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Determination of drivers of stock-out performance of retail stores using data mining techniques

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dc.contributor.advisor Chris Caplice. en_US
dc.contributor.author Usman, Khalid, M. Eng. Massachusetts Institute of Technology. en_US
dc.contributor.other Massachusetts Institute of Technology. Engineering Systems Division. en_US
dc.date.accessioned 2009-04-29T17:14:26Z
dc.date.available 2009-04-29T17:14:26Z
dc.date.copyright 2008 en_US
dc.date.issued 2008 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/45246
dc.description Includes bibliographical references (leaves 66-67). en_US
dc.description Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008. en_US
dc.description.abstract This research applies data mining techniques to give a picture of the interaction of performance variables such as between stock-outs and store attributes, and stock-outs and other variables including store sales, income and demographic data, as well as various aspects of inventory management data. This research uses three data mining techniques: multiple ordinary-least-squares (OLS) regression, logistic regression and data clustering. The first part of the research evaluates how the effect of stock-outs at the distribution center (DC) level impacts the downstream sales at the store-level. Using multiple regression techniques, it was observed that stock-outs at the distribution center level have a detrimental impact on the sales at the retail store level. The second part of the project focuses on understanding the relationships of store stock-out performance to various drivers. The variables that were determined to be drivers of store performance include income level of the area, demographic profile, years the store has been in operation, day of the week delivery from distribution center, distance of store from the distribution center and average inventory-on-hand. Using data clustering techniques, worse performing and good performing clusters of stores were identified. The two worse performing clusters were 'Low-Income, Newer' stores and 'Newer, Further from DC' stores. The three good performing clusters were 'High-Income, High-Inventory' stores, 'Closer to DC, Older' Stores and 'High-Income, Smaller' stores. en_US
dc.description.statementofresponsibility by Khalid Usman. en_US
dc.format.extent 84 leaves en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.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.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Engineering Systems Division. en_US
dc.title Determination of drivers of stock-out performance of retail stores using data mining techniques en_US
dc.type Thesis en_US
dc.description.degree M.Eng.in Logistics en_US
dc.contributor.department Massachusetts Institute of Technology. Engineering Systems Division. en_US
dc.identifier.oclc 310335536 en_US


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