May 12, 2022
A recurring challenge to successful Department of the Air Force (DAF) acquisition program execution is poor contractor performance. Early indication of potential contractor performance risks and execution issues is critical for proactive acquisition management. When contractors are in danger of not meeting contractual performance goals, DAF acquisition management may not be fully aware of the shortfall until, for example, a schedule deadline is missed, government testing indicates poor performance, or costs exceed expectations. In response, the authors developed a new way to apply data science to a variety of government and external data sources to assess the relative contractor performance risks and early indicators of performance issues in DAF acquisition contracts and programs. This method seeks to produce risk and performance indicators earlier than do current information sources and metrics.
In this report, the authors outline the functional software specification and basic design of that prototype, its architecture, and the core software components within it. The authors focus on the technical details of the prototype, along with summary context on the basic function of the approach.
The Prototype Architecture
Data Ingestion and Processing