An exploratory analysis of projected Navy officer inventory strength using data farming

U.S. statutory policy requires the armed services to continuously balance manpower inventory with congressionally authorized requirements. Inaccurate forecasts put the Navy's budget at risk and degrade overall mission readiness. Navy policymakers must be able to rely on accurate inventory forecasts to develop necessary manpower plans that steer inventory to match planned authorizations. Strength planners, in turn, rely on forecasting models like the Officer Strategic Analysis Model (OSAM) in an attempt to accurately predict future inventory levels. This study utilizes applications of data farming to OSAM to simulate Unrestricted Line Officer (URL) inventory over a seven-year period. Additionally, the research utilizes applications of Design of Experiments (DOE) to project Surface Warfare Officer (SWO) inventory across a variety of assumptions, including a proposed Enhanced Probationary Officer Continuation and Re-designation (EPOCR) policy. Analysis finds that current policy will reduce FY2016 URL inventory by 8% over a seven-year period, and over-execute SWO inventory authorizations by 40%. We find that EPOCR reduces operating strength deviation (OSD) in total SWO inventory strength by 12% by FY2022. Additionally, implementing a low accession plan and a high transfer plan is the most robust in correcting OSD. When implemented correctly, EPOCR has the potential to decrease OSD to modest levels with minimal risk of under-execution.http://archive.org/details/anexploratorynal1094550586Lieutenant, United States NavyApproved for public release; distribution is unlimited

Improving Navy MPTE Studies with Model-Driven Big Data

The goal of this research was to improve upon the ability of OPNAV N1 analysts to quickly and efficiently obtain experiment-based information from their computational models. The enhanced information will enable N1’s analysts to better support Navy leadership in resource and policy decisions that shape the future Navy and help it retain and develop its most talented Sailors. This project built on previous collaborations with N1 using data farming to enhance the information gleaned from their Navy talent management models, such as the Officer Strategic Analysis Model (OSAM) model, the Production Resource Optimization (PRO) model, and the Navy Total Force Strength Model (NTFSM). During this research period, (1) Ensign William Desousa (2015) investigated the behavior of economic inputs in NTFSM; (2) Lieutenant Peter Bazalaki (2016) used the new data farming capabilities we developed in OSAM to investigate Surface Warfare Officer (SWO) inventory across a breadth of possibilities; and (3) Lieutenant Allison Hogarth (2016) built, tested, and demonstrated a user interface in Excel that enables users of the PRO model to automatically execute a sophisticated design of experiments—the tool that enables this new capability is known as Production Resource Optimization Model With Experimental Design (PROMWED). In addition to working with the student-officers, the faculty supporting this project performed an empirical study of statistical software packages that may provide better understanding of the high-dimensional behavior of manpower models in the future (Erickson, Ankenman, & Sanchez 2016).Naval Research ProgramPrepared for Topic Sponsor: OPNAV N1; Research POC Name: Mr. Ian AndersoNPS-N16-N154-