Applying Automated Machine Learning to Improve Budget Estimates for a Naval Fleet Maintenance Facility

Abstract

A study was undertaken to improve the accuracy of staffing overtime budget predictions for a naval fleet maintenance facility and identify primary factors associated with overtime accrual. A series of models based on facility work orders were developed using the R statistical suite and the open source package EGO.ai for automated machine learning. Along with the model's predictive capabilities for budgetary planning, primary work order attributes associated with overtime hours were also determined based on the variables of importance. These gave insight into the type of maintenance and personnel assigned to the maintenance task which contributed to the highest accrual of overtime hours. Additionally, the monthly best curve fit for past budget predictions revealed a sigmoidal relationship, which was used to assist in the prediction of fiscal year 2019/2020 budget. The budget estimate from the model was found to be within 5% of the total budget expended hours over the fiscal year. As new annual data are provided or adjTitional facilities examined, the models can be retrained or rebuilt to include new information and allow decision makers to prepare more accurate funding estimates - potentially reserving funds for upcoming critical maintenance tasks or saving funds through alternative approaches to task management.