Author Description

The U.S. Air Force (USAF) deploys flying units with readiness spares packages (RSPs) to try to ensure that the units are stocked with enough parts to be self-sufficient for 30 days. Predicting which parts are likely to fail--and, therefore, which parts should be included in the RSPs--is important because overstocking can be expensive and understocking can threaten mission readiness. This report presents a discussion of whether and when artificial intelligence (AI) methods could be used to improve parts failure analysis, which currently uses a model that assumes a probability distribution. To do this, several machine-learning models were developed and tested on historical data to compare their performance with the optimization and prediction software currently employed by the USAF, using A-10C aircraft data as a test case. This report is the third in a five-volume series addressing how AI could be employed to assist warfighters in four distinct areas: cybersecurity, predictive maintenance, wargames, and mission planning. This report is aimed primarily at those with an interest in predictive maintenance, RSPs, and AI applications more generally.