{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/77476"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/77476","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"From plant to dealer : improving route optimization for outbound vehicle distribution at an automobile manufacturer","abstract":"With rising fuel costs and increasing rates among specialized shipping carriers, cost mitigation in outbound distribution is increasingly important for automobile manufacturers. Many manufacturers have turned to specialized, licensed supply chain software to optimize their distribution network to determine the appropriate path for each product from factory to dealer. While these software programs include robust algorithms for optimizing the network, they are only as strong as the user inputs. To gain maximum value from supply chain software, automotive companies must fully understand the structure of their networks, their costs, and their constraints to ensure that the model is all-inclusive. This paper attempts to understand the distribution model used at Nissan North America by formulating the model algebraically with a linear program. With insights to the model design, we uncover several opportunities for improvement. Specifically, we create a more inclusive objective function by ensuring that all relevant costs are captured so that the model optimizes the \"total landed cost.\" We also highlight several opportunities for increased model flexibility in areas where the model is over constrained -- both in its mathematical constraints and in its structural design. With increased flexibility, supply chain software has more alternative paths in the network to choose from, increasing the opportunity for the program to find a lower cost solution. Lastly, we stress the importance of using the software for scenario analysis to create a more responsive supply chain. When implemented, the improvements presented in this paper yield a cost savings of over $10 million. The principles of the model improvements in this thesis can be applied to distribution optimization in any industry.","abstract_html":"With rising fuel costs and increasing rates among specialized shipping carriers, cost mitigation in outbound distribution is increasingly important for automobile manufacturers. Many manufacturers have turned to specialized, licensed supply chain software to optimize their distribution network to determine the appropriate path for each product from factory to dealer. While these software programs include robust algorithms for optimizing the network, they are only as strong as the user inputs. To gain maximum value from supply chain software, automotive companies must fully understand the structure of their networks, their costs, and their constraints to ensure that the model is all-inclusive. This paper attempts to understand the distribution model used at Nissan North America by formulating the model algebraically with a linear program. With insights to the model design, we uncover several opportunities for improvement. Specifically, we create a more inclusive objective function by ensuring that all relevant costs are captured so that the model optimizes the "total landed cost." We also highlight several opportunities for increased model flexibility in areas where the model is over constrained -- both in its mathematical constraints and in its structural design. With increased flexibility, supply chain software has more alternative paths in the network to choose from, increasing the opportunity for the program to find a lower cost solution. Lastly, we stress the importance of using the software for scenario analysis to create a more responsive supply chain. When implemented, the improvements presented in this paper yield a cost savings of over $10 million. The principles of the model improvements in this thesis can be applied to distribution optimization in any industry.","abstract_has_math":false,"creators":["Katcoff, Elizabeth"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Leaders for Global Operations Program at MIT","school":null,"contributors":[],"advisors":["David Simchi-Levi, and Don Rosenfield."],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-22T22:21:27Z","subjects":["Sloan School of Management.","Engineering Systems Division.","Leaders for Global Operations Program."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. 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Many manufacturers have turned to specialized, licensed supply chain software to optimize their distribution network to determine the appropriate path for each product from factory to dealer. While these software programs include robust algorithms for optimizing the network, they are only as strong as the user inputs. To gain maximum value from supply chain software, automotive companies must fully understand the structure of their networks, their costs, and their constraints to ensure that the model is all-inclusive. This paper attempts to understand the distribution model used at Nissan North America by formulating the model algebraically with a linear program. With insights to the model design, we uncover several opportunities for improvement. Specifically, we create a more inclusive objective function by ensuring that all relevant costs are captured so that the model optimizes the \"total landed cost.\" We also highlight several opportunities for increased model flexibility in areas where the model is over constrained -- both in its mathematical constraints and in its structural design. With increased flexibility, supply chain software has more alternative paths in the network to choose from, increasing the opportunity for the program to find a lower cost solution. Lastly, we stress the importance of using the software for scenario analysis to create a more responsive supply chain. When implemented, the improvements presented in this paper yield a cost savings of over $10 million. 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Many manufacturers have turned to specialized, licensed supply chain software to optimize their distribution network to determine the appropriate path for each product from factory to dealer. While these software programs include robust algorithms for optimizing the network, they are only as strong as the user inputs. To gain maximum value from supply chain software, automotive companies must fully understand the structure of their networks, their costs, and their constraints to ensure that the model is all-inclusive. This paper attempts to understand the distribution model used at Nissan North America by formulating the model algebraically with a linear program. With insights to the model design, we uncover several opportunities for improvement. Specifically, we create a more inclusive objective function by ensuring that all relevant costs are captured so that the model optimizes the \"total landed cost.\" We also highlight several opportunities for increased model flexibility in areas where the model is over constrained -- both in its mathematical constraints and in its structural design. With increased flexibility, supply chain software has more alternative paths in the network to choose from, increasing the opportunity for the program to find a lower cost solution. Lastly, we stress the importance of using the software for scenario analysis to create a more responsive supply chain. When implemented, the improvements presented in this paper yield a cost savings of over $10 million. 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