Predicting Federal Contractor Performance Issues Using Data Analytics
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Authors
Gill, David
Muir, William A.
Rendon, Rene G.
Subjects
Advisors
Date of Issue
2019-04-30
Date
Publisher
Monterey, California. Naval Postgraduate School
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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.
Type
Report
Description
Series/Report No
Department
Organization
Identifiers
NPS Report Number
SYM-AM-19-061
Sponsors
Naval Postgraduate School Acquisition Research Program
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Format
Citation
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.