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Reducing inventory by simplifying forecasting and using point of sale data

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dc.contributor.advisor Lawrence Lapide. en_US
dc.contributor.author Agarwal, Atul en_US
dc.contributor.author Holt, Gregory Douglas Barton en_US
dc.contributor.other Massachusetts Institute of Technology. Engineering Systems Division. en_US
dc.date.accessioned 2006-07-13T15:14:20Z
dc.date.available 2006-07-13T15:14:20Z
dc.date.copyright 2005 en_US
dc.date.issued 2005 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/33310
dc.description Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2005. en_US
dc.description Includes bibliographical references (leaf 69). en_US
dc.description.abstract This thesis assesses the value to vendors of using point of sale data to predict what retailers will order from them. In particular, we look at how The Gillette Company can use point of sale data generated by two of their customers, (Wal-Mart and Target), to predict the orders of all of Gillette's customers combined. The thesis also examines the impact on forecasts of shortening and simplifying the demand planning process. By improving the forecast of orders from their customers, vendors like Gillette can reduce safety stock inventory which is held as protection against unpredictable demand. en_US
dc.description.statementofresponsibility by Atul Agarwal [and] Gregory Douglas Barton Holt. en_US
dc.format.extent 78 leaves en_US
dc.format.extent 3878982 bytes
dc.format.extent 3882183 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
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
dc.subject Engineering Systems Division. en_US
dc.title Reducing inventory by simplifying forecasting and using point of sale data 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 62311669 en_US


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