Predicting Federal Contractor Performance Issues Using Data Analytics

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.