Authors:
Cheryl Eisler
1
and
Mikayla Holmes
2
Affiliations:
1
Centre for Operational Research and Analysis, Defence R&D Canada, 60 Moodie Drive, Ottawa, Canada
;
2
University of Victoria, Department of Mathematics and Statistics, 3800 Finnerty Road (Ring Road), Victoria, Canada
Keyword(s):
Automated Machine Learning, Predictive Analytics, Budget, Overtime Hours, Fleet Maintenance Facilities, Work Orders.
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 H2O.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 y
ear. As new annual data are provided or additional 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.
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