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1 – 10 of 258Nathan Parker, Jonathan Alt, Samuel Buttrey and Jeffrey House
This research develops a data-driven statistical model capable of predicting a US Army Reserve (USAR) unit staffing levels based on unit location demographics. This model provides…
Abstract
Purpose
This research develops a data-driven statistical model capable of predicting a US Army Reserve (USAR) unit staffing levels based on unit location demographics. This model provides decision makers an assessment of a proposed station location’s ability to support a unit’s personnel requirements from the local population.
Design/methodology/approach
This research first develops an allocation method to overcome challenges caused by overlapping unit boundaries to prevent over-counting the population. Once populations are accurately allocated to each location, we then then develop and compare the performance of statistical models to estimate a location’s likelihood of meeting staffing requirements.
Findings
This research finds that local demographic factors prove essential to a location’s ability to meet staffing requirements. We recommend that the USAR and US Army Recruiting Command (USAREC) use the logistic regression model developed here to support USAR unit stationing decisions; this should improve the ability of units to achieve required staffing levels.
Originality/value
This research meets a direct request from the USAREC, in conjunction with the USAR, for assistance in developing models to aid decision makers during the unit stationing process.
Details