Improving Navy recruiting with the new Planned Resource Optimization Model with Experimental Design (PROM-WED)

Abstract

The Navy spends over $300 million per year to recruit approximately 35,000 new active duty enlisted Sailors. The Navy has historically used a non-linear optimization model, the Planned Resource Optimization (PRO) model, to help inform decisions on the allocation of those recruiting resources. Input variables to the PRO model include economic influences and policy factors. The result is a recommended allocation of resources for advertisements, recruiters, enlistment bonuses, and education incentives. The PRO model's primary limitations are (1) potential deviations of input variables are not taken into consideration, and (2) extensive experimentation is not feasible. Realistically, input variables to the PRO model fluctuate, are unpredictable, and can interact with other variables to influence the recruiting environment and affect the optimal allocation of recruiting resources. This paper describes the Planned Resource Optimization Model with Experimental Design (PROM-WED), a tool that alleviates the limitations and enhances the analytic utility of the legacy PRO model. PROM-WED embeds the legacy PRO model within a data farming environment. PROM-WED's graphical user interface and decision support capability provide decision makers with robust insights into variable interactions and uncertainties to better inform their recruiting resourcing decisions.