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dc.contributor.advisorSergio Caballero.en_US
dc.contributor.authorPorter, Danaka M. (Danaka Michele)en_US
dc.contributor.otherMassachusetts Institute of Technology. Supply Chain Management Program.en_US
dc.date.accessioned2018-09-17T14:50:05Z
dc.date.available2018-09-17T14:50:05Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/117798
dc.descriptionThesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged student-submitted from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 59-63).en_US
dc.description.abstractExcess inventory is prevalent in both the armed forces and defense companies; it takes up space and resources that could be used elsewhere. This thesis proposes a method to reduce the excess inventory and associated costs, while maintaining instant part availability, despite design changes which alter the number of parts required. A single period model extension was created based on K-means clustering of the parts according to lead-time and cost. These groupings provided the backbone of the cost functions created in the thesis. A predictive demand function was also created so that the design change's alterations to demand would be captured. The cost function was optimized using the predicted demand, to find an optimal order quantity that met the demand requirements and was the lowest cost option. Together these single period model function extensions allowed for a 31 percent decrease in excess inventory and 34 percent decrease in total cost. Due to the nature of this report the companies' names have been removed, and the data naming conventions were altered so as to protect the nature of the parts.en_US
dc.description.statementofresponsibilityby Danaka M. Porter.en_US
dc.format.extent63 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSupply Chain Management Program.en_US
dc.titleUsing K-means clustering to create cost and demand functions that decrease excess inventory and better manage inventory in defenseen_US
dc.typeThesisen_US
dc.description.degreeM. Eng. in Supply Chain Managementen_US
dc.contributor.departmentMassachusetts Institute of Technology. Supply Chain Management Program.en_US
dc.identifier.oclc1051223334en_US


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