The Office of Acquisition Analytics and Policy, which is part of the Office of the Under Secretary of Defense for Acquisition and Sustainment

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Executive Summary

The Office of Acquisition Analytics and Policy, which is part of the Office of the Under Secretary of Defense for Acquisition and Sustainment (OUSD(A&S)), tasked IDA to assess the feasibility of using machine leaning to analyze contracts for major defense acquisition programs (MDAPs). The goal of the analysis was to extract data from contracts and predict program performance. The study was divided into three stages: crawling, walking, and running.

Crawling

The crawling stage consisted of building a dataset. During this stage of the analysis, contracts were collected and processed.1 The contracts chosen were listed in Selected Acquisition Reports (SARs) between December 1997 and December 2018, and were from MDAPs that were no longer reporting as of November 2019. Examining contracts from this period ensured that each program was more than 90 percent complete. Additionally, the dataset was limited to this period so that program performance outcomes were known which is necessary when using machine learning algorithms for predictive purposes. We collected 24,364 contract files in PDF format spanning 149 contract numbers and 34 MDAPs. (The MDAPs and their associated contract numbers are in Appendix B.) Finally, we used the Institute for Defense Analyses Text Analytics (IDATA) capability to turn the collected files into a machine-readable dataset.

Walking

In the walking stage, contract data were evaluated by training machine learning algorithms on our data to answer relatively simple questions. This activity ensured that the dataset was of reasonable quality, the machine-learning algorithms were functioning properly, and reasonable answers were produced. During this stage, word clouds were generated for each program. The following figures show the word clouds for two programs, CH-47F and ATACMS-APAM, respectively.

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