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dc.contributor.authorDavid Gill
dc.contributor.authorWilliam A. Muir
dc.contributor.authorRene G. Rendon
dc.identifier.citationPublished--Unlimited Distribution
dc.descriptionAcquisition Management / Defense Acquisition Community Contributor
dc.description.abstractThe purpose of this research is to evaluate the degree to which predictive modeling techniques can enhance the quality of contractor source selection decisions. Use risk indicators created from existing publicly available contracting datasets to predict which contractors are most likely to perform successfully. Examples of risk indicators are quantitative measurements of contractor dollar velocity, instability in federal contract business, and level of experience in performing similarly sized contracts. Examine how big data analytics can be used to augment traditional source selection techniques such as proposal evaluation and past performance/responsibility checks.
dc.description.sponsorshipAcquisition Research Program
dc.languageEnglish (United States)
dc.publisherAcquisition Research Program
dc.relation.ispartofseriesAcquisition Management
dc.subjectPredictive Modeling
dc.subjectContracting Datasets
dc.subjectBig Data Analytics
dc.titlePredicting Federal Contractor Performance Issues Using Data Analytics
Appears in Collections:Annual Acquisition Research Symposium Proceedings & Presentations

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