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dc.contributor.advisorKaren Zheng and Chris Caplice.en_US
dc.contributor.authorOti, Olufemi (Olufemi Oluwole)en_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2013-09-24T19:36:35Z
dc.date.available2013-09-24T19:36:35Z
dc.date.copyright2013en_US
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/81007
dc.descriptionThesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2013.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 68-69).en_US
dc.description.abstractAmazon is one of the world's leading retailers. At the core of Amazon's business model is providing consumers with endless selection, and as a result, the large number of vendors used to provide that selection greatly increases the complexity and cost of operating the inbound supply chain. This growth has also created many opportunities for the company to leverage its size and scale to lower transportation costs and improve supply chain flexibility. This project explores implementing load consolidation strategies within the "Hub and Spoke" distribution framework to provide these benefits. As -65% of total unit volume from the inbound transportation program managed by Amazon is shipped as costly less-than-truckload (LTL) or small-parcel (SP) freight, there are significant opportunities to use consolidation hubs throughout the inbound network to reduce spend on LTL and SP in favor of more cost effective full truckload (TL) shipments. To evaluate the opportunity and provide the inbound team with a useful strategic planning tool, a comprehensive network optimization model was targeted as a project deliverable. After researching the current state of the inbound transportation network through departmental interviews and visits to carrier hubs and fulfillment centers, key inputs were identified to feed the model. The mixed integer program solution uses these inputs to minimize total inbound transportation cost for the network subject to expected transit time performance targets by choosing what consolidation hubs and destination lanes freight should be routed to. Using a data-set of shipments originating in the Southwestern geography, an average saving of 13.7% on annual LTL and SP spend was projected by routing 37% of freight volume through consolidation hubs. Results showed freight density as an important driver in savings. In areas with more originating freight, outbound full truckloads can be filled more readily and hence consolidation opportunities can be taken advantage of more often. This tool and the supporting analyses will help the inbound transportation organization uncover more cost saving opportunities in routing freight through its growing network. In addition to financial cost savings, the strategy will increase supply chain flexibility, reduce environmental impact, and can help increase Amazon's control over the end-to-end inbound transportation network.en_US
dc.description.statementofresponsibilityby Olufemi Oti.en_US
dc.format.extent74 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSloan School of Management.en_US
dc.subjectEngineering Systems Division.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleHub and spoke network design for the inbound supply chainen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.description.degreeM.B.A.en_US
dc.contributor.departmentLeaders for Global Operations Program at MITen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.contributor.departmentSloan School of Management
dc.identifier.oclc857789936en_US


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