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dc.contributor.advisorMichael Whinston.en_US
dc.contributor.authorCarey, Matthew, M.B.A. Sloan School of Managementen_US
dc.contributor.otherSloan School of Management.en_US
dc.date.accessioned2017-10-18T15:08:23Z
dc.date.available2017-10-18T15:08:23Z
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/111881
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2017.en_US
dc.description"June 2017." Cataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractAs processing, modeling software and design experience become more advanced, the idea of designing a system through models has gained traction in numerous Department of Defense programs. The efficiency gains piloted through implementation of models in the engineering process have also prompted the movement of incorporating models into the acquisition process. While initiatives have been started to incorporate models into the acquisition process (model-based acquisition), there is no publicly documented assessment to determine if an acquisition office possesses the needed skills. This paper explores the purpose of model-based acquisition and assessment structures, and proposes a CMMI like generalized quantitative method by which to assess the readiness of a government acquisition office for model-based acquisition.en_US
dc.description.statementofresponsibilityby Matthew Carey.en_US
dc.format.extent56 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.subjectSloan School of Management.en_US
dc.titleModel based capability assessmenten_US
dc.typeThesisen_US
dc.description.degreeM.B.A.en_US
dc.contributor.departmentSloan School of Management
dc.identifier.oclc1005076980en_US


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