Early Predictive Indicators of Contractor Performance: A Data-Analytic Approach
Abstract
In this report, the authors describe a new way to apply data science to a variety of disparate and disjointed government and external data sources to highlight the relative contractor performance risks and provide earlier indicators of performance issues in DAF acquisition contracts and programs than would normally be achieved in traditional formal reporting. Although the authors cannot definitively state this is the optimal approach, this method seeks to produce risk and performance indicators earlier than current information sources and metrics do. This is the final report for Phase II of an effort to test the approach outlined here by building a prototype that uses actual data to calculate contractor risk measures and performance metric values relative to those of their peers, the available contractor base, or fixed thresholds, presenting outliers to prototype users for further human investigation and assessment.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2022
- Accession Number
- AD1168975
Entities
People
- Alejandro V. Camargo
- David Kravitz
- Grant E Johnson
- James Ryseff
- Megan Mckernan
- Philip S. Anton
- Samantha S Cohen
- Stephen B. Joplin
- William Shelton
Organizations
- RAND Corporation