% of businesses and more than 70% of employment, according to the Organization for Economic Cooperation and Development. Despite their number, these firms face a low survival rate: per the Small Business Administration, only 50% are still in business after five (5) years. We identified lack of cash availability as a driving force behind this high failure rate. There is an opportunity for these firms to achieve improvements in cash availability by managing their inventory better. This is relevant for these firms because they operate on a cash basis, meaning they pay for all necessities with cash and, in turn, collect all payments in cash as well. In this capstone, we present an inventory management framework that focuses on improving the cash availability for micro and small firms via better inventory management. At the start of this project, we conduct a field study analysis with three (3) firms in the city of Bucaramanga, Colombia, to better understand their operational practices. Our field study reveals that every firm indeed relies on cash for daily operations, and that these firms struggle to manage that cash effectively. As a result, we map the financial and operational performance of six (6) firms for ~17 weeks and develop a product segmentation and an inventory framework that reduces average inventory and increases inventory savings by up to ~80%, while also increasing inventory turns per segment. Additionally, we create a “business pulse dashboard” that provides weekly visibility to cash management, focusing on sales, profits, inventory, and expenses. Thu, 30 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126451 2020-07-30T00:00:00Z https://hdl.handle.net/1721.1/126450 Dynamic Trade Policy and Supply Chain Design Within the Oil and Gas Industry Sharkey, Liam Trade policies of the late 2010 decade are characterized by a unique combination of severity, shorter lifespan, and greater frequency. Supply chain leaders within the oil and gas industry may not recognize the full range of responses they might take when responding to this new dynamic. This report will do three things: first, understand how supply chains are currently reacting to this new dynamic; second, propose frameworks for outlining how supply chains could respond to this new challenge; and lastly, recommend a course of action given a certain supply chain design. This was accomplished using semistructured interviews, case studies, reductionism, and supply chain resilience literature to develop, propose, consider, and validate potential frameworks. Two frameworks are presented in the report. The first framework has four distinct sub-approaches to tariff impact: tariff acceptance, tariff reduction, tariff avoidance, and tariff elimination. As a supply chain moves from tariff acceptance to tariff elimination, cost appears to increase and disruption risk appears to decrease. The data suggest that supply chains within the oil and gas industry tend to implement a solution that does not change the risk profile. The second framework focuses on three approaches a supply chain leader can adopt when allocating cost associated with trade policy: first, the firm can pass along the tariff’s financial impact to the customer; second, the firm can push back on the supplier and force them to take the financial impact of the tariff; and third, the firm assumes a portion of the tariff’s financial impact. The data suggests that most oil and gas supply chains assume some portion of the cost. Thu, 30 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126450 2020-07-30T00:00:00Z https://hdl.handle.net/1721.1/126449 Data Aggregation for Data Analytics in Medical Device Supply Chains Lamas Oporto, Gabriela; Alhalafawy, Sherif Product visibility in the medical device supply chain is a challenge for suppliers, distributors, and hospitals. The lack of visibility makes managing inventory complex, and it is made more difficult when businesses have a segregated distribution model. In this model, a surplus of systems collects inventory data at the supply chain nodes, but the data is not integrated due to system barriers. This aspect of ‘big data’ is a current problem multiple supply chains are facing as they look towards future data analytic capabilities. In this capstone, we evaluated the potential of integrating the sponsoring company’s data sets from fragmented planning systems in enabling advanced data analytics and visualization that can improve inventory management. We successfully created a data aggregation tool after cleaning and transforming the data sets and performed data analysis on the aggregated data. SKU segmentation was completed, and their inventory distribution analyzed. Results support using aggregated data sets for data analytics in medical device supply chains. We recommend that the sponsoring company integrate the tool into their business processes and use customer centric data to enhance their inventory management. The medical device industry struggles with product visibility and the lack of connectivity is a barrier, but as companies continue to strive towards aggregated systems for data analytics, these capabilities would lay the framework for better inventory management in their distribution networks. Thu, 30 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126449 2020-07-30T00:00:00Z https://hdl.handle.net/1721.1/126431 Exploring Carbon Offsets for Freight Transportation Decarbonization Dame, Catherine; Hefny, Abdelrahman Carbon offsets present a mechanism to leverage corporate sustainability commitments to fund fleet renewal programs, decommissioning aging trucks and replacing them with greener vehicles. This study evaluates the feasibility of this approach from a financial and logistical perspective. First, the demand for transport carbon offsets is forecasted using inputs from global greenhouse gas emissions data, corporate sustainability commitments related to transportation, and CDP corporate disclosure data, pricing the potential mature market size at over $300M per year. Secondly, a novel method is defined to quantify the avoided emissions from decommissioning for a variety of vehicle types, leveraging industry standards for carbon accounting such as the Global Logistics Emissions Council Framework. Finally, the study breaks down the costs associated with a fleet renewal carbon offset program, and provides operational recommendations and best practices for fleet renewal programs. The analytical tools and frameworks developed in this study can be utilized to support the design and implementation of a transportation-based carbon offset program that has the potential to drive significant impact in global carbon emissions reduction. Wed, 29 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126431 2020-07-29T00:00:00Z https://hdl.handle.net/1721.1/126393 Humanitarian Assistance for Markets in Conflict: a System Dynamics Approach Hao, An Qi; Srinath, Sindhu Humanitarian organizations play a crucial role in providing aid to the victims of disasters – whether natural or man-made. One of the leading organizations is the International Committee of the Red Cross (ICRC), which saves lives and alleviates the suffering of those affected by armed conflicts. The relief action to be taken for a conflict-stricken area is assessed using a market analysis method which helps ICRC in understanding the overall condition and behavior of the market, to what extent it could support the beneficiaries and whether support to market actors is required and in what form. ICRC wants to enhance this market analysis method by adding dynamic interactions between market actors and how changes in the market environment, planned and unplanned, may help or hinder market functionality. This will help ICRC in choosing a suitable response action for a type of market actor based on how it will affect other actors in the market. Our capstone project uses system dynamics method to map the interactions between market actors. The system dynamics model we developed can simulate market conditions under different scenarios of disruptions or humanitarian interventions. The usage of this model for the market analysis process will strengthen the involvement of the supply chain team in assisting the program team to collectively decide on the best response. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126393 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126392 Development and Application of an Immunization Network Design Optimization Model for UNICEF Hashimoto, Yuto; Ribeiro Carretti, Henrique Over the past five years, immunization coverage in Sub-Saharan Africa has stagnated at 72%. Immunization supply chains are expensive and complicated, and creating a model that helps optimize these supply chains is of great importance not only for people’s health but also for the efficient use of limited budget in developing nations. Most existing supply chain optimization models aim at minimizing costs or maximizing profit, which do not always fit in the context of humanitarian logistics. Also, they regard the demand as exogenous, but the proximity to health centers may affect people’s demand. Therefore, the objective of this project is to build an optimization model for vaccine network design that aims to maximize the access to immunization and incorporates the endogenous demand function. We take a two-step approach to build the model, and each step has a process of model formulation and validation. The first step is to formulate the toy model, a simplified version of the model using an example dataset, to understand the basic behavior of the model. With the toy model validated, we formulate the final model, which incorporates more complexities based on the real dataset. Following that, a case study of The Gambia was conducted to validate the effectiveness of our model and to provide useful insights in a real-world context regarding the applicability of our solution procedure. The results of the case study show the ability of the model to increase access to immunization. Through the opening of new outreach sites and the optimization of outreach allocation and scheduling, it would be possible to increase the immunization access from 91% to 97.1%. Furthermore, our analysis contributes by showing that the better determination of the shape of a demand coverage function is a promising area of future research. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126392 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126391 Increasing Supply Chain Visibility by Incentivizing Stakeholders to Use Blockchain Dasan Potty, Vijay Krishnan; Yu, Zhehao With increasing customer expectations for fast and cheap deliveries and competition to capture market share, retail organizations are increasingly compelled to make their supply chains as efficient as possible. A major driver of inefficiency in supply chains is the lack of visibility of the goods, information, and financial flows. This lack of visibility leads to decreased customer service levels and increased disputes in the supply chain. To tackle this problem, companies are investing in supply chain visibility tools such as blockchain technology. But there is no clear understanding of the impact of this new technology on supply chains. Our research models the transportation network of our corporate partner, Walmart, through system dynamics methodology and quantifies the impact that blockchain technology would have on the transportation service level and the number of shipment-related disputes. Our results suggest that when stakeholders in a supply chain introduce blockchain-enabled visibility technologies, there is a significant increase in the percentage of deliveries that are on-time and in full (OTIF), and a reduction in dispute management costs. At the same time, there are several disincentives and challenges, such as high setup cost and lack of understanding of the technology, that Walmart needs to consider to increase blockchain adoption among its stakeholders. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126391 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126390 Reducing Oil Well Downtime With A Machine Learning Recommender System Madrid, Jesus; Min, Andrew The oil and gas industry plays an important role in the world’s Gross Domestic Product by providing energy resources to the world. With the price for oil commodities falling in recent years, oil and gas companies require high operational efficiency in order to maintain profits. Unplanned downtime leads to high unnecessary costs representing on average 7.95% of the cost structure of companies in this capitalintensive industry. As a solution, companies have turned to advanced analytics and Big Data to reduce downtime and maintenance costs. This study involves the development of a machine learning recommender system intended to reduce unplanned downtime at oil well facilities. The developed recommender system uses the similarity among customers to predict future purchases and make product recommendations. Predictions are a function of the k-nearest neighbors to each customer, determined using the Euclidean distance or cosine similarity. We followed a binary classification machine learning approach with imbalanced classes by first splitting historical sales data into a training and testing dataset. Then we used the F-2 score and Precision-Recall curve to validate the models’ performance in making accurate recommendations. Recommendations group similar products or services together, reducing the number of times an oil well is taken down for maintenance, therefore reducing downtime. Our results show that this recommender system could lead to a reduction of 1.7 days of downtime and produce cost savings of $2.5 million per customer per year, equivalent to 6.44% savings. The additional products or services sold could lead to additional revenue of $660K per year for the sponsoring company. The recommender system was based on one specific product line within the company, so we believe there is additional opportunity to scale it for larger downtime reduction and increased revenues. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126390 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126388 Machine Learning and Optimization-Based Modeling for Asset Management Casey, Justin; Rafavy, Carlos This capstone project is sponsored by a water technology company and particularly covers its industrial pump rental business across the United States. With millions of dollars of annual spending for pump mobilization, the company looks for ways to improve the overall asset utilization rate. At its current practice, the company has not regularly used any statistical method or algorithm for demand prediction. Moreover, decisions for asset movement between branches are largely arranged between individual branch managers on an as-needed basis. We propose an improvement for the company’s asset management practice by modeling an integrated decision tool which involves evaluation of several machine learning algorithms for demand prediction and mathematical optimization for a centrally-planned asset allocation. We find that a feed-forward neural network (FNN) model with single hidden layer is the best performing predictor for the company’s intermittent product demand and the optimization model is proven to prescribe the most efficient asset allocation given the demand prediction from FNN model. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126388 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126387 Continuous Multi-eEchelon Inventory Optimization Mathur, Sundeep Global supply chains are becoming increasingly complex systems that drive significant investments in inventory throughout the network. Our sponsor for this project uses a multi-echelon inventory optimization (MEIO) model to manage safety stock inventory across its network. The MEIO model helps them optimize inventory based on upstream and downstream supply chain performance but it does not guarantee year over year reductions in inventory levels that the company desires. To address this issue, we studied how the company can better utilize MEIO to systematically reduce its inventories over time and created a methodology that can be employed by other companies also. We applied the methodology on two products that are presented as case studies. For the chosen products, we found that variation in supply lead time is the primary reason for high MEIO safety stock values. We further identified the underlying cause of variation and provided recommendations to reduce variation in lead time in each case study. This research creates a framework that companies can use to systematically minimize MEIO safety stocks and presents case studies that apply this framework to minimize variation in supply lead time on two products and their corresponding MEIO safety stocks. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126387 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126384 An Omnichannel Distribution Model to Better Serve Online Customers Aouad, Wassim; Ganapathi, Nikhil With the rising adoption of e-commerce and online shopping, many retailers are facing the challenge of transitioning across channels to offer a seamless customer experience. One way of addressing this challenge consists of leveraging omnichannel retailing. Our sponsor company, a large US grocery retailer, is striving toward providing an omnichannel customer experience in the US retail grocery market. To help our sponsor to achieve this objective, we analyzed how to best integrate the company’s offline and online distribution channels for one of its brands in Massachusetts. We leveraged the insights from the analysis to build a mixed integer linear program that optimizes the company’s operational costs while meeting the customer demand and complying with the facilities’ capacity constraints. We also conducted multiple scenario analyses, such as an unexpected increase in the online demand due to unforeseen situations like the COVID-19 crisis, in order to assess the flexibility and robustness of our proposed model. Our omnichannel model enables the sponsor company to achieve substantial cost savings, as the associated transportation, handling and facility opening costs are ~22% lower than those incurred by the current distribution network. Finally, the scenario analyses demonstrate that our omnichannel model is flexible and reliable, allowing our sponsor to absorb a 37% increase in the online customer demand in the most cost-effective manner (i.e., without having to incur additional costs on top of the current network’s costs). Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126384 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126383 Utilization of the American Truck Driver Zhang, Mei Qing; Buttgenbach, Adam The electronic logging device mandate was implemented with the intention of keeping truck drivers in compliance with the hours of service regulations to reduce driver fatigue and trucking accidents. Two years after the electronic logging device mandate became law, there have not been many studies that use trucking operational data such as the newly available electronic logs to look for efficiency gain. Our team received six months newly available raw logging data. This paper aims to use different analysis techniques in machine learning on the raw electronic logging data to find areas of opportunity that can be used by management to control and improve driver utilization. The three significant factors that we investigated for on the amount of time a driver spends at each freight location are: the time of day the driver arrives at a shipper location, the impact from a specific location, and the frequency that the carrier visits a specific shipper. Each of the three factors were found to imply a statistically significant impact on the stop duration. This study shows the usefulness of using electronic logging data to identify the underlying factors on stop time so that managers can schedule truck drivers more efficiently. This will allow for higher driving hours during the day, which translates to higher income for the drivers. Since the raw electronic logging device data is readily available for all On The Road carriers, we hope to inspire further data analysis on electronic logging device data to help improve the lives of truck drivers. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126383 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126382 Optimizing Fleet Utilization by Adjusting Customer Delivery Appointment Times Copley, Colleen; Lu, Charles The supply of trucks and drivers is struggling to keep up with the increasing and volatile demand for ground transportation. As a result, for companies like Niagara Bottling LLC., supply chain managers are pressured to optimize their logistics networks. Niagara Bottling is projected to deliver over 1 million full truckloads of bottled beverages to customers across North America in 2020 and transportation costs are already their second highest contributor to Cost of Goods Sold (COGS). Currently, Niagara’s customers have overlapping delivery window requirements which cause significant fluctuations in delivery volumes throughout the day. Niagara hypothesizes that if these delivery appointments were more evenly distributed throughout the day, the same number of loads could be delivered with fewer trucks and therefore less cost. A heuristic algorithm is created to maximize fleet utilization by modifying these delivery appointment windows so that multiple scenarios can be compared based on fleet utilization and cost savings metrics. This paper will further articulate the methodology and assumptions used to generate these scenarios and provide context to the recommendations for utilization improvement on Niagara’s logistics network. Regions with high customer mix saw increases in utilization as high as 25% and decreases in cost as high as 45%. Regions with high delivery volumes saw increases of utilization as high as 13% and decreases in cost as high as 18%. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126382 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126381 Conditions for Deep Supplier Engagement: A Cross-Case Comparison Gerhart, Gina Building deep, strategic supplier relationships has come to the forefront of companies’ goals in recent years. There are many different strategic sourcing methodologies available to procurement professionals. However, there is a gap in identifying the reasons and motivations as to why companies develop their suppliers, and how suppliers are developed in different business environments and contexts. To address this question, this study used semi-structured interviewing in support of a cross-case comparison approach. Analyzing the similarities and differences between modern businesses and how their sourcing decisions are made is crucial to better understand the motivations for developing suppliers. It was found that many companies have similar goals when investing in supplier health, but that the sourcing approach might differ based on age and size of the business, along with the stage of growth the business is in. This research shows that there is no “one method fits all” when it comes to strategic sourcing. The strategy needs to be more tailored to the current business needs and goals. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126381 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126380 Scenario Planning for Offshore Wind Supply Chains 2030 Chen, Haiyin The offshore wind industry is expected to be a major contributor to climate change mitigation and renewable energy transition. Supply chain challenges abound in realizing the potential of offshore wind development. This study focuses on devising supply chain strategies especially toward China, to help energy companies fulfil offshore wind development goals. Scaling up offshore wind requires a long-term view; therefore, scenario planning was utilized to help decision makers tackle uncertainties by preparing for several possible futures. Twelve key driving forces were identified. Three scenarios for 2030 and potential supply chain strategies were developed and surveyed in an energy company and an industry business network. Results show different focus areas of sourcing, construction, assembly and installation strategies based on markets and scenarios. The study contributes to decision making for shaping the future of offshore wind supply chain. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126380 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126379 Application of Linear Models, Random Forest, and Gradient Boosting Methods to Identify Key Factors and Predict Truck Dwell Time for a Global 3PL Company Benjatanont, Sireethorn; Tantuico, Dylan Driver dwell time is an important challenge the U.S trucking industry faces. High, unplanned dwell times are costly to all stakeholders in the industry as they result in detention costs, declining performance and decreased driver capacity. With the increasing demand for these services, it is important to maximize the driving time of drivers in the industry by minimizing dwell time to free up capacity and provide competitive wages. This project utilizes the data of a third-party logistics company with the goal to understand the factors that influence dwell time, and to construct the model to predict dwell time of a load. In the analysis, linear models, random forest, and gradient boosting methods were explored based on regression and classification approach. Ultimately, the random forest classification model with one-hour bins is the recommended model as it had the highest predictive performance while the one-hour bins was sufficient to meet the business need. Additionally, the analysis concludes that shipper facilities are the most significant driver of dwell time. Hence, understanding and integrating more granular observations on shipper practices within their facilities will allow a third-party logistics company to improve its driver fleet utilization and increase the predictive performance of their dwell time prediction model. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126379 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126378 Supply Chain Coopetition: A Simulation Model to Explore Competitive Advantages in Logistics Pedreira, Henrique Berbel; Melo, Tarso Supply chains continuously face pressure to increase efficiency and differentiation to support business continuity. A not yet fully explored way to face this challenge is coopetition, where two competitor companies decide to partner on specific functions to get benefits and differentiate themselves from other companies. This project uses data from two world-renowned food manufacturing companies in Brazil as a case study to evaluate the quantitative benefits of the coopetition approach in terms of transportation cost, CO2 emissions, and service level. Using a simulation model, this study demonstrates that the supply chain coopetition can drive importantbusiness advantages. This research also shows that if companies adhere to the coopetition approach without implementing collaborative policies, the overall costs, CO2 emissions, and service level benefits are approximately around 5%, which may not be enough to motivate the companies to get onboard. Therefore, the study proposes policies that can leverage the reduction of outbound transportation costs up to 25%, the decreasesin average lead time up to 10%, and the drop in total CO2 emissions up to 23%. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126378 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126377 Resource Optimization During Merger and Acquisitions Transactions Ahmed, Bilal; Jung, Sae Pil Mergers and Acquisitions have become means of a quick transformation for companies while basic guidelines related to resource allocation during a transaction are not available. Therefore, this capstone project set out to determine a mathematical approach with the aim to estimate the number of human resources required to create a stable supply chain operation during the sequential merging and separating of subsidiaries. We approached the problem in two steps. First, we used Mixed Integer Linear Programming (MILP) to calculate the optimal resource allocation number after divesture of the business units. The optimization was helpful to find the baseline resource requirement, but the result still generated backlog as the calculation was made under deterministic conditions. In step two we added flexibility to our model through functional simulations to capture the effect of uncertainties. Allowing us to adjust the center of amplitude related to backlog (performance metric for our system) as close to '0' as possible. After conducting the simulation-based optimization, we revealed the most advantageous resource allocation options while simultaneously providing beneficial insights for strategic decision making by the executive management. As a result, we were able to reduce the absolute number of required resources from 13.22 to 11.71 while enabling stable post-merger operation through a scalable and adaptable resource-allocation model. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126377 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126376 Designing an Efficient Supply Chain for Specialty Coffee From Caldas-Colombia Botero Lopez, Santiago; Chaudhry, Muhammad Salman The coffee industry generates annual revenues of approximately US$250 billion, with more than 60 million people depending on it globally. However, coffee farmers in producing countries are operating at a loss due to selling at low prices to intermediaries who control the downstream channels. Recent studies highlighted the need to open channels for farmers to access the market of finished products (roasted coffee). However, most farmers do not have the skills and resources to create these alternatives. In this research, we develop a cost minimization model that determines the most cost efficient supply chain network configuration, starting from the farmers in Caldas, Colombia, to the Northeastern region of the United States. We formulated a MILP network model with a cost minimization objective. We considered several candidate facilities, transportation modes, and coffee processing technologies in our model. Our model also considers multiple periods, multiple echelons, single product, and weight and volume variations along the supply chain. The model was solved for different demand scenarios and the optimal solution was analyzed for each. The outcome is a better understanding and a framework of the optimum cost network configuration, which can be implemented by producers. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126376 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126375 Building Sustainable Supply Chains in the Era of e-Commerce Gatmaitan, Christian; Yangali Del Pozo, Lisha Consumer preferences are driving changes within the retail space. E-Commerce is growing rapidly and there are increased pressures on companies to be environmentally friendly, yet still cost-competitive. Therefore, it is of the utmost importance for retailers to adjust their distribution network to accommodate these new customer preferences. Four key steps of this process are (1) measuring the current capacity of the system, (2) forecasting future demand, (3) adjusting capacity in order to meet this forecasted demand while minimizing cost, and (4) quantifying the environmental impact of each adjustment. Our research focuses on applying these four steps to Coppel, a Mexican retail chain. A time study and capacity analysis were completed to understand current-state capacity. An annual forecast was created using the Holt-Winter Method. This forecast was used as an input for our developed Mixed Integer Linear Program (MILP) that minimizes cost while still meeting customer demand. Finally, the Greenhouse Gas (GHG) method was used to quantify the environmental impact of each cost-reduction measure that the MILP suggests. Our approach reduced the processing cost per unit for Coppel by 4.8% for a total savings of $126M when compared to their previous current-state. This result verifies that our four-step approach can be utilized to reduce cost and therefore yield higher profits for companies in the retail space. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126375 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126373 Assessing the State of Supply Chain Sustainability Barrington, Ashley; Allegue Lara, Laura Supply chain sustainability has increased in importance for companies of all sizes, public and private, across a wide range of industries. While there has been increased excitement in tandem with proclamations of lofty goals around the topic of supply chain sustainability, it has proven challenging to operationalize sustainability when many companies focus on short-term financial goals or lack science-and context-based sustainability targets. The focus of this research is to understand current and future supply chain sustainability practices from the perspective of frontline professionals, across industries, geographies, cultures, and regulatory environments in 2019. This research gathered insights and data through a survey distributed to frontline supply chain professionals, executive interviews, and additional research sources. Results confirm increased corporate interest in supply chain sustainability. However, misalignment may exist between executives who set overarching corporate goals and strategies and frontline professionals who are tasked with the tactical implementation of these strategies. Companies struggle to implement sustainability initiatives under constrained resources with conflicting priorities. Results also indicate that companies may be overstating social and environmental goal commitments, as overall investment levels are lower than goal commitment levels. To better understand these issues and how companies are adopting supply chain sustainability, this research project was commissioned by the MIT Center for Transportation and Logistics and the Council of Supply Chain Management Professionals. To set the stage for future State of Supply Chain Sustainability reports, we will reveal the results of our research on supply chain sustainability in 2019 with an added focus on what the events of 2019-2020, such as the global COVID-19 pandemic that is still unfolding at this writing, could mean for supply chain sustainability in coming years. Fri, 24 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126373 2020-07-24T00:00:00Z https://hdl.handle.net/1721.1/126279 Predicting and Planning for the Future: North American Truckload Transportation Sokoloff, David; Zhang, Gaohui The trucking industry is crucial to the United States economy. An overwhelming majority of goods transported across the US are moved in trucks. For most companies, truck transportation is a prominent component that impacts their production, warehousing, customer service, and overall business performance. In fact, trucking constitutes one of the largest operational costs for a company. Trucking costs are highly volatile due to their association with the capricious freight industry and the US economy. Unexpected market fluctuations inevitably disturb companies’ budget planning and operations, as well as impact their profits. This paper formulates a machine learning model to predict the US truckload dry van spot rate and a playbook of contingent actions. The model variables target and recognize the key elements in the trucking industry and the economy. Tested across 6 years of data, the model achieved an average MAPE below 7% and mean error below 0.05 for predicting 12 months in the future. The strong forecast accuracy allows companies to employ our playbook’s strategic and tactical measures to mitigate risk and unplanned costs stemming from the volatility in the US trucking market. Tue, 21 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126279 2020-07-21T00:00:00Z https://hdl.handle.net/1721.1/126277 Going Awry: Understanding Transportation Budget Failures Bandaru, Venkateswararao; Dolci, Emilio The US truckload transportation industry goes through phases of over and under supply of capacity, causing a dramatic impact on freight rates and transportation budgets. External factors like macroeconomic conditions, unexpected market forces, and changing regulatory policies tend to influence the velocity of these phases. We present an analysis of factors affecting the transportation budgets within the ambit of the transportation industry and shippers’ procurement processes. The results of our research suggest that prevailing truckload market conditions impact shippers’ transportation budget accuracy. The volume variation of a lane, and the origin or destination states, also have an impact, to a lesser degree. Higher awareness of the market conditions that influence transportation budget accuracy will allow shippers to be more effective in their planning processes. Tue, 21 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126277 2020-07-21T00:00:00Z https://hdl.handle.net/1721.1/126275 Traditional routing guide performance and segmentation to improve compliance with contracted budget Alnajdawi, Tala; Jimenez, Israel Lopez A strong seller’s market in 2017 and 2018 led to dramatically increased costs in transportation due to demand surpassing supply, government regulations, and a shortage of truck drivers. As a result, the carrier rejection rate by primary carriers in the routing guide increased. This research examines the performance of routing guides to segment freight to help shippers identify and characterize where and how budget overruns occur. Using data characterization and regression modeling, we examine the plan data (carrier/lane/volume) and analyze how the transactions performed against it. We analyze one year’s worth of shipper data from March 2019, when the plan was made, to March 2020 for three shipper sizes. We classify how lanes perform relative to the planned budget to determine the underlying factors that contribute to budget overruns by creating a freight categorization framework. A linear regression model was built to quantify the impact of independent variables such as distance, lane volume, origin/destination, and lane freight types (dry/refrigerated/frozen) on spend, volume, and total cost contribution to deviations from planned budget. The research found that frozen lane freight loads contribute to higher budget deviations, while dry van loads tend to contribute to lower budget deviations. Furthermore, specific origins and destinations impact budget deviations depending on the shipper. While volume deviations contribute to budget overruns more than price deviations. Finally, we provide insights to determine better segmentation strategies for procurement and management of transportation bids in the future. Tue, 21 Jul 2020 00:00:00 GMT https://hdl.handle.net/1721.1/126275 2020-07-21T00:00:00Z https://hdl.handle.net/1721.1/121370 Horizontal Collaboration in Last Mile Delivery of Online Grocery Orders Nagarathinam, Arun; Zhang, Minhui Digital technology advancement has been greatly changing grocery retailing customers’ behavior and expectations. Enabled by digital technologies and pleased by the digital disruptors, customers nowadays are expecting convenient online shopping and fast delivery. To retain their loyalty, retailers are forced to provide better services through digital transformation. World’s leading retailer Walmart faces the same challenges and takes proactive initiatives to stay competitive. Our study focuses on one of the Walmart’s initiative - the launch of Walmart’s online grocery platform. We propose a framework to help build up the logistics platform that allows horizontal collaboration. The framework includes such steps as identifying a partner, calculating the cost benefits, creating. The partner selection and simulation are performed by a model developed by the team which provides the basis to solicit the partners to collaborate on the platform. The model is used to calculate the delivery costs (pre-collaboration) from two locations (source and destination). The model will also calculate the delivery costs (post collaboration) across multiple destinations using VRP algorithm. Based on the sensitivity analysis results generated via simulation, we prove that order density, delivery time window and the driver engagement are the major factors that influence delivery lead time, delivery costs and SLA. Selecting the correct delivery areas and having the right partners will help the platform build density and order volume. This will create a reinforcing loop where more orders will generate increased demand for drivers and effectively drive down the delivery cost, thus enhancing the competitiveness of the platform. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121370 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121354 Impact of Freight Consolidation on Logistics Cost and Emissions Mohan, Ajay; So, Sik (Lance) The transportation sector is one of the two principal contributors to greenhouse gas emissions (GHG) and its contributions are expected to double by 2050. Growing consumer demand for home deliveries with high service levels at low costs is quickly becoming the standard. As a result, retailers are transforming their supply chains to meet these requirements through innovation in transportation. However fast delivery is a double-edged sword – it is expensive for companies to sustain and generates greenhouse gases from fuel combustion, which is harmful for the planet. Previous studies have shown that customers may be willing to wait for their deliveries if the environmental impact of fast delivery is presented to them. Specifically, customers are willing to wait for up to 4 days for their shipments if greener means of transportation are used for delivery. The increase in delivery lead time will present opportunities to make operational changes centered on consolidating shipments and increasing density in last mile routes, which will help reduce unit cost and emissions. This is the core focus of this project. Our research considers the impact of freight consolidation on Carbon Dioxide equivalent emissions and logistics costs for omnichannel home delivery. We analyze data from a large retailer in Mexico, derive insights on current operational metrics, apply heuristic methods to minimize trips and validate our hypothesis by means of experiments run on specific scenarios. We find that increased delivery lead times can lead to better route utilization, fewer trips to deliver customer shipments and therefore cost saving opportunities and lower emissions. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121354 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121339 Transportation Cost and Tariff Optimization in the Specialty Tire and Wheels Industry Pedersen, Kristin; O'Donnell, Brian Complexity arises as products can take multiple transportation paths that flow from manufacturing sites through to different distribution centers and onward to the end customer destination. Changing transportation costs and volatile tariff rates exacerbate this complexity, as established product flows may become sub-optimal from a cost perspective. Optimization models can be used to determine the lowest-cost solution to ship products from the manufacturing origin to the end customer. This Capstone developed a mixed integer linear programming model for Carlstar, a global leader in the specialty tire and wheel industry. The objective was to identify the optimal routing solution to minimize transportation and tariff costs for each of the company’s five product market segments. The model provided for multiple possible routing options, including shipping direct to the customer from the manufacturer or through a distribution center. Multiple scenarios were run using different rates for transportation costs, tariffs, and customer demand. Model constraints included manufacturing location, demand, and flow balance through the distribution centers. Results indicate that Carlstar could save almost 20% on distribution costs by increasing the number of direct to customer shipments. The impacts of tariffs, demand fluctuations and handling costs were smaller than expected, indicating that once an updated transportation network is established, it would not have to be updated very often to maximize potential cost savings. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121339 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121322 Optimal Green Fleet Composition Using Machine Learning Patil, Vrushali; Samaha, Elissar Due to the use of petroleum-based fuel, the transportation sector is one of the two principal contributors to greenhouse gas emissions and its contributions are expected to double by 2050. Freight sector contributes to around 30% of all transport related CO2 emissions. Since different type of vehicles exhibit different fuel efficiency when operating in different regions and under different load conditions, companies face the challenge of determining which vehicles are more fuel-efficient and have better emissions performance. In this study, we asses carbon emissions and fuel efficiency characteristics of delivery trucks in the inbound delivery fleet for one of the largest retail companies in Mexico: Coppel. Coppel’s inbound fleet consists of 590 trucks, operating in diverse geographies throughout Mexico, making it difficult to direct compare their fuel efficiency. We use machine learning algorithms to analyze Coppel’s trucks’ performance and examined their fuel efficiency for varying road and different traffic conditions. We use these insights to build a green fleet optimization model that considers costs and CO2 emissions performance. By running different scenarios, we observe solutions where CO2 emissions drop by 3.5% with 0.04% increase in costs for Coppel’s inbound fleet. We also observe evidence that brand and age play an important role in the CO2 emissions performance of the vehicles. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121322 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121321 The Impact of Product Portfolio Complexity on Fleet Size Mollard, Juan; Buitrago, Sebastián The assortment of products that a company offers is one aspect that affects the complexity of their supply chain: the greater number of products, the greater the complexity. This project aims to measure how reducing the number of products affect the multi-compartment fleet size in a construction chemical industry company. The reduction of products, called replacement, was done by aggregating the demand of products that have similar characteristics but different density and concentration, without affecting the revenue. The analysis was performed by running 8 scenarios of a Monte Carlo simulation for each of the 11 plants the company operates. The scenarios accounted for seasonality by running one scenario for each quarter, and for product reduction by running each quarter with and without product replacement. The results obtained show a reduction of 7.2% in the number of trucks required for the operation. Most of the reduction (approximately 90%) is explained by the use of higher density of replacement products, which are more concentrated, and the remainder through more efficient loading due to reduced complexity with fewer products. A sensitivity analysis was also carried out on several parameters, such as average distance per trip and average demand, among others. For this problem context, it can be concluded that other factors such as the reduction of the average distance per trip or the decrease of the time per stop have a greater impact on the reduction of the fleet than the reduction of the number of products in the portfolio. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121321 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121320 Last-Mile Distribution Network Optimization in Emerging Markets: A Case Study in São Paulo, Brazil Rivas, Oswaldo; Greene, Kenneth As more of the world’s population moves to cities, urban logistics becomes increasingly important to a company’s distribution operations. Urban distribution network studies must address the same strategic questions as traditional large-scale distribution networks including sizing and locating distribution facilities and determining which facilities should serve which customers. These studies are more complex, however, because they must also address operational problems including vehicle routing and daily scheduling. In our case study, we analyze a business-to-business, last-mile distribution network and evaluate how network performance changes in response to combinations of input parameters like vehicle and facility costs, facility availability, and average travel speeds. We extend a mixed-integer linear programming model, incorporating vehicle-routing and location-allocation problems, to determine the appropriate network design and weekly delivery-route schedule to serve customers in a cost-effective manner. Our analysis shows that a multi-echelon distribution model is not always necessary in urban settings; a single-echelon system is preferred when second-tier facilities are relatively expensive. With respect to labor, significant cost-savings, as high as 35%, can be realized by increasing service time of vehicles through overtime or sub-contracting labor. Common urban problems, including traffic; access to infrastructure; and vehicle travel restrictions tend to increase network costs by small amounts, approximately 10%, and in some cases lead to cost savings of approximately 5%. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121320 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121319 Comparative Evaluation of Drone Delivery Systems in Last-Mile Delivery Garcia, Oriol; Santoso, Antonius With the latest technological advancement, the use of drones has emerged as an innovative and viable business solution for last-mile distribution. An efficient drone delivery system has to address the classic vehicle routing problem (VRP): "What is the optimal set of routes for a fleet of drones to serve a given set of customers?." The goal of this research project is to evaluate the optimal design and operational performance of four different drone delivery systems, using real-life last-mile truck delivery data. The authors quantitatively model four different drone delivery systems, from a pure drone delivery system to an unsynchronized drone-truck system and compare their relative benefits and shortcomings under various scenarios. A Memetic Algorithm, an extension of a Genetic Algorithm, is developed and used to optimize delivery routes of truck and drones for all the four delivery models. Our research shows that Memetic Algorithm is quite robust handling VRP with 50 customers, yielding only 3.7% gap from the optimal solution. Among the four considered delivery models in this research, the Delivery System model 4 - where truck and drone share same area of service - performs superior than other three models, providing 100% coverage to all customers and reducing minimum tour time as high as 80%. The outcome of this research will help shape the quantitative and qualitative comparison of drone delivery systems and set the foundation for modelling and analysis of more advanced systems (e.g. synchronized truck-drone delivery system). It also helps industry to understand the possible use cases for drones in last-mile delivery and the most crucial levers of these models to maximize the performance of such drone delivery systems. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121319 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121318 The Impact of Special Events on the Freight Spot Market Gard, Adam; Famofo-Idowu, Olasunkanmi The United States freight spot market moves in patterns consistent with general economic fluctuations within the broader macro-economy. While the spot market does follow general trends, there is an underlying belief that pricing fluctuations occur as a result of external events. These external events, such as natural disasters, market holidays, terrorist attacks, etc., all fall into the category of “special events” due to their ability to gain a physical presence that alters socio-economic interactions. Socio-economic interactions change the market, but there has been little research into the effect that these special events have on the freight spot market. The aim of this study was to determine the underlying correlation between special events and the spot market and to discern any recognizable patterns that could help the solution providers craft effective strategies to respond to special events. A temporal-spatial multi-variable linear regression model was developed using historical spot market transactions provided by the solutions provider. The model’s results were transferred into a heat map, which was compared against a heat map of the actual values. After testing through 20 models and 3 key performance indicators, no linear correlation could be established. The highest correlation (R2) value was 0.147, which was observed in the model, Outbound Volume for Hurricane Harvey, and the lowest was 0.014, which was observed in the model, CPM for Hurricane Matthew. Linear regression has been the historical modeling technique for spot market rates, but now that the field is beginning to expand into physical and temporal regions of freight understanding, linear regression does not have the capacity to recognize the nuances that extend beyond basic economic principles. While linear regression did not provide a strong correlation between special events and the spot market in this study, future testing utilizing nonlinear techniques, such as neural networks and machine learning, is likely to produce better correlation results. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121318 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121317 Root Cause Analysis and Impact of Unplanned Procurement on Truckload Transportation Costs Aemireddy, Nishitha; Yuan, Xiyang The tender rejection rate by primary carriers for the TMC division of CH Robinson nearly doubled from 2015-16 to 2017-18. An increase in tender rejection rates directly results in an increase in transportation costs for shippers. Increasing demand in the market from 2015 to 2018 was a major cause of the increase in tender rejections. Previous research found that increasing tender lead times leads to a decrease in the tender rejection rates. In this research, we also explored the impact of factors such as lane consistency, lane volatility, corridor volume, carrier type, pickup day of the week, origin-destination characteristics, and tender lead times, on the tender rejection rates and costs. We used three years of customers’(shippers’) tender data from October 2015 to September 2018, which enabled us to capture the differences between soft market and tight market conditions. A linear regression model was built to quantify the impact of each of the above factors on cost per load. Logistic regression models were built to estimate the probability of tender acceptance by a primary carrier as well as the likelihood of a routing guide failure. The research found that shorter lead times have a correlation with higher primary acceptance rates and higher costs. Lane consistency emerged as an important factor in determining tender rejections. Regional sensitivity was also found to be a key determinant of the rate charged by carriers and the likelihood of tender rejection. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121317 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121316 Modeling Large Scale e-Commerce Distribution Networks Calero, Nelson; Zhang, Yao As urbanization and e-commerce continue to grow at a rapid rate, companies are rethinking the way they operate their final-mile distribution networks to prepare for the scaling of their costs. How can companies leverage their existing e-commerce distribution networks to fulfill demand across multiple service offerings, and should they jointly consider inventory control decisions in those design decisions? We explore this question by examining where companies locate inventory, how they use their existing facilities, and how both are used to satisfy demand. This research uses the augmented routing cost estimation (ARCE) formula alongside mixed-integer linear programming to determine the most cost-optimal network design for an e-commerce retailer. Overall, we determined that inventory control decisions have a significant impact on e-commerce distribution networks and should be jointly considered when considering network design. Alongside the solution, the managerial insights derived from our findings support multiple strategies and offer considerations in implementing a redesigned distribution network, especially if a company is limited in their ability to reorder inventory or have limitations in their transhipment operations. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121316 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121315 Increasing Fleet Utilization Through a Heuristic to Determine Optimal Backhaul Routes Tahilyani, Geetika; Venkatesh, Shrihari As transportation costs rise, companies need to become more efficient to remain profitable. One way to increase efficiency in transportation is to increase fleet utilization through the addition of backhaul routes. Most truck routes consist of delivering from a distribution center to stores and then returning to the distribution center empty. Backhaul routes are created when a truck delivers to the last store in the route and then picks up a delivery from a supplier to the distribution center. By adding backhauls to routes, trucks are driving fewer empty miles, and companies have to rely less on expensive third-party logistics providers to deliver material from suppliers to the distribution centers. Backhauls not only reduce operating costs but also result in a reduction in carbon emissions. In this capstone project, we developed a methodology for determining backhaul routes to be added to an established routing pattern. This methodology makes sure that all of the backhauls routes that are found are feasible solution by ensuring the total trip will be completed within 14 hours, which is a federal regulation for the amount of time a driver can be on the road. We then performed a sensitivity analysis to evaluate the robustness of the solutions to changes in the parameters. This sensitivity analysis helped identify the practical solutions that should first be implemented since these solutions have a higher chance of being successful. A case study was performed on Ahold Delhaize, one of the largest food retailers in the world, to evaluate the results from using this methodology. The analysis was performed on one of the operating entities of Ahold Delhaize, Food Lion. We were able to identify 18 feasible backhaul routes that could generate $320,000 in annual savings and reduce 166,800 pounds of CO2 emissions. When applied to the entire company, this could result in up to $1.6 million in annual savings and reduce CO2 emissions by 830,000 pounds. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121315 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121314 Decoupled Capacity with Powerloop Fankhauser, Elisa; Li, Ge With the current imbalance of supply and demand of truck drivers in the U.S., improving driver utilization is critical to secure reliable freight transportation. The largest source of downtime for carriers is the time spent at shippers’ facilities during the unloading and loading process, during which carriers might be detained for longer than two hours and fees charged to shippers. Powerloop provides an alternative model by allowing small carriers to participate in its trailer pool model that provides round trips to carriers and allows shippers to pre-load freight at their convenience. This improves the utilization of carriers by using dropped freight compared to the traditional live-load model. This project focuses on the benefit Powerloop provides to shippers, and assesses the quantitative impact of Powerloop on on-time delivery performance and detention fees. To model the expected on-time delivery and detention fees of Powerloop, we conducted a discrete event simulation by separating each activity during the load delivery process for both the traditional and Powerloop models. The simulation model results indicate that Powerloop loads have a 2% higher on-time delivery rate compared to the traditional live-load freight model, and can expect to save $11-16 per load in detention fees. As Powerloop moves along the learning curve and gains greater density through more customers, we expect these values to improve and provide and even greater benefit to shippers. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121314 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121313 E-commerce and the environment: Finding the optimal location for in-store pick-up Alvarado, Carla; Liu, Yangfei In recent years, ecommerce has been expanding at an increasing rate. Many retailers are making efforts to improve their channel integration to enhance the order fulfillments for their customers. Therefore, companies often see network optimization as a key element within the ecommerce building. At the same time, more companies are trying to make their supply chains more sustainable, and transportation evidently accounts for a large share of CO2 emissions. Nonetheless, the trade-off between cost reduction and CO2 emissions reduction is often difficult to determine, and even harder to see as a tool for a company to make strategic decisions. Our research focuses on an American department store chain with the aim to select the optimal stores as pick-up spots and evaluates the environmental impact of the findings. We use more than 10 million records provided by The Company and develop a binary integer linear programming model to estimate potential savings. We perform a sensitivity analysis to analyze the impact of the customer’s decisions in both economic and environmental magnitudes. Results show the significant importance of two variables: the customers’ willingness to travel and pick up their packages, and the customer’s willingness to avoid using a motor vehicle. Our results include savings of $77K in Massachusetts and $1,319K in California. Finally, with this analysis we provide recommendations for implementing the order-online-and-pick-up-in-store mode in a sustainable and cost-effective way, including educating the customers in using more environmentally friendly transportation modes. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121313 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121312 Facility Location Optimization for Last-mile Delivery Collins, Brittany; Wang, Hao Technological breakthroughs and consumer preferences have led to e-commerce growth in the United States. Given the continued rise in e-commerce demand, the final leg of transportation of goods to the end recipient, or the “last-mile,” is of increasing importance to freight services companies. The last- mile is one of the most challenging and complex aspects of business for such companies, who must manage the cost of getting items to consumers’ doorsteps against an increasing expectation for level of service and delivery times. The sponsor company seeks to become more competitive by optimizing their delivery network through exploring alternative facility location options to reduce the cost to service last-mile deliveries. Seven metropolitan areas were chosen based on volume and significance to the business: San Francisco, Los Angeles, Chicago, Dallas, Atlanta, Newark and New York City. The research hypothesis was that creating a more strategically-located network of cross-docks, each more proximal to common delivery points, would generate cost efficiencies through a reduction in overall travelling distance. The analytical objective was to explore the tradeoff between the additional facilities’ operating costs and the implied savings on purchased transportation costs. We implored a two-step methodology to explore this tradeoff: a center of gravity analysis to identify potential facility locations based on the concentration of customer demand, and a mixed integer linear programming (MILP) model to identify the cost optimal solution given the balance between the transportation cost reduction and the new facilities’ lease costs. The model output resulted in a shortlist of facilities, locations, and costs for each region. The average distance to serve the customer fell by 14 miles (40%) compared to the baseline average distance of 35 miles, and only 8% of customers remained greater than 50 miles away from a cross-docking facility compared to 14% in the baseline. The proposed facilities were $15 per-square-foot-per-year less than baseline lease fees on a weighted average basis. The key takeaway from the facility location optimization model was that adding facilities reduced the overall network cost by 23%, as the reduction in transportation cost to serve the historical demand outweighed the incremental facility lease fees. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121312 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121310 Assessing Feasibility of the Delivery Drone Butcher, Blane; Lim, Kok Service level growth is hindered by declining activity from customers needing access to physical assets stored by the sponsor company. The delivery drone presents a viable option to support the initiative of maintaining service levels while reducing cost. To explore the feasibility of the delivery drone, a comprehensive review of delivery drone technology, application, implementation, and regulations is paired with an operational and financial analysis for the sponsor company. The analysis reveals that, given the current landscape, 0% of current deliveries are eligible for drone delivery, but the future potential is as high as 35%. While the delivery drone is capable of maintaining service levels, it has yet to show cost savings potential or practical operational practicality. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121310 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121309 Alternate Pricing Model for Transportation Contracts Sinha, Atmaja; Thykandi, Rakesh Transportation spend is an increasingly relevant topic of concern for all manufacturing companies. Along with the money spent on transporting goods, service betterment has become an everyday expectation. In a tight market, when contract carriers are unable to fulfill the shipper’s demands, the shipments are tendered to the spot market, where the costs are higher and service levels are lower. Through our study, we developed a dynamic index-based pricing which updates the contract rates on a monthly basis. This not only reduces the auction ratio (percentage of shipments going to the spot market), but also quantifies the incremental line haul savings/costs. We developed an optimization model based on national average line haul rates for contract carriers and spot market published by DAT to maximize the number of shipments moved from spot market to contract carriers, while satisfying various constraints such as cost and monthly variation. Our model shows that 8% of the shipments that had gone to auction would stay with contract carriers for a few, but not all, locations without any additional spend. Shippers can use our model to gather insights and reduce the auction ratio to drive better service levels and reduced costs even in tight markets. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121309 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121308 Gender Impact on Small Firms in Latin America Su, Yen-Nong Traditionally, female business owners have been believed to have relative disadvantages in operating businesses compared to their male counterparts especially in developing countries. This study examines whether gender difference affects the cash management efficiency of micro and small enterprises (MSEs) in Latin America. Cash conversion cycle (CCC), the metric which bridges supply chain upstream procurement and downstream sales, is adopted in this study as the key indicator to measure the cash management efficiency. We observe evidence that inventory management is a critical factor that affects CCC. According to our findings, we recommend that MSEs owners in Latin America who want to improve their cash management efficiency but have limited resources should put their first priority on revamping their inventory management system, such as by building up records and basic inventory policies. In addition, we do not find strong evidence of a gender impact on MSEs in terms of cash management, nor any correlations among financial indicators and supply chain management practices that are significant. We also propose a new cash management assessment that relates directly to inventory management, supplier management and customer management practices, and thus, we suggest to further explore this in relationship with financial indicators. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121308 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121306 Leveraging E-commerce Sites to Absorb Retail Stores’ Excess Inventories Cho, Hyuk; Lahoti, Ketan With an aim to avoid stock-outs, retail pharmacy business constantly deals with the problem of inventory build-up across the supply chain. There are myriad reasons for the rise in inventory such as promotions and forecasting errors. While there is a need to improve the forecasting accuracies with better data coming from the point of sale (POS), the increasing complexities associated with such factors as climate change affecting flu seasons and occurrences of disasters compound the forecasting errors. Hence, there is a need in the system to identify excess stocks being built up across various locations and reduce the inventory. The client is considering to address the problem of excess inventory by sending it from its stores to Mail Centers. The Mail Centers will then sell off this inventory quicker as it fulfills far more prescriptions per day in comparison to the stores. The focus of this study is to develop a model to automate this process of quantifying excess inventory across thousands of retails location for the client. We then suggest a process to reduce the stocks by issuing operational guidelines for each store to pick top stock keeping units (SKUs) and pack and ship them from stores to respective Mail Centers. The frequency for doing out this activity is also suggested after carrying out the cost benefit analysis involved. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121306 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121305 Behavioral Management Patterns: Small Firms’ Recipe for Growth Chacra, Fadi; Rocha, Joshua More than 99% of the companies in the world are micro, small, or medium size enterprises and account for ~70% of the jobs, on average, in OECD countries. However, due to a lack of productivity, among other factors, only a third of them survive beyond 42 months. This paper explores the potential associations between behavioral management patterns and business growth and productivity in micro and small enterprises in Latin America. We analyze survey data collected from Mexico and Colombia and observations from company visits and workshops conducted in Mexico with managers of micro and small firms. We observe that risk, delegation, and goal setting are all influential predictive features for business growth and productivity. We also find that the associations between business performance and these behavioral patterns are better captured through non-linear models when compared to linear models. For example, when evaluating the out-of-sample accuracy for revenue growth, the non-linear model performs ~27.29% better than the linear model. This suggests that behavioral patterns are not independent from each other, but rather interact and combine in ways that can create different formulas for successful behavioral management. In addition, our results suggest that behavioral patterns should not always be viewed in terms of extreme terms such as “high” or “low”, as suggested by linear models; but rather that there are optimal, potentially moderate, bounds for the levels of each behavior. For example, the non- linear model for employee growth shows that managers with moderate levels of risk-tolerance have an increased probability of high growth compared to those who exhibit risk levels outside of the optimal bounds. Last, we also observe evidence that suggests that the willingness to adopt new technologies and processes as a behavioral management pattern has little predictive contribution to business growth and productivity, and may instead, be an indicator of the manager’s inability to perform a particular task or job well. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121305 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121304 Joint Replenishment and Base Stock Model for the U.S. Beer Industry Moison, Nathan The United States beer industry operates, by law, under a three-tier distribution network. MillerCoors[SAC1] has made the strategic decision to open distribution centers across the United States to handle complexity while maintaining service levels. Opening new distribution centers, and introducing a new tier to the supply chain, requires MillerCoors to define a new inventory deployment strategy to maintain their service levels to their customers. This project provides a solution to the inventory deployment problem by determining how a joint replenishment approach combined with the base stock inventory policy improves the deployment of inventory across MillerCoors supply chain. A repeatable and scalable heuristic is developed which employs joint replenishment to determine economic production frequencies and links to the base stock policy across all echelons within the supply chain. The results showed inventory reductions across 75% of all products analyzed. The results also demonstrate the importance of distribution centers to pool demand and improve alignment of inventory deployment across the supply chain. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121304 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121303 Forecasting Model for Sporadic Distributor-Based Market Elazzamy, Ahmed; Park, Stanley With the evolution of technology, more data became available to observe consumer purchase patterns. Traditional forecast methods used to rely on only shipment history. Nonetheless, due to the accessibility of consumer data, a forecast process that integrates downstream flow has shown good results in improving the forecast accuracy in supply chains. In this research we investigate the benefits and validity of linking downstream distributor data in a fast-moving consumer goods company to improve forecast accuracy for intermittent demand. We used multi-tier regression analysis to link distributor sellout data to a retailer in order to predict shipment volume, and then performed a comparison analysis using the Croston method. We concluded that using multi-tier regression analysis has made a slight improvement on an aggregated level; however, the success of this method is subject to data availability that could be a constraint in certain situations. The Croston method has shown significant improvements at the item level and helped to better stabilize the forecast, yet it doesn't consider downstream data. We show a comparison between the two methods, and how to primarily link distributor data in the company's forecast to improve forecast accuracy. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121303 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121302 Raw Material Minimum Order Quantity Optimization Shenoy, Shilpa; Zhao, Ai The sponsoring company, wants to review their raw material ordering policy and production plan for one of their product segments. This product faces a high degree of volatility in demand and the company currently orders one month of demand worth of products from the suppliers. The suppliers offer incremental discounts for larger quantities of raw materials ordered, and the company wants to leverage this discount better. To that end, our research focuses on how to optimize the raw material ordering policy in a way that reduces the total costs, while storing sufficient raw materials to ensure continuity of the production plan. The model we developed provides the optimal minimum order quantity (MOQ) to use while re-ordering raw materials. It also incorporates a switching rule that automatically switches the MOQ value to a higher or lower value depending on the demand forecast and determines the order quantity (OQ) of the raw material. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121302 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121300 Demand Forecasting for Ebola Responses Rains, Robert The accelerating global trends of urbanization, interconnectivity, and population mobility are creating the conditions to increase the frequency, severity, and velocity of future outbreaks of high threat pathogens. Expeditionary interventional outbreak responses play a critical role in localizing instead of globalizing the devastation from these events. Outbreak responses are resource intense, logistically complicated, multi-disciplinary endeavors that require rapid deployment and implementation of highly specialized staff, structures, and systems. The complexity of an expeditionary interventional outbreak response to contains numerous challenges for the supply chain management behind successfully implementing a response. The literature available on outbreak responses are primarily motivated and focused on the dynamics and spread of diseases rather than the operational management of response efforts. This capstone contributes to the field of public health security and humanitarian logistics by presenting a model for forecasting bed-capacity and consumable material requirements essential to response operations. The model in this paper follows the flow of patients from point of origin in the Ebola disease infected (Ebola-positive) and endemic disease infected populations (Ebola-negative) entering the Ebola isolation and treatment (EIT) network through discharge. By mapping patient flow as impacted by several metrics it provides an estimated census in the EIT network under various scenarios. This estimated census is then used as an independent variable to determine the dependent variables of bed-capacity and key material requirements. The analysis of the model’s results demonstrates that Ebola-negative, rather than Ebola-positive, patients are the primary driver of capacity and service requirements for the EIT network. Furthermore, capacity and service requirements of isolating Ebola-negative patients can be substantially reduced by improving time between sample collection and testing for Ebola (diagnostic velocity). In conclusion recommendations are made for further research to solidify our knowledge of response dynamics to strengthen a holistic understanding of response operations to focus solutioning on the critical points of failure that can hinder response efforts. Improving operational methods and tools by identifying and quantifying these dynamics will improve future outbreak response and is necessary for humans to adapt faster than the emerging risks of infectious diseases. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121300 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121297 Analytics Driving Supply Chain Segmentation for Lenovo Gosling, Luiz; Urrutia, Javier Although segmentation strategies and their benefits are common topics in the academic literature, two realities contrast in the realm of supply chain management: While a vast number of companies still perceive operations as cost-centers, only a few have adopted profit-driver approaches. The second group embraced segmentation to deploy end-to-end supply chain strategies that clearly match the segments’ requirements to the company’s capabilities, adding sustainable value in the process. Lenovo Data Center Group (DCG), sponsor of this project, proposed an assessment of their Hyperscale Business Unit’s supply chain and how they could better serve their client portfolio. With that aim in mind, this capstone had three goals: (i) review current frameworks present in the supply chain segmentation literature; (ii) identify DCG’s key client and product groups in its portfolio through quantitative methods; and (iii) propose specific supply chain policy guidelines for each identified group, creating baselines for change. We applied a clustering model to process DCG’s sales and operations records to not only identify clusters of clients and products, but also quantify their differences in terms of supply chain. Data was preprocessed and then ran through an EFA for dimensionality reduction without losing traceability for insights and discussions. Two dimensions were identified: Importance and Complexity. k-Means clustering algorithm was then applied on the resulting dataset, and four client-product clusters were identified. With results in hand, a workshop was conducted with experts from DCG to explore a policy-cluster framework and understand how policies are set according to products' and clients' nature. Our analysis shows that four segments exist within DCG’s portfolio and operating segmented supply chains is likely to positively affect performance. A workshop identified guidelines behind distinct supply chain policies for each cluster and provided a framework for segmented strategies’ design, thus helping managers rethink supply chains to better fit given segments. Moreover, the framework enables a data-driven approach to segmentation, which can be deepened with further analysis – for example, micro-segmentation of clients. To this end, we believe that our work has significant practical implications for Lenovo DCG. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121297 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121296 Investigation of potential added value of DDMRP in planning under uncertainty at finite capacity Ducrot, Leo; Ahmed, Ehtesham The Demand Driven Material Requirement Planning (DDMRP) was introduced in 2011 to improve the performance of supply chain planning. The Demand Driven Institute (DDI) reports that DDMRP reduces the inventory levels by 31% (median) while improving the service level by 13% (median) and reducing the customer order lead time. Such results can have a significant impact on the financial performance of a company and provide a competitive advantage. In this project, we investigate how DDMRP operates in a capacity constrained environment. Qualitative and quantitative techniques were used to collect data about the real-life implementations of DDMRP for different size companies operating in various industries. Afterward, a simulation analysis was carried out to compare the algorithms of DDMRP and Advanced Planning System (APS). Our results show that DDMRP outperforms heuristics-based planning and provides similar results as a solver-based planning. Our survey confirmed the order of magnitude of the improvements claimed by the DDI in terms of service level, inventory level, and customer order lead time. In addition, we learned that implementing DDMRP forces the company to develop extended supply chain training programs across the company. These programs combined with the focus on product flow from the demand driven approach help the companies to streamline their operations. Streamlined operations is essential to maintain the service level high and the inventory low over time. This research proves that DDMRP can perform well in planning at finite capacity under uncertainty. DDMRP can reduce the working capital and offer a competitive advantage, which gives DDMRP the potential to be a game-changer in supply chain planning. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121296 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121295 Replenishment Policies for Retail Pharmacies in Emerging Markets Chu, Kevin; Pizano, Juan Pharmaceuticals account for over hundreds of billions of dollars of the global annual healthcare expenditure. Inventory management is essential for the financial health of the retail pharmaceutical industry. The retail pharmacy studied in this research, faced a challenge managing high-performance inventory policies. This capstone project aims to determine how a set of replenishment policies can help maintain efficient inventory levels and minimize undesired effects of non-centralized discounts and stock-outs in the stores. Our analysis is based on descriptive analytics such as demand frequency, variability, level and profit, data mining and quantitative models such as inventory control, sensitivity analysis and scenario analysis on forecast horizon, stock-out penalty, and customer service level to determine the replenishment policies best suited for the group of prioritized SKUs analyzed. The analyzed policies demonstrate the tradeoff between leveraging supplier-pushed discounts and the increased costs of excess inventory. In addition, the tradeoff between reducing holding costs and controlling stock-out penalties is analyzed. From the SKUs analyzed the research suggests using the (Q, R) policy for high profit SKUs for an average of 33% cost reduction and (s, S) policy for low profit SKUs for an average of 37% cost reduction. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121295 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121294 Improving Inventory Strategies for Consumable Materials in the Aerospace Industry Haber, Jake Aerospace and defense companies often struggle with effective inventory management of consumable material inventories. Unlike piece parts, which have a known and defined required quantity, consumable material requirements may vary from one build to the next, even on different builds of the same parent assembly or part number. This variation and lack of information causes waste, in the form of both expiration of materials and excess labor required to manage these inventories. A main contributor to this waste is an extreme commitment to risk avoidance within the aerospace industry. Aerospace firms do not want material availability to stop the production line for any reason and will often over-order inventory to ensure that does not happen. The threat of shutting down the production line prevents adoption of legitimate and beneficial inventory policy as traditional inventory management strategy is disregarded. This capstone provides the foundation for a new-to-company approach to inventory management strategy in which WOB Corporation (actual company name disguised) may continue to build and improve upon into the future. A pilot program was created and implemented through the course of this capstone project that successfully integrated a new inventory approach for ten strategically important part numbers without negatively impacting ongoing production activity. The pilot program materials were pushed out to the production floor via a Kanban inventory management system for storage until time of use. Benefits realized include an average 50% scrap frequency reduction as compared to the legacy stock keeping strategy. The project also had a significant positive impact on labor cost avoidance equivalent to approximately $15,000 per delivery set of finished products through improved process flows. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121294 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121291 Demand Forecasting and Inventory Management for Spare Parts Chawla, Gaurav; Miceli, Vitor Spare parts management is an essential operation in the supply chain of many companies, owing to its strategic importance in supporting equipment availability and continuity of operations. In many supply chains, the demand of spare parts is inherently more uncertain compared to traditional fast-moving products. This is due to the fact that spare parts demand is highly intermittent, mostly observed with a long period between consecutive orders, where a no demand period is followed by a period of an order signal. As spare parts are critical to the continuity of operations, companies tend to stock more inventories to mitigate the risk of irregular demand pattern. Gerber Technology, a manufacturing company that sells industrial machines and the spare parts that support them, faces challenges in its spare parts demand forecast quality and inventory management. This challenge has recently been negatively impacting the company’s inventory costs and customer service level, where the actual inventory is consistently higher than the targeted level. Meanwhile, higher inventory levels are not being translated into higher service level to its customers. In summary, the company has seen increased costs with a lower service level. Therefore, the aim of this project was to improve the demand forecast accuracy and the spare parts service level of the company while optimizing inventory costs. For this purpose, we used SKU classification for demand categorization and inventory control. With these categorizations, we then allocate the recommended demand forecasting techniques and optimize the inventory levels of the company. By following these processes, we achieved an improvement between 7% to 14% in forecasting accuracy measured by the Root Mean Squared Error (RMSE). We could also gain up to 3% improvement in service level leading to $1.3M additional revenue opportunity. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121291 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121289 Inventory Planning in Engineer-to-Order (ETO) Steel Industry Guo, Dong Inventory planning is one of the most important processes in supply chain management that plays an important role in the success of an Engineer-to-Order (ETO) business. Supply chain and inventory managers in ETO businesses always face challenges in determining an appropriate inventory level because of the uncertainty nature of the ETO industry. In particular, for the steel ETO industry, better inventory planning will help the company reduces the inventory cost significantly. This capstone studies the raw materials inventory planning of an ETO utility infrastructure manufacturer. The current inventory planning of the sponsor company is outdated and does not meet the required service level. Thus, the company aims to improve its inventory management system by replacing the traditional methods with scientific- based ones. In the initial stage of this capstone, several challenges such as uncertain demand, uncertain lead time, uncertain project bid win/loss possibility have been identified that affect the inventory decisions of the company. This capstone focuses on the normal business process, which does not consider unexpected demand surge, and which assumes winning the returning project. This study first identifies the models applicable to the company and then compares the total cost of the selected models under a centralized (aggregated) decision process. Two inventory planning models (s,Q model and R,S model) were studied. The main contribution of the model is to suggest optimal safety stock level, review period, and order quantity to the company. After comparing the total cost of the two models, the (R,S) model was chosen. This model will help the case company to optimize the inventory spending on an annual basis. Sensitivity analysis was conducted on lead time and service level (CSL) of the (R,S) model. Further studies are suggested to capture the unexpected demand surge, uncertain lead time, new project win/loss, etc. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121289 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121288 Comparison and Financial Assessment of Demand Forecasting Methodologies for Seasonal CPGs Gundogdu, Burak; Maloney, Jeffrey Forecast accuracy is an ongoing challenge for made-to-stock companies. For highly seasonal fast-moving consumer packaged goods (CPGs) companies like King’s Hawaiian, an improved forecast accuracy can have significant financial benefits. Traditional time series forecasting methods are quick to build and simple to run, but with the proliferation of available data and decreasing cost of computational power, time series’ position as the most cost-effective demand forecasting method is now in question. Machine learning demand forecasting is increasingly offered as an improved alternative to traditional statistical techniques, but can this advanced analytical approach deliver more value than the cost to implement and maintain? To answer this question, we created a three-dimensional evaluation (cube search) across five unique models with varying pairs of hyper-parameters and eight different data sets with different features to identify the most accurate model. The selected model was then compared to the current statistical approach used at King’s Hawaiian to determine not just the impact on forecast accuracy but the change in required safety stock. Our approach identified a machine learning model, trained on data that included features beyond the traditional data set, that resulted in a nearly 4% improvement in the annual forecast accuracy over the current statistical approach. The decrease in the value of the safety stock as a result of the lower forecast variation offsets the incremental costs of data and personnel required to run the more advanced model. The research demonstrates that a machine learning model can outperform traditional approaches for highly seasonal CPGs with sufficient cost savings to justify the implementation. Our research helps frame the financial implications associated with adopting advanced analytic techniques like machine learning. The benefits of this research extend beyond King’s Hawaiian to companies with similar characteristics that are facing this decision. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121288 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121287 U.S. Consumer Preferences for Seafood Traceability Ray, Sunitha Americans are the second largest consumers of seafood globally but more than 90% of the seafood consumed is imported, causing the seafood supply chain to be long, opaque, and complex. This gives rise to several concerns such as intentional mislabeling, species substitution, illegal unreported and unregulated (IUU) fishing, unsustainable fishing practices and human rights violations in the seafood industry. Despite recent government mandates and initiatives such as the Seafood Import Monitoring Program (SIMP) as well as advocacy from industry and supply chain players, end-to-end information flows from harvest location to the table, remains a challenge. This is mainly due to lack of common harmonized Key Data Elements (KDEs), lack of interoperability, lack of global adoption of standards, and heterogenous systems. Traceability appears to provide a mechanism to alleviate these concerns through enabling information flows triggered by three major drivers of seafood traceability: producers, consumers and regulators. Of the three, consumers are perceived to have the least influence on traceability. This paper investigates whether consumer preferences can drive seafood traceability. It also explores specific consumer characteristics and preferences for whole-chain traceability through direct survey, interviews with industry stakeholders and review of existing literature. The survey results show that consumer preferences play a lesser role as compared to other drivers, due to several exogenous reasons. However, further analysis shows that high propensity for traceability preferences may be influenced by high income levels, frequency of consumption and domicile habitats closer to urban and coastal areas in the U.S. Steps towards establishing an integrated, collaborative and inclusive approach to standardization of KDEs may help move the seafood traceability agenda forward. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121287 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121286 RFID & Analytics Driving Agility in Apparel Supply Chain Kumar, Anil; Ting, Peter The apparel industry is facing significant challenges. Today’s consumers have less patience to wait, and omnichannel retailing is the new norm. This requires the entire apparel supply chain to become more agile, which means that stakeholders need to have better visibility, speed and flexibility. While supply chain digitalization helps the industry to become more agile, enabling technology like Radio Frequency Identification (RFID) has not been adopted in scale. This capstone aims to answer how RFID creates value in the apparel supply chain by improving agility. Based on our sponsor’s RFID pilot, we study the technology’s potential in its logistics & distribution and retail stages. Using process analysis, RFID data analysis, and cluster analysis, we identify relevant value drivers for different stakeholders. In the pilot’s context, we find three clusters: fastmoving omnichannel, online long tail and retail longtail, which have different supply chain characteristics. We also connect RFID data, captured at different checkpoints, with existing system data to generate business intelligence for the clusters. The result shows that RFID improves store KPIs such as daily inventory record accuracies and on-shelf availability. In addition, we analyze supply chain policies for the following value drivers: planning, inventory management, replenishment, and store management. In general, RFID provides end-to-end product visibility, which is beneficial for all stakeholders. Also, there are different levers that can be used to improve speed and flexibility for different stakeholders. Overall, the retail store gains most value from RFID initiatives. Nevertheless, significant value can be created for other stakeholders from advanced analytics and appropriate data sharing. Organizations need to leverage analytical tools and techniques to improve supply chain agility. Our findings can be useful for other apparel businesses that currently use the traditional mass manufacturing model and are seeking to improve their supply chain agility. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121286 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121284 Inbound Incoterm Conversion Brown, Mark; Yadav, Pratik Most organizations spend significant time on improving the processes and systems related to outbound movement of goods, i.e., cargo moving from the production site to customers. This focus often ignores the other aspect of the supply chain, namely inbound cargo movement, which refers to the shipments coming from the suppliers to either the purchasing organization or the production site of the organization. This capstone project investigates the various material buying arrangements (which primarily differ in operational setup and risk) that the buying entity will take on. This capstone project focuses on a selected group of twelve products from two suppliers with different characteristics such as origin, volume, price, supplier relationship, etc. Total logistics costs including the transport cost, purchase cost, and inventory costs are calculated. Risk is also included per incoterm option to give an overview for business managers on how to approach the buying decisions. In general, if the company would like to convert from C&D to E&F incoterms, it would mean that the buying organization is taking more risk upon itself and hence needs to have a solid operational setup (inhouse or outsourced) to manage the ownership responsibilities that come with changing incoterms. The end result is a matrix of selected scenarios which will allow the buyer to understand the risk associated with each incoterm under a set of conditions and the expected cost difference. The section ends up with opportunities that might be presented if the organization starts buying more under E&F incoterms instead of C&D incoterms along with the potential risk rating under each scenario. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121284 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121283 Omnichannel Supply Chain Transformation for Third Party Logistics Providers Kamranpour, Neysan; Konnerth, Marion E-commerce and store fulfilment operations have traditionally been held separate as multichannel supply chains because of their different operational requirements. Omnichannel distribution makes inventory available to several distribution channels that were traditionally kept separate. Separate channels have overlaps, higher transportation costs and do not allow the same customer experience as omnichannel distribution can offer. This work considers a third-party logistics provider’s distribution operations in China to answer the Question: “How will the supply chain transform to support store fulfilment for E-commerce consumers?” A baseline model depicting current operations is first presented, and then develops three omnichannel designs with varying operational constraints. An IT selection framework is finally developed to help recommend the most appropriate Order Management System to enable the omnichannel transformation and answer the question: “What OMS features will be necessary to transform a multichannel supply chain into an omnichannel supply chain?” This work concludes by demonstrating that a successful omnichannel network can reduce current costs by up to 53% and maintain current lead time service levels even if fulfillment demand is doubled during peak periods. The ability of presenting an omnichannel solution to potential customers will present a competitive advantage for a third party logistic provider. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121283 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121281 Simulation Test Bed for Drone-Supported Logistics Systems McCunney, Brent; Cauwenberghe, Kristof Same-day delivery is an increasingly relevant logistics service because of a continued boom in e- commerce. As customer orders are submitted dynamically during the day, companies need to dispatch vehicles from distribution centers to fulfill these orders as they come in. Given their advantages over conventional vehicles such as direct flight, no road traffic, and no driver/operator requirements, autonomous drones have recently been proposed for last mile package delivery. This study examines in what situations drones could be used to resupply trucks to reduce the time and/or cost of delivery. Drones can be used to dispatch packages from the distribution center to transshipment points where trucks can pick up packages instead of returning to the distribution center. The use of drones requires a new transportation network and routing. A simulation using SIMIO was developed to assess the impact of using drones to resupply trucks through transshipment points. Both a subset of the city of Boston and a portion of the rural area around Pittsfield, MA were used with package delivery orders arriving throughout the simulated time. Through dynamic dispatching for same-day delivery, orders were delivered significantly faster when transshipment points were resupplied by drones than in conventional last mile delivery. In the Pittsfield region analysis, the baseline with one transshipment point was 36% faster than conventional package delivery. The total truck distance traveled while delivering packages was also reduced by 24% or on average 80km per 8-hour work day. In the Boston case study, the baseline scenario of 4 transshipment points was 66% faster (2 hours) at delivering packages than conventional package delivery. Four transshipment points also resulted in a 10% or 60km a day less distance traveled for the trucks. Our research indicates that last mile package delivery companies can use drones and transshipment points to reduce package delivery time as well as truck travel distance. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121281 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121280 Predicting Shipping Time with Machine Learning Jonquais, Antoine; Krempl, Florian With the globalization of trade, transit time reliability has become a critical point in the shipping industry as irregularities will lead to more delays further down the supply chain. Our sponsoring company, A.P. Møller – Mærsk A/S (Maersk) provides freight forwarding services to its clients, offering them a complete set of supply chain solutions for shipping their goods across the world. Currently Maersk uses an in-house tool, Harmony, which provides descriptive analytics for shipment times and their variations based on historical distributions. However, Maersk is facing commercial pressure from its customers for a better estimation of its shipment transit time reliability, which has become a key measurement of its operational performance. The goal of our project was to determine whether Machine Learning and predictive analytics can improve the estimated time of arrival for a shipment. Using Machine Learning computing, we developed a model capable of predicting shipping times by training the algorithms on historical shipment data, and incorporating external sources of data related to the most impactful factors regarding schedule reliability (e.g. holiday seasons and port congestion levels). We found that Machine Learning in this instance might be a partial answer to this problem, as it performs better on long lead time than on short lead time when comparing to more classical approaches. Our model has a mean absolute error (MAE) of 3.74 days when making a prediction at the time of booking transportation whereas our baseline model (which only considers historical average transit times on a shipping lane) predicts with a 4.3 days MAE at the same time. When making a prediction at the time the vessel leaves the port of origin, the two models actually perform similarly, with a MAE of 2.1 days for both. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121280 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121279 Incentivizing No-Rush Delivery in Omnichannel Retail Heuser, Alison; Ashraf, Tabjeel E-commerce sales have grown exponentially since the introduction of the smart phone in 2007 and the trend is expected to continue. As retailers enter e-commerce, the pressure to provide more and faster delivery options to the customer is increasing. The resulting complexity and increased delivery speeds are often expensive. Providing incentives to influence customers to choose no-rush delivery is one method by which retailers can seek to lower these logistics costs. Previous studies have demonstrated that monetary incentives can influence behavior; many researchers have studied logistics costs models. This study focuses on the fast fashion industry and combines research of consumer behavior with a logistics cost model to determine the effectiveness of incentives to drive cost savings. Customers were surveyed for lead time decisions in the presence of varied monetary incentives to determine the impact of the incentive on consumer behavior. The customers were asked about both basic items and trendy items to differentiate behavior between product categories. Linear regression of this data showed that retailers need to provide an incentive of $1.18 per day of extra lead time to a customer purchasing a basic item and $1.14 per day of extra lead time to a customer purchasing a trendy item. These incentives were used as an input cost to a delivery cost model. This model used the vehicle routing problem to estimate logistics costs. The model compared the cost of routes with standard shipping to routes that included no-rush packages over one-week. The results showed that it is possible to save an average of 3% to 32% in logistics costs depending on the percentage of customers who opt in to no-rush delivery. This study showed that it is possible to use incentives to influence consumer behavior and that behavior can have an impact on the logistics costs. It is critical to study both consumer behavior and logistics costs together for a retailer to determine the correct incentives to offer. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121279 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/121278 Aggregate Production Planning for Engineer-to-Order Products Cheng, Cheng; Shafir, Elizaveta The contract manufacturing industry is growing and shifting from standard products to highly customized engineer-to-order (ETO) products. Different from standard products, ETO orders have more production process uncertainties because their design specifications and production process can be changed after the orders have been accepted. Such uncertainties increase production costs, the risk of late delivery and associated penalties, exposing the contract manufacturers to profitability decrease. Since every ETO production is unique, companies cannot rely solely on historical data to ensure accurate planning. The goal of the project is to solve the aggregate production planning problem of ETO orders. Usually, uncertainty is mitigated by keeping inventories. Such safety stock, however, does not work for customized products as they are usually a one-time purchase and therefore, cannot be kept on a regular basis. For customized production, buffer time and capacity can be used against process uncertainty. We formulate an Aggregate Production Planning (APP) model as deterministic, multi-product, multi-stage, and multi- period linear programming (LP). It minimizes the total production cost by balancing the in-house production, inventory holding, outsourcing, overtime hours cost, and backlogged orders penalties. Cost drivers for total production cost are analyzed for multiple scenarios with different production times. We then calculated the buffer capacity and performed constraint sensitivity analysis using the shadow price method. Based on the data analysis, we make recommendations for the sponsor company for planning horizon that we model: add an employee for one production stage and remove an employee from another, use 7% buffer capacity for the base plan to minimize the total production cost for the set of all possible scenarios, use a combination of hiring, overtime hours, and outsourcing. These recommendations lead to 12.32% cost reduction compared to the cost if the company does not use an aggregate planning model and the recommendations. Moreover, we formulate an aggregate production planning approach for the sponsor company to use in the future to ensure an optimal plan with the minimum total production cost. Tue, 01 Jan 2019 00:00:00 GMT https://hdl.handle.net/1721.1/121278 2019-01-01T00:00:00Z https://hdl.handle.net/1721.1/118141 Planning for Peak Demand in Reverse Logistics Thakur, Anshu; Teoh, Ian Martin The project sponsor is a reverse logistics company that provides Returnable Transport Items (RTIs) to large manufacturing companies, distributors and retailers. Its business model is characterized by a unique closed-loop supply chain. The seasonal peak demand for RTIs from June to September negatively impacts service levels and costs. The sponsor company seeks an opportunity to level load production and build inventory position, while optimizing the service levels and annual supply chain costs. This capstone project proposes an optimal supply chain plan by analyzing historical data, identifying key cost-service drivers and creating a Scenario Planning Tool (SPT) that demonstrates tangible benefits in terms of cost and service level improvements. The data analysis quantifies the correlation between serviceability (days of coverage), inventory position and supply chain component costs (cost to serve). This correlation is used to model a Scenario Planning Tool (SPT) that recommends the optimal supply plan and directs the inventory policy decisions in order to maximize serviceability, while minimizing the total annual supply chain costs. A key takeaway is that the correlation between inventory policies and supply chain costs provides an opportunity to optimally plan inventory coverage in order to minimize supply chain costs while meeting service level targets. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118141 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118140 A Study of Shipper Performance in the Less-Than-Truckload Market Yin, Ben (Bin); Rallis, Christos W. When it comes to LTL shipping, it can be tough for shippers to get the performance that they expect due to the makeup of LTL networks. On-time performance is dependent on many more factors than in full truckload shipping. Performance often comes down to attributes of the shipment such as size and weight and also attributes of the geographical shipment volume. It is critical for shippers to understand these attributes and how they contribute to on-time performance of their own shipments. Through quantitative and qualitative analysis, this capstone details the shipment, shipper, and geographical characteristics that impact on-time performance of LTL shipments. Data from 33 shippers over a period of nearly two years was provided by C.H. Robinson and TMC (a division of CHR). This data was evaluated through a mix of regression and segmentation methods, as well as through qualitative understanding of the industry and economic landscape. The modeling and analysis here within describe the attributes of high performing shipments and provides guidance for shippers as to how to strive for the best performance. We found that shipment size, transit length, and destination shipment volume are among the largest drivers of on-time performance. Although on-time pick up and on-time delivery share some common significant drivers, significant drivers are not all the same for both. This report dives into further detail to help shippers understand the drivers what they can do to manage expectations and performance of their LTL shipments. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118140 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118138 Operating Strategies for a Segmented Supply Chain Orende, Bernadette An operations strategy is a method or plan of action that corporations employ to reach their goals. A good strategy enables a company to operate efficiently and use its resources effectively. The purpose of this capstone project is to formulate optimal supply chain operating strategies for demand, sourcing, and distribution by replenishment stream for a fast-moving consumer goods company (FMCGC). A replenishment stream is how demand from an external customer flows into the sponsor company’s supply chain. The sponsor company has identified four replenishment streams; base demand, promotions, new initiatives and incremental business activities. It seeks to confirm whether there is a benefit in differentiating the supply chain by the four identified streams of some hybrid. An exhaustive literature review and analysis of shipment data provided reveals that there is no one size fits all demand, sourcing, and distribution strategy in consideration of the different replenishment streams. In addition, through a variety of methods including segmentation, calculation of the coefficient of variation, time series analysis, and the generation of forecasts, I conclude that in certain instances there is a benefit to differentiating the supply chain by the 4 identified replenishment streams and in other cases it is advantageous to consolidate them by some hybrid. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118138 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118137 Apeel Sciences: Lot Traceability of a Breakthrough in Food Science Technology Miller, Ronald Russell; Taylor, Hilary Apeel Sciences has made it their mission to help eliminate food waste. To do so, they are introducing their exciting new product, EdipeelTM, to market. Edipeel is an edible, natural, flavorless coating that, when applied to the surfaces of fresh fruit, reduces the rate at which produce spoilage occurs. The product more than doubles the viable shelf life of harvested produce. As a food product, the production of Edipeel must comply with federal regulations for lot traceability. As Apeel prepares to manufacture Edipeel at scale, they have recognized that their current practices for traceability would need to evolve. To ensure their operations were compliant and scalable, Apeel enlisted our support to evaluate their processes and recommend improvements where necessary. Apeel’s service model for the application of Edipeel adds complexity to their process and became a key focus area of our evaluation. Through onsite interviews and an end-to- end supply chain review, we were able to verify their compliance with federal regulations for lot traceability and identify the areas of their process most vulnerable to operating at scale. We recommended both process and technological improvements to mitigate risk for the future. These recommendations help Apeel to remain compliant as they pursue their mission to help eliminate food waste. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118137 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118135 Learning from Route Plan Deviation in Last-Mile Delivery Li, Yiyao; Phillips, William This capstone project studies route deviations in the last-mile deliveries of a large soft drink company. Last-mile delivery is a critical problem due to its substantial economic impact on operational cost, and deviations from the planned routes can potentially prevent companies from minimizing these costs. We study whether delivery crews systematically, consistently and substantially deviate from the planned stop sequence of their routes. Additionally, we analyze what drives these deviations and whether they add economic value or not. With this objective, we perform regression analysis and build classification models, using one-year data across two countries, Mexico and USA. Our models predict whether a driver will deviate from the planned route, and the impact of the deviations on the route’s distance. The findings show that the customers’ ZIP codes are highly useful to predict deviations. Additionally, drivers are more likely to deviate and increase the route’s distance when more customers are visited, suggesting that this is where the company should focus their efforts. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118135 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118133 Analyzing Upstream Impact on Downstream Shelf-Availability Li, Xu On-shelf availability (OSA) is key in the Consumer Packaged Goods (CPG) industry. In this project, out of stock (OOS) patterns are identified, grouped, and analyzed in order to gain meaningful insights to improve OSA. The approach is to normalize a time-series dataset, search for similar OOS patterns, and analyze some particular patterns. By focusing on a particular type of pattern, in which an OOS event happens within a pre-defined time period, some behaviors are observed from this study: first, as the time period is decreased (from 7 days to 4 days), the similar OOS pattern happens more frequently; second, a steep inventory drop appears to be an infrequent event, based on the sample dataset. The purpose of this study is to draw a common approach that could possibly be used by practitioners; the firm could possibly use the index and similarity search approach to identify patterns in all GTINs. The potential insights of this study are: first, stock outs don’t seem to be predictable based solely on DC data, since steep inventory drops appear to be infrequent events; and second, a firm could possibly use the identified patterns to connect point of sale data with OOS events and then identify the drivers of out of stocks. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118133 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118123 Improving the process of container shipping using blockchain Jain, Puneet Container shipping is one of the most important aspects of the global supply chain. Currently, approximately 60% of all seaborne trade is moved through containers. However, despite the growth in trade, there has been little investment in process improvements which has led to the growth of supply chain issues in the containership industry. Three critical issues were identified that have marred the overall efficiency of the supply chain. The first is customs clearance that creates barriers to trade and inefficiencies. Second is aging technology in the container shipping industry which is creating a lot of wastage in the system. Third is inefficient contracting practices. We create a value stream map as well as a re-engineered process using blockchain. With the use of blockchain, we see that despite certain limitations, the process can be made more efficient as blockchain has the potential to build trust amongst various participants in the supply chain. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118123 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118122 Improving Forecast Accuracy through Demand Sensing Humphrey, Evan M.; Laino, Federico E. This capstone assesses the feasibility and potential value of implementing Demand Sensing in the supply chain of a major consumer medical device company- Johnson & Johnson Vision Care. Specifically, we explore the potential benefits of incorporating downstream Supply Chain data into forecasting, reducing latency between Sales and Operations Planning (S&OP) cycles, and quantifying demand shaping actions. The motivation for this project was to find new sources of efficiency within a constrained production environment . Our approach was twofold: first, to statistically examine the accuracy of the current forecast system and second, to assess the pragmatism of Demand Sensing. The results of our statistical analysis suggest using alternative methods for forecasting slow-moving SKUs. Our research into advanced forecasting methods provide real world recommendations regarding the benefits and drawbacks of Demand Sensing approaches. We constructed three initiatives - one for each Demand Sensing approach - that can be used independently, or in any combination, to create a Demand Sensing forecasting system. The three initiatives are called Latency Reduction (LR), Downstream Data Integration (DDI), and Measuring the Impact of Demand Shaping Actions (DSA). Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118122 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118121 Uberization Effects on Freight Procurement Helguera Sánchez, Ignacio; Hendra Mukti, Lydia Paramita According to a report by A. T. Kearney, in 2016 the US business spent $1,392.64B on logistics costs. 90% of transportation spending is procured in the form of Long Term Contracts. A Long Term Contract drives long procurement cycles that can last over 6 months, which results in significant financial risk for both shippers and carriers. It is estimated that 10% of freight under Long Term Contracts fall out and ends up in the spot market due to low tender acceptance and market volatility. The spot market, on the other hand, can be highly dynamic. Typically, shipper pays 20% more for freight in spot market compared to what would be agreed upon in a Long Term Contract. Regardless of the freight procurement method, shippers are constantly faced with market volatility and are left scrambling to find a new carrier capacity when carrier fall off occurs. Assuming a shipper could book a truck instantly, how would their procurement strategy and supply chain network change? We hypothesized that there is a financial benefit to all parties from faster, more liquid transportation transactions, through lower labor costs spent on freight procurement transaction and shorter planning cycles in transportation procurement. Digital freight matching via an on-demand app, facilitates long arduous transactions in a real- time low-cost manner. Using System Dynamics models, this project developed a behaviorally based conceptual model to analyze effects of a digital freight matching app on shippers’ freight procurement. Our analysis shows the shipper will choose a more efficient, low-cost alternative to the spot market, provided that the shipper’s total spending is lower than what would be agreed upon in a Long-Term Contract. Digital freight matching benefits shippers by providing needed freight capacity at lower cost. Individual changes in market volatility, shipper volatility, app efficiencies, or carrier and shipper adoption rates would potentially add more than 10% variability in freight rate paid by shipper and more than 50% digital freight matching app adoption rate for both shippers and carriers. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118121 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118120 Optimal Supply Chain Operating Strategies by Replenishment Stream Gorman, Mary Kate Agnes In the world of consumer packaged goods (“CPGs”), not all demand is created equal. While Base Demand typically comprises anywhere from 50 – 80% of day-to-day volume, other demand profiles, or replenishment streams, make up the delta. For the purpose of this project, demand patterns for five replenishment streams (Base Demand, Incremental Business Activity, Promotional Activity, New Initiative Phase-In, and New Initiative Phase-Out) were analyzed to determine if and how operating strategies from an inventory, planning, and distribution perspective should be adjusted to maximize end customer service level for a newly launched personal care product. While for certain operating strategies (i.e. inventory) it was found to not be feasible to strategically align processes around replenishment streams, there could be both a financial and operational benefit to align certain processes for planning and distribution with certain replenishment streams. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118120 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118119 Driving Savings via Inbound Logistics Network Design Felicio, Geraldine Mae P.; Sharma, Deepika A CPG company is examining its end-to-end supply chain to find opportunities to optimize both cost and visibility. One minimally tapped source of these opportunities is the inbound supply network. This report studies three design changes to the CPG company’s current inbound supply network, namely: 1) Consolidated Inbound and Outbound Deliveries, 2) Supplier Village, and 3) Reallocated Near-Site Flow and Storage. Design 1 studies reusing inbound delivery trucks as outbound delivery trucks to reduce empty mile costs. Design 2 studies locating suppliers nearer the CPG company’s plants to reduce required lead time and inventory levels. Design 3 studies more efficiently allocating raw material and finished good storage to enable better end-to-end product flow via reduced inventory and handling. Models for calculating transportation, inventory, and handling costs for each design were developed. Current costs were compared with costs if these designs were applied. Overall, the studied designs of the inbound supply networks were determined to be feasible sources of savings for the company. Design 1 showed potential savings worth $800,000 per year. Design 2 generated savings of $886K. Design 3 led to better product flow, resulting in potential annual savings of $2.6M. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118119 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118118 International Production Network Planning Cheung, David; Pieper, Ross A multinational chemical company is looking to include duties and duty credits as consideration factors in its production planning. This undertaking requires understanding of not only domestic trading laws but also international trading laws and how trading blocs can affect them. The company’s products are manufactured and shipped across different regions of the world. In almost every stage of its supply chain, it is exposed to some form of tariffs and credits. In this paper, the researchers present a mathematical optimization model that aims to help the company to achieve production efficiency for one of its agricultural business units while accounting for duties and duty credits. Analysis includes scenario simulations to test different rates of duties, different locations of production facilities, and different sourcing countries. The results suggest that depending on the circumstances mentioned, duties and duty credits can become significant parts of total production costs. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118118 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118117 The hidden impact of micro retailers’ survival rate on the logistics cost of consumer packaged goods companies Castañon Choque, Ximena Millions of mom-and-pop stores represent 40-70% of the market share of Consumer Packaged Goods (CPG) companies. Many of these stores are located in megacities in developing countries, cities over 10 million people, where characteristics such as traffic congestion and a dense population make the last-mile delivery process very complex. Therefore, the CPG companies that achieve efficient logistics to reach these stores will have an advantage over their competitors in terms of being top global retail players. However, many mom-and-pop stores disappear in developing countries every year due to poor productivity performance, while others appear due to low barriers of entry. Overall, the number of mom-and-pop stores keeps growing. In this capstone, we study the impact of the dynamics of their appearance and disappearance on the logistics costs of a distributor company that gets supplies from CPGs and delivers directly to mom-and-pop stores in Mexico City. We use cost-to-serve estimations and continuous approximation models for routing to show that by improving the survival rate of mom-and-pop stores, CPGs may avoid loses in transportation costs of up to 31%. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118117 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118116 3D Printing’s Impact on the Metalworking Industry Ingberman, Aline Blanche Grynbaum; Assavaniwej, Sittipat This project qualitatively estimates the potential impact of 3D printing on the United States metalworking industry. The adoption rate of 3D printing has been increasing in the last few years. On one side are businesses with long development and production lead times, competing in fast-paced industries. Moreover, some businesses are in the situation in which they need to reduce their supply chain complexity, to reduce costs and lead times. On the other side are businesses that are exploring the capabilities of Additive Manufacturing (AM) in their relationships with individual customers. A good example is the health care industry, where the need for customized implants like orthopedic replacement parts and tooth crowns has made this industry an early adopter. Using data collected through site visits, an online survey sent to the sponsoring company’s main customers, as well as interviews with companies currently using the traditional material removal manufacturing process and companies basing a large part of their operation on 3D printing, we studied the trends, main points of attraction, and barriers to AM adoption. Our analysis suggests that 3D printing will be mainly used for prototyping, at least in the next 3 to 5 years. While some companies, especially in the health care industry, also are using it for their daily operations, almost no survey respondent envisioned using 3DP for mass production. Currently, the main barriers to AM adoption are the high initial investment (a barrier that probably will be broken in the next years, as the adoption of the technology increases) and the limited variety of metals available. The key benefits that business expect to derive from using 3DP are reduced costs and lead time. In today’s globally competitive market, being able to respond fast while keeping competitive prices could be a key differentiation factor, and may help adopters achieve that. Finally, our study also indicates the size of the companies that are more likely to adopted 3DP. Small business (1 to 50 employees) are most likely to adopt, followed by very large business (with more than 1000 employees). The adoption horizon is almost evenly distributed among the next one to three years, the next 3 to five years and the next 5 to 10 years. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118116 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118115 Optimizing Product Group Segmentation Budhiraj, Anushka; Du, Ye Piece picking is an integral operation in distribution centres (DCs). The partner company has it as the largest part cost item of DC operations. The current product slotting and assignment planning needs to be improved to improve space utilization and reduce labour expenses. The team approached the problem by using a two-staged ABC segmentation method to determine the a more efficient slotting. The derived slot assignment results in an average saving of 27.62% in travel distance. This Capstone therefore offers a novel perspective into piece-picking optimization and improves efficiency and cost effectiveness. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118115 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118114 Improving the Survival Rate of Small Firms in Latin America: A case study in Aguascalientes, Mexico Arizaleta Valera, Mirna J.; Zhu, Xiaohan Micro and small firms represent the largest percentage of the businesses operating in Latin America having a high employability contribution and a high potential Gross Domestic Product (GDP) impact on the region. However, depending on the context of the country, region and sector nearly one out of four companies fail to develop into a high-growth firm every year. The focus of this research is to identify the critical Business and Supply Chain practices that improve micro and small firms’ survival rate considering the managerial qualities involved in the development of the firm. To conduct this analysis, we propose a model as result of the deep dive with companies in Aguascalientes, MX. This model classifies companies within a Survival or Success Stage based on the delegation level of the organization, main challenge of the firm, and the main activity driver for managers. From this model, we conclude that most firms in Aguascalientes, MX are in Survival Stage despite its years of existence. As part of a larger project developed by the Center of Transportation and Logistics at MIT, we analyze the existing data from small firms in Bolivia, Colombia, and Peru by applying the modified version of our first model to identify the critical practices. In addition, we conducted two workshops in Aguascalientes, MX where we gain insights about that main challenges of small firms. Based on further analysis, we identify Financial Planning and Operations Management as the critical practices with the highest gap of adoption (15%) compared to firms in Success Stage. Moreover, we conclude that the managerial qualities of the owner have an important impact on the development of the firm. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118114 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118113 Online Grocery & Omnichannel Strategy: Predicting Home Delivery Adoption Alberts, Ryan Alexander; Lahad Abinader, Antoine The traditional way to reach customers in e-commerce is home delivery. Retailers have expanded fulfillment options to include picking up from a store, locker, 3rd-party collection point, and more. This study focuses on two channels: pick-up from the store and home delivery. Groceries present a unique category for eCommerce due to particularly onerous complications from last-mile delivery of fresh products. Existing research is lacking in comparisons of channel options in the context of online grocery that capture interactions of channel and customer attributes. This study identifies critical markets for home delivery of online grocery and provides insights into drivers of channel choice in this context. It does so by first modelling home delivery adoption – applying machine-learning algorithms to historical customer data – and then analyzing channel preferences via a Discrete Choice Experiment devised by the authors expressly for this study. The study quantifies the importance of geographic features in home delivery adoption, including density of existing online grocery customers and their distance from a store. The study also quantifies the likelihood of customer channel preference given varying channel attributes; for example, a customer is no more likely to choose pick-up from store if it is ready today vs. tomorrow. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118113 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/118108 Predicting Carrier Load Cancellation Al-Habib, Ali; Favier Gonzalez, Nicolas Truckload cancellations by carriers are causing disruptions in the trucking industry operations. By extrapolating the findings from the 3PL’s data studied in this research to the whole trucking industry, it is estimated that 32 million cancellations occur every year. These cancellations result in around $4.6 billion extra cost. If these cancellations can be predicted, shippers and transportation brokers can avoid loss of money and resources caused by the required rebooking process. This research explores the key drivers of loads’ cancellation using historical cancellation patterns. It evaluates the applicability of different predictive models that were built using three-year data from a third-party logistics provider. These models include logistic regression, random forest, neural networks and k-nearest neighbors. However, the research focuses mostly on logistic regression, as it provides more insights of the main drivers of the cancellations. The resulted models were capable of correctly predicting only 16% of the cancelled loads. In effort to improve the accuracy of the logistic regression model, tradeoff analysis was developed to study the impact of adjusting the threshold. The analysis showed that using lower threshold can improve the correctly predicted cancellations to 42%. However, for every additional cancelled load predicted correctly, around 3 uncancelled loads are predicted as cancelled. As all models gave comparable results, the research concludes that the available load information and historical cancellation behaviors are not enough to predict future cancellations. The research concludes by recommending business solutions to be implemented in order to reduce the probability of cancellations. These solutions include educating carriers on the impact of cancellation and encouraging them to cancel with longer timeframe when cancellation in inevitable. Moreover, further research might focus on surveying carriers to identify the root causes of cancellations and capture details related to these causes. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/118108 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117953 Pattern Recognition in Consumer Packaged Goods Data Aboutaleb, Hanin The speed at which a manufacturing company analyzes big data and reacts to the market trends can be key to its success. In consumer packaged goods (CPG) companies, a delay in analyzing the huge amounts of data produced could result in missing opportunities to identify patterns that indicate problems, and therefore, delay response initiatives to prevent problems. This was the case with a leading CPG company that was experiencing lack of on-shelf availability of some of its profitable products. It was necessary to analyze internal data to identify where and why this problem existed. Data collection, selection and analysis were conducted over the inventory data and sales data of a leading brand at a large retail partner. This analysis enabled us to identify patterns in inventory records, including short replenishments cycles and frequency of out of stocks. It was significant that, during a few months following product introduction, there was a high frequency of out of stocks. This reduces the product’s availability on the retailer’s shelf; hence, the service level drops. A few months later, replenishment policies of some products were changed to maintain their inventory above safety level. Upon pattern identification, we provided observations on which specific stock keeping units (SKUs) and distribution centers (DCs) experienced the biggest issues. These observations could potentially be leveraged to reduce out of stocks earlier, increase on-shelf availability and improve performance in retail. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117953 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117631 Beyond the Seaport: Assessing the Inland Container Transport Chain Using System Dynamics Toukan, Mamoun; Chan, Hoi Ling The significance of seaports in enabling global trade and impacting global supply chains require them to operate efficiently. Due to the required interactions, seaports are not only affected by their operations, but by other parties in the transport chain. The existing interactions between multiple parties in the transport chain add complexity to the system. Using Jordan as a case study, and focusing on containerized transport, this paper develops a System Dynamics framework that assesses the impact of alternative strategies on the container transport chain. The paper first identifies the sub-systems in the Jordanian container transport chain, to introduce a conceptual model that illustrates the causal relations in the system. A System Dynamics framework is then presented to assess the impact of different strategies on the container transport chain. Based on industry trends in reducing container delivery time and adding capacity at the terminal, three different alternatives are introduced and simulated under multiple scenarios. The list of alternative strategies includes: investing in the hinterlands, implementing technology to reduce documentation processing time and a combination of both. The simulation output shows while the first alternative reduces the dwell time by over six days, it does not reduce delivery time. The second alternative is extremely effective in reducing delivery time but does not add resilience to the terminal’s capacity. The third alternative is the highest ranking in terms of delivery time and container turnaround. Simulating the third alternative again over a longer period shows that it outperforms the current alternative for up to 275 days only. The third alternative resulted in an increase in fleet utilization, which ultimately leads to a congestion in the terminal. Thus, a holistic view needs to be taken when assessing the impact of different strategies and ensuring the right KPIs are chosen. The presented framework is highly relevant to decision makers including policy makers and investors in Jordan and elsewhere. For Jordan, with government plans to reduce container dwell time, the framework provides added insights for decision makers, beyond the seaport. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117631 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117630 The $100 question: Supply chain priorities for small firms Pereira Nunes, Rafaela; Paulino, Ramón A large majority of companies in Latin America are micro or small firms, making them an important part of the region’s economy. These firms are a relevant source of jobs, but a lack of managerial skills and resources threatens their survival. In this study, we analyze a set of supply chain best practices and define the more relevant practices for micro and small enterprises in a growth context. Based on a set of interviews and immersion, by way of site visits, with 15 small and micro firms located in Mexico City, we develop a system dynamics model that illustrates the influence of these supply practices on company growth. Our results show that, after quality and service baselines are met, practices related to capacity building, collaboration, and market expansion drive faster growth and should be prioritized by companies that aim for expansion. These insights are a major step in developing more effective assessments and training for micro and small firms in Latin America and improving the overall economic performance of the region. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117630 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117629 Network Design for Mid-day Meal Program Singh, Priyanka; Noor, Afsaruzzaman The Mid-day Meal program is an initiative taken by the Government of India to improve the nutritional status of school-age children nationwide. It involves many agencies and NGOs. Akshaya Patra is the most popular and largest NGO among them which in India is currently serving meals to 1.6 million children every day. It uses centralized kitchens to prepare meals and then deliver them to schools within a four hours of delivery window. Currently, it is facing the challenge of fulfilling the growing demand of meals within short delivery windows while keeping the transportation costs and the fixed costs of the kitchens low. Keeping in mind that food is a highly perishable item and has a very short delivery window (four hours), we designed a kitchen network to solve Akshaya Patra’s problem by using an optimization method called Mixed Integer Linear Programming (MILP). We also tested various scenarios such as network design considering kitchens’ capacity constraint, design with cross docking, and the use of insulated containers. Our model suggests a network design approach using cross docking and insulated containers. The proposed model has lowest cost and can be replicated in other states also. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117629 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117628 Logistics Cost Minimization and Inventory Management Decision for Yarn Manufacturers in China Mak, Ka Hing Logistics efficiency impacts the profitability of manufacturing companies to a large extent. According to IDC Manufacturing Insights Report of 2014, logistics cost makes up 7.87% of sales of the business. Statistics reveal that the carrying cost in China has been increased to over 3.5 trillion RMB in 2016 accounting for 5.8% of the GDP, i.e. 3% higher than the US. In our case study, the spinning mill business in China has experienced over 10% increase in warehouse and transportation costs in 2017. This study shows that by adjusting the replenishment policy and the transportation planning of cottons and yarns according to their seasonality characteristics of supply and demand, 23% of total logistics cost can be saved for the sponsor company. A multi- period Mixed Integer Linear Programming (MILP) model integrating both inventory management and transportation network of a cross region spinning mill infrastructure is developed for logistics cost minimization. Industrial-wise constraints including the cotton import quota, cotton mixing strategies and seasonality of cotton supplies can be adapted to other spinning mill companies for optimization purpose. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117628 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117627 Risk Mitigation at Call Centers Li, Jin; Nieto Valencia, Viviana Climate catastrophes (i.e. tornadoes, hail, hurricanes, etc.) have a significant economic and operational impact on the operation of call centers. It was found that catastrophe events such as hurricanes critically impact the operation of the affected location for a period of two months after the hurricane has occurred. A sudden increase of demand affects the service level agreement Company X has with its customers due to a shortage of labor resources to attend the inbound calls until the process stabilizes and the location can achieve an adequate service level. Can a company leverage resources from a network of call centers to support impacted locations during a disruptive climate catastrophe event? This study focuses on the development of a call rerouting model. The problem was divided into four main parts: (i) Data preprocessing, (ii) Demand analysis with the use of exponential smoothing, (iii) Capacity analysis using queueing theory and, (iv) Determination of locations to deviate the inbound calls to with the use of a Mixed Integer Linear Programming Model (MILP). In conclusion, the project defines a framework for the company to balance resources during high pressure situations, which can be applied to different types of disruptions in the inbound calls process. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117627 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117626 Internal Inventory Management, Analysis and Improvement for a CPG Company Moreno Sanchez Briseno, Jorge; Lalos, Konstantinos D. The sponsor company, a multinational CPG, is under pressure to reduce inventory levels of finished goods and raw and pack materials, while achieving its target sales service level. Currently, it uses a single-echelon inventory management approach. This prevents the company from achieving cross stage inventory improvements. For that reason, in our research, we analyze the benefits of pooling the variance from the customer facing demand while setting the inventory levels at the raw materials stage. We developed a model built on the sponsor company’s model, adding the link between the final demand variability with the stock policies for raw and pack materials. Through a simulation on a sample of finished goods and their raw materials, we found that that accounting for the final demand variability in both stages leads to a significant reduction of inventory levels. Our proposed inventory management procedures and guidelines help the sponsor company to reduce the inventory capital investment, without affecting its service level. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117626 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117625 Enhancing Sales and Operations Planning Performance with Analytics Khan, Minhaaj; Kidambi, Srideepti Effective businesses implement a monthly Sales and Operations Planning (S&OP) process within their organizations to align and coordinate different functions of the business under a single plan. Despite this significant undertaking, once businesses align on a S&OP plan, little effort is made on methodically assessing risks in the plan to determine opportunities for improvement. The objective of this project was to help a sponsoring company in the Food & Beverage industry proactively predict high probability risks in their S&OP plan (i.e., imbalance of supply and demand), allowing them to mitigate the negative consequences that result from reactively managing these risks. Data mining methodologies were applied to S&OP data for a selected company brand with the goal of determining model(s) that could best predict high probability risks for intervention and risk mitigation. The predictor (independent variables) and response (dependent) variables were derived from the S&OP data and the models were trained, validated and tested in R. Supervised classification algorithms were used to build classification models for each of the four risk outcomes (binary); 50% over forecast, 50% under forecast, weeks of supply below four weeks, and stockouts. Of the four risk outcomes studied, only 50% over forecast provided a viable model for business application. Compared to business as usual, this classification model (applied to a 12-week period) improved 3-month lag accuracy by 5.7%, reduced bias to near zero and added a conservative $415,000 in operating profit. Applying methodologies identified in this study across all brands and extrapolating operating profit improvement across a full year, the sponsoring company can capture $15MM increase in profits annually. This study concludes by recognizing that even without large data sets (i.e., big data), there are a multitude of benefits companies can gain through the application of predictive analytics to capture business risks in their S&OP plans. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117625 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117624 "Would You Be Willing to Wait?": Consumer Preference for Green Last Mile Home Deliver Fu, Andrew Jessie; Saito, Mina The growing trend of e-commerce has led to new ways of selling and delivering products, resulting in increasing scale and complexity of last mile home delivery. The drive to provide convenience to consumers has led companies to offer faster delivery times. As a result, companies have focused on facility location, network design, and asset utilization (trucks, drivers), in order to improve service and speed. Few, however, have questioned whether consumers truly want convenient and fast delivery. Rather than focusing on a company’s operations, we approach the last mile home delivery from the perspective of the consumer. Our research considers whether consumer preferences for home delivery options can be influenced by environmental incentives, which include CO2 equivalent, electricity, trash, and trees. A case study with a corporate partner, Coppel S.A. de C.V. (“Company”), one of Mexico’s largest retail companies, reveals ways to incentivize consumers to wait longer. The case study involves a field study of approximately 1,000 home deliveries to predominantly low socioeconomic households across ten regions of Mexico. The results suggest that consumers are willing to wait longer for their home deliveries when given the environmental impact reduction. Moreover, information on trees saved is the most effective at incentivizing consumers to wait longer, regardless of education, occupation or socioeconomic status. Finally, using this extended delivery lead time, we provide an alternative methodology for improving vehicle utilization in last mile deliveries of a one-warehouse-N-customer system. The improved utilization results in lower fuel consumption and reduced carbon emissions. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117624 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117623 Demand Forecasting of the Bike-sharing Service in Beijing Liu, Terry; Fricke, Leonhard Maximilian In response to increasing urbanization, China seeks alternative public transportation methods, such as bike- sharing, which has demonstrated social and environmental benefits. As a result, the number of bike-sharing programs has grown rapidly over the last five years in China. Our sponsoring company TalkingData collects bike-sharing usage data via smartphones and aims to provide insights to bike-sharing operators. Therefore, our main objective in this project is to analyze smartphone data to understand the bike-sharing demand in Beijing. We conducted interviews with bike-sharing stakeholders and investigated a one-month sample of data that TalkingData collected from bike-sharing operators in Beijing merging it with secondary data from online resources. We found that the level of bike-sharing activity varies across the city Beijing both in terms of location and time throughout the day. We discovered that both, time and environmental related factors significantly affect the bike-sharing demand. In contrast, our study revealed that some factors stated in literature such as pollution level do not affect bike-sharing demand in Beijing significantly. Hence, we suggest that drivers of bike-sharing demand differ across cities or countries making it worthwhile to perform location specific analysis. We fitted linear regressions, neural networks and random forests on the compiled dataset and compared their respective performance. We found that, based on the one-month sample, linear regression performs best amongst the three models in predicting hourly bike-sharing demand in Beijing. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117623 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117620 Dynamic Customer Service Levels: Evolving Safety Stock Requirements for Changing Business Needs Covert, Daniel Patrick; Ortiz Millán, Joaquín Alberto Retail companies struggle to maintain the appropriate levels of inventory on promotional and seasonal items due to management pressure to never be out of stock. Dynamically changing desired service levels during promotions or seasonal periods will ensure accurate safety stock levels and reduce manual interventions. We simulate inventory policies introducing dynamic changes in desired service levels to determine the impact on inventory, stock outs, and fill rate. We show that dynamic service levels can reduce inventory and stock outs while maintaining the same fill rate as fixed policies. Having different service levels throughout the year contingent on business needs ensures that companies will have the right inventory at the right time. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117620 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117619 Evaluation of Different Delivery Policies in the Cement Industry Coloma Lopez, Juan Carlos; Vasconcelos Groenner, Michel Patrick Many supply chains must change in order to capture new opportunities and adapt to new demands and new markets. This is study explains how we can improve the delivery policy in the cement industry to lead the change needed. We have studied the case where a cement company, Votorantim Cimentos, wants to sell smaller orders and seeks a delivery policy to address this challenge. To tackle this problem, we developed a methodology to analyze different delivery policies. Our methodology includes a simple heuristic to determine when each order is going to be shipped. After assigning each load to a truck we can calculate the transportation cost -- a function of the number of trucks -- and the penalty costs -- a function of the difference between the date the customer required the product and the actual delivery date. Using this methodology we run different possible delivery policies and analyze the results. Compared to the company's everyday delivery policy we achieved a solution with 77% lower extra transportation costs and 31% lower total relevant costs. We also propose a methodology to evaluate when to ship or hold an order given a set of orders. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117619 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117616 LNG Supply Chain Resilience Falaiye, Adegoke Oluwaseun; Chiam, Fu Song The demand for Liquefied Natural Gas (LNG) has continued to grow globally over the past decade with increasing numbers of disruptions and plant outages. Considering these dynamics, oil and gas companies will have to prepare their operations to meet increasing demand with agile response to disruptions. This is particularly true with the case study company for this capstone, a company that manages the upstream oil and gas pipeline infrastructure in Nigeria. This capstone project used historical supply chain disruption data of the case study company to develop a model for prioritizing response to multiple pipeline disruptions. Our case study company has operations in two hubs where response to pipeline disruptions are traditionally treated at individual hub level. Our study shows that a holistic treatment of the entire network rather than in hubs could potentially improve supply chain resilience. Firstly, an existing resilience framework, the Balanced Scorecard of Resilience (BSR) model, was adapted to the unique characteristics of the gas pipeline network to determine the expected business impacts of each node. Secondly, the expected business impacts and cost of restoring disruption was used in ranking response and repair. It was shown that the BSR framework could be adopted to gas pipeline network, this adoption could enable practitioners and managers to make informed risk-based business decisions in enhancing overall LNG supply chain resilience. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117616 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117614 Lead Time Reduction of Laboratory Testing Services Chee-Awai, Michael; Semel, Joel Services, more specifically laboratory testing services, that await arrival of customer inputs prior to commencing suffer from long lead times. Can performing the steps in a group of services concurrently with the transportation lead time of customer input reduce service lead times? This research clustered services according to their co-occurrence frequency. Then it developed a model to perform as many steps in those services as possible prior to customer input arriving. The model triggered performance of those actions by a customer message at the time of inputting the shipment. The results showed the model is effective only when groups of services ordered on single inputs can be performed concurrently with the transportation lead time. Lead time reduction is not effective when multiple services are ordered but only some are performed in advance. In conclusion, a one-to-many relationship between customer input and ordered services requires process step concurrency between all services and input transit time to effectively reduce lead times. Additionally, customer communication can give service providers a window of time to perform services just-in-time. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117614 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117612 Forecasting Seasonal Footwear Demand Using Machine Learning Kharfan, Majd; Chan, Vicky Wing Kei The fashion industry has been facing many challenges when it comes to forecasting demand for new products. The macroeconomic shifts in the industry have contributed to short product lifecycles and the obsolescence of the retail calendar, and consequently an increase in demand variability. This project tackles this problem from a demand forecasting perspective by recommending two frameworks leveraging machine learning techniques that help fashion retailers in forecasting demand for new products. The point-of-sale (POS) data of a leading U.S.-based footwear retailer was analyzed to identify significant predictor variables influencing demand for footwear products. These variables were then used to build two models, a general model and a three-step model, utilizing product, calendar and price attributes for predicting demand. Clustering and classification were used under the three-step model to identify look-alike products. Regression trees, random forests, k-nearest neighbors, linear regression and neural networks were used in building the prediction models. The results show that the two forecasting models based on machine learning techniques achieve better forecast accuracy compared to the company’s current performance. In addition, the proposed methodology offers visibility into the underlying factors that impact demand, with insights into the importance of the different predictor variables and their influence on forecast accuracy. Finally, the project results demonstrate the value of forecast customization based on product characteristics. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117612 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117611 Network Design Model for Fuel Retail Castro, Fabio Amaral de; Mulama, Bonaventure A small chain of gas stations in Brazil intends to expand to new retail locations. Which factors should it consider when locating new stations, in order to better leverage a combination of product prices and transportation costs? This work develops a Linear Optimization network design model in order to identify the best locations, supply origins and distribution routes that yield optimized profits. Through the Linear Optimization model and Monte-Carlo simulation, we identify that, by modeling the entire network and the route topographies, the model is expected to deliver profits 7% higher than is the case currently. These factors, however, do not seem to be as significant as station-specific factors such as operating costs, sales volume and price policy – factors beyond the scope of this work. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117611 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117610 Route Clustering in Transportation with Geospatial Analysis and Machine Learning to Reduce CO2 Emissions Barkah, Ade; Robert, Patrick The transportation sector is a significant contributor to greenhouse gas emissions; hence, it is desirable to lower carbon emissions from this sector, especially with the expected growth in transportation needs from e-commerce. Since a particular vehicle type may exhibit different emissions characteristics when operating in different regions, determining which vehicle types should serve each region is an important consideration. This project assesses carbon emissions and fuel efficiency characteristics of delivery trucks in the fleet for one of the largest retail companies in Mexico: Coppel1. Coppel’s fleet consists of a number of truck types operating in diverse geographies throughout Mexico, making it difficult to make “apples-to-apples” comparisons of their performance. We apply machine learning to cluster routes using GPS traces from Coppel’s trucks and examine their performance in varying road and traffic conditions. We group the company’s routes into four different clusters based on factors such as road elevation, road gradients, average vehicle speed and the length between delivery stops. Furthermore, by considering vehicle weight utilization (i.e. load), we find a cluster of routes associated with increased fuel consumption and carbon emissions. We also rank the most efficient vehicles for each cluster. These results can inform route assignments to minimize fuel consumption and in planning of Coppel’s future fleet composition. We estimate that by operating the most efficient vehicle type at each cluster we could reduce vehicle fuel consumption by up to 7.2%. Finally, by reducing fuel consumption, we also limit the release of CO2 emissions to the environment. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117610 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117609 Combinatorial Reverse Auctions in Construction Procurement Al Shaqsi, Salim Construction procurement often involves negotiations between many parties over multitudes of components. The process of allocating contracts to suppliers is generally a complex process involving multiple decision makers processing large amounts of information. Minimizing project costs while meeting stringent specification and schedule requirements is especially difficult. In most cases, procurement processes do not consider combinatorial (or package) bids from suppliers. Allowing combinatorial bidding in the procurement process has been shown to reduce costs in other industries. It can, therefore, be expected to reduce costs in construction. This research proposes the use of combinatorial reverse auctions to minimize construction costs. Various models were applied to real data to determine feasibility compared to the baseline of allocating all items to the lowest bidder. Data included seven scenarios selected based on the number of suppliers and number of items. Each scenario included a subset of bids submitted by approved suppliers. A sensitivity analysis was performed on each model-scenario pair to consider uncertainty in supplier pricing structures. Results of the analysis provided justification for the use of combinatorial reverse auctions in construction procurements. Cumulative cost savings across all seven scenarios were 6.4% for unconstrained models and 2.7% for constrained models with limits on the number of awarded suppliers. Further analysis on the computation time and distribution of solutions across various methods indicated the superiority of the iterative approach to solving the winner determination problem in terms speed and optimality. However, stochastic and meta-heuristic approaches using a genetic algorithm led to higher variations in allocations while maintaining low variations in total cost. This suggests that they can be used to provide families of near-optimal solutions. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117609 2018-01-01T00:00:00Z https://hdl.handle.net/1721.1/117607 Supply Chain Network Optimization for Global Distribution of Cementitious Materials Abt, Hans Josef Sebastian; Tisera, Germán Daniel Companies trading cementitious materials face an increasingly volatile economic environment and continuous challenges to ensure the availability of strategic raw materials. In this context, the ability to globally source slag represents a competitive advantage for cement companies because of the limited availability of the material, its importance in the cement production process, and the complex network of supply and demand nodes. In this project, we introduce an optimization model to support managerial decision-making in the global distribution of cementitious materials for a multinational company. We develop a Mixed Integer Linear Program (MILP) to find quantitative solutions that maximize total contribution margin. In addition, we use scenario-planning techniques to assess the sensitivity of our results with regard to multiple potential futures, to account for changes in relevant demand, supply, and costs in a dynamic economic environment. The results show opportunities to increase contribution margin by 11% through an optimized allocation of existing volumes in the current network. We suggest further improvements to the contribution margin by introducing new trading routes as well as different pricing strategies for customers. Additionally, the model shows how prices and transport costs are the main determining factors for the company’s profitability; increasing transportation costs by 20% results in a 51% reduction in contribution margin. Ultimately, we develop a model that is relevant to a number of different network optimization problems, and adaptable to different economic conditions. Mon, 01 Jan 2018 00:00:00 GMT https://hdl.handle.net/1721.1/117607 2018-01-01T00:00:00Z