Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/1751
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | David Gill | |
dc.contributor.author | William A. Muir | |
dc.contributor.author | Rene G. Rendon | |
dc.date.accessioned | 2020-03-16T18:01:04Z | - |
dc.date.available | 2020-03-16T18:01:04Z | - |
dc.date.issued | 2019-05-13 | |
dc.identifier.citation | Published--Unlimited Distribution | |
dc.identifier.uri | https://dair.nps.edu/handle/123456789/1751 | - |
dc.description | Acquisition Management / Defense Acquisition Community Contributor | |
dc.description.abstract | The 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.sponsorship | Acquisition Research Program | |
dc.language | English (United States) | |
dc.publisher | Acquisition Research Program | |
dc.relation.ispartofseries | Acquisition Management | |
dc.relation.ispartofseries | SYM-AM-19-061 | |
dc.subject | Predictive Modeling | |
dc.subject | Contracting Datasets | |
dc.subject | Contractors | |
dc.subject | Big Data Analytics | |
dc.title | Predicting Federal Contractor Performance Issues Using Data Analytics | |
dc.type | Article | |
Appears in Collections: | Annual Acquisition Research Symposium Proceedings & Presentations |
Files in This Item:
File | Size | Format | |
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SYM-AM-19-061.pdf | 895.84 kB | Adobe PDF | View/Open |
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