Overview

Accelerating the effectiveness of the contractor performance system

Properly managed contractor/supplier management systems include a robust performance monitoring and improvement mechanism. The Department of Homeland Security (DHS) is beginning to invest artificial intelligence/machine learning (AIML) technology into the Contractor Performance Assessment Reporting System (CPARS).

This project seeks to accelerate the usefulness of CPARS by determining how contractor performance reports should be used in sourcing purchases. This includes identifying issues and influences of those featuring AMIL. The team will also expand previous research on performance and cultural implications of Procurement Innovation Lab (PIL) processes and techniques.

Solution 

Through focused research, the team will offer insights on how a modern CPARS system should work in the federal government and how AIML should interact with users in such a system. Using both quantitative and qualitative analysis, multiple databases will be analyzed to determine the factors of contractor assessment. In-person interviews will also be conducted to gain the practical understanding of acquisition workforce readiness and experience with AIML in contractor performance systems.

Sociotechnical systems (STS) theory helps to form a basic understanding of how PIL techniques interact with DHS culture. However, it is not immediately clear how to best use that knowledge in a functional way. In another phase of the project, the team will work with DHS to develop a capability to continue STS and performative investigations of PIL techniques. Together, the group will ideate a method to use STS knowledge to effectively guide PIL implementation and use.

Impact 

Working with PIL leadership, the project team will help develop enhanced techniques and methods to implement PIL within current DHS purchasing culture. These solutions will also help DHS procurement understand how AIML technology can be implemented efficiently and used effectively for purchasing decisions and supplier performance evaluations.

Research Leadership Team 

Principal Investigator: Thomas Kull