Logistic regression for a better matching of buyers and suppliers in e-procurement
Author(s)Tian, Shuo, S.M. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Computation for Design and Optimization Program.
Stephen C. Graves.
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The 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.
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 57-58).
DepartmentMassachusetts Institute of Technology. Computation for Design and Optimization Program.
Massachusetts Institute of Technology
Computation for Design and Optimization Program.