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dc.contributor.advisorStephen C. Graves.en_US
dc.contributor.authorTian, Shuo, S.M. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Computation for Design and Optimization Program.en_US
dc.date.accessioned2011-03-24T20:23:53Z
dc.date.available2011-03-24T20:23:53Z
dc.date.copyright2010en_US
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/61901
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 57-58).en_US
dc.description.abstractThe thesis aims to provide a way to identify better matches between buyers and suppliers who are using an e-procurement platform provided by a US based worldwide online market company. The goal is to enhance the shopping experience of the clients, increase the retention rate and grow the customer base of the company. We establish two logistic regression models. The first model is to predict the probability of suppliers winning an RFQ (request for quote). From the calculated probabilities, we are able to rank all the suppliers and tell the buyers who may be the most qualified providers for them. Also, the suppliers will be aware of their odds of winning among all the competitors. Our model shows that price is the most decisive factor for winning, and geography and prior business relationships with the buyer are also important. The second model is used to estimate the probability of successfully awarding an RFQ. We model how likely the RFQ is to be awarded by the buyer. Such information will be especially helpful to suppliers. The process of the RFQ and the relation and intention of the buyer seem to be the most influential factors.en_US
dc.description.statementofresponsibilityby Shuo Tian.en_US
dc.format.extent64 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.subjectComputation for Design and Optimization Program.en_US
dc.titleLogistic regression for a better matching of buyers and suppliers in e-procurementen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computation for Design and Optimization Program.en_US
dc.identifier.oclc706825540en_US


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