{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/139357"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/139357","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Building Resilient Supply Chain using Interactive Visualization","abstract":"As supply chains expand globally and companies pursue speed, efficiency, and reduction in cost, the probability of disruptions propagating through the network grows. There are many documented threats to global supply chains: political instability, natural disasters, dock strikes, poor product quality, communications failures, currency risks, cyber-attacks, and recently a pandemic. These disruptions often incur additional costs and require time to respond and recover from these disruptions. Companies realize the importance of resilience in the supply chain network. However, due to the complex nature of the network, traditional processes, and outdated technology, the leadership team cannot make proper decisions against such disruptions. On the other hand, we found evidence of the importance of interactive visualization in decision-making. This research project introduces the application of interactive visualization in supply chain resilience decision-making. The application can be broken down into three parts. Firstly, a backend mixed-integer linear programming model that solves for a minimum total cost based on the inputs. Secondly, a front-end UI allows users to create any disruption scenarios using parameters - geography, time period, product, to visualize disruption such as demand variation, a shutdown of transshipment location, or a change in transportation mode. Lastly, a JSON file that connects the front and back end seamlessly. We use the application to create scenarios that are relevant for a multinational company. For the first use case, we explore the consequences of a shutdown of airports near distribution centers. For the second use case, we explore the availability of more than one transportation mode per lane. We analyze the results from the use cases to plan mitigation strategies for any such disruptions in the future. In conclusion, by creating scenarios and visualizing the network in a single and easy-to-understand application, we facilitate decision-making to test the network's resilience.","abstract_html":"As supply chains expand globally and companies pursue speed, efficiency, and reduction in cost, the probability of disruptions propagating through the network grows. There are many documented threats to global supply chains: political instability, natural disasters, dock strikes, poor product quality, communications failures, currency risks, cyber-attacks, and recently a pandemic. These disruptions often incur additional costs and require time to respond and recover from these disruptions. Companies realize the importance of resilience in the supply chain network. However, due to the complex nature of the network, traditional processes, and outdated technology, the leadership team cannot make proper decisions against such disruptions. On the other hand, we found evidence of the importance of interactive visualization in decision-making. This research project introduces the application of interactive visualization in supply chain resilience decision-making. The application can be broken down into three parts. Firstly, a backend mixed-integer linear programming model that solves for a minimum total cost based on the inputs. Secondly, a front-end UI allows users to create any disruption scenarios using parameters - geography, time period, product, to visualize disruption such as demand variation, a shutdown of transshipment location, or a change in transportation mode. Lastly, a JSON file that connects the front and back end seamlessly. We use the application to create scenarios that are relevant for a multinational company. For the first use case, we explore the consequences of a shutdown of airports near distribution centers. For the second use case, we explore the availability of more than one transportation mode per lane. We analyze the results from the use cases to plan mitigation strategies for any such disruptions in the future. In conclusion, by creating scenarios and visualizing the network in a single and easy-to-understand application, we facilitate decision-making to test the network's resilience.","abstract_has_math":false,"creators":["Tripathi, Prabhakar"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"System Design and Management Program.","school":null,"contributors":[],"advisors":["Winkenbach, Matthias"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-06","date_published":"2021-06","updated_at":"2026-07-22T22:20:44Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/139357","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Winkenbach, Matthias"]},{"key":"dc:contributor.department","label":"Department","values":["System Design and Management Program."]},{"key":"dc:creator","label":"Author","values":["Tripathi, Prabhakar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-01-14T15:06:14Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-01-14T15:06:14Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-06"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Science in Engineering and Management"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/139357"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["As supply chains expand globally and companies pursue speed, efficiency, and reduction in cost, the probability of disruptions propagating through the network grows. 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Secondly, a front-end UI allows users to create any disruption scenarios using parameters - geography, time period, product, to visualize disruption such as demand variation, a shutdown of transshipment location, or a change in transportation mode. Lastly, a JSON file that connects the front and back end seamlessly. We use the application to create scenarios that are relevant for a multinational company. For the first use case, we explore the consequences of a shutdown of airports near distribution centers. For the second use case, we explore the availability of more than one transportation mode per lane. We analyze the results from the use cases to plan mitigation strategies for any such disruptions in the future. 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Secondly, a front-end UI allows users to create any disruption scenarios using parameters - geography, time period, product, to visualize disruption such as demand variation, a shutdown of transshipment location, or a change in transportation mode. Lastly, a JSON file that connects the front and back end seamlessly. We use the application to create scenarios that are relevant for a multinational company. For the first use case, we explore the consequences of a shutdown of airports near distribution centers. For the second use case, we explore the availability of more than one transportation mode per lane. We analyze the results from the use cases to plan mitigation strategies for any such disruptions in the future. 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