ABSTRACT
In 2019, predictive models were initially developed to attempt to better predict an annual budget for staffing overtime hours within a Royal Canadian Navy (RCN) fleet maintenance facility. The H20.ai open-source framework was used, and models were implemented in the R programming language. Model validation at the time showed the predicted hours were within 5% error rate compared to the actual data. However, when it came to re-apply the process to fiscal year 2020/2021 data, the impact of the COVID-19 pandemic on factors such as the workforce and the logistics supply chain, changed the system dynamics sufficiently that the autoML algorithms had difficulty generating accurate estimates. Therefore, it was decided to examine how times series forecasting methods would predict overtime hours at the fleet maintenance facility. Since historical daily data were readily available, the open-source Prophet model developed by Facebook was used because it can incorporate multiple seasonal patterns, as well as variable holiday effects. The models were tested on fiscal years 2019/2020 and 2020/2021, which showed over 90% accuracy in predicting the total overtime hours. The revised approach in this follow-on study was used to provide financial comptrollers with a prediction for fiscal year 2021/2022. © 2023, Springer Nature Switzerland AG.