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dc.contributor.authorRene G. Rendon
dc.contributor.authorUday Apte
dc.contributor.authorMike Dixon
dc.identifier.citationPublished--Unlimited Distribution
dc.descriptionContract Management / NPS Faculty Research
dc.description.abstractThis paper examines the use of Big Data analytic techniques to explore and analyze large datasets that are used to capture information about DoD services acquisitions. It describes the burgeoning field of Big Data analytics, how it is used in the private sector, and how it could potentially be used in acquisition research. It tests the application of Big Data analytic techniques by applying them to a dataset of CPARS ratings of acquired services, and it creates predictive models that explore the causes of failed services contracts using three analytic techniques: logistic regression, decision tree analysis, and neural networks. The report concludes that four variables exhibit the largest impact on the success/failure rates of services contracts: type of contract; awarded dollar value; workload per filled billets; % of 1102 billets filled by contracting office.
dc.description.sponsorshipAcquisition Research Program
dc.languageEnglish (United States)
dc.publisherAcquisition Research Program
dc.relation.ispartofseriesBig Data
dc.subjectBig Data Analysis
dc.subjectServices Acquisition
dc.subjectServices Contracts
dc.subjectSuccess of Services Contracts
dc.titleBig Data Analysis of Contractor Performance for Services Acquisition in DoD: A Proof of Concept
dc.typeTechnical Report
Appears in Collections:Sponsored Acquisition Research & Technical Reports

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