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Title: Predicting Federal Contractor Performance Issues Using Data Analytics
Authors: David Gill
William A. Muir
Rene G. Rendon
Keywords: Predictive Modeling
Contracting Datasets
Big Data Analytics
Issue Date: 13-May-2019
Publisher: Acquisition Research Program
Citation: Published--Unlimited Distribution
Series/Report no.: Acquisition Management
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.
Description: Acquisition Management / Defense Acquisition Community Contributor
Appears in Collections:Annual Acquisition Research Symposium Proceedings & Presentations

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