Predicting and Improving FA-18 Squadron Readiness Using the Digital Aviation Readiness Technology Engine and the Resource Optimization and Allocation Model

Given manning, training, and equipment datasets from Naval FA-18 squadrons, a machine learning model for determining the monthly mean number of mission capable aircraft per squadron is created. This model is extended to create an ensemble of models that determine the flight hour execution of a squadron over a three-month period. The ensemble, known as the Digital Aviation Readiness Technology Engine (DARTE), is used to predict squadron performance and readiness, classifying a squadron's future performance with 70% accuracy 90 days in advance. This information, along with historical and contextual information, is presented in an interactive dashboard that may be used as a decision-making aid. The Resource Optimization and Allocation Model (ROAM) further aids decision makers with a statistical approach to proactively improving squadron readiness by optimizing the distribution of maintainers throughout the enterprise to maximize mission capable aircraft. This novel approach to maintainer distribution is not concerned with meeting personnel targets, but rather obtaining a goal: providing high-quality courses of action to improve readiness. ROAM provides insights on the overall manning levels and may be used to influence training and hiring practices. Together, DARTE and ROAM improve the readiness of the Naval Aviation Enterprise.