Analytics and Simulation for Decision Support: Good Results Achieved by Teaming the Two

Abstract

Analytic methods are used to create mission information from raw intelligence data from multiple sources that are collected to support end-user decisions. For example, sensor data from intelligence, surveillance, and reconnaissance (ISR) sensors provide human, open-source, electronic, and signal intelligence about targets of interest. However, missions spanning large operating environments may require diverse analytic methods that produce varying results, thus complicating the decision process and placing the burden of interpretation on the end user, such as a military commander. Coupling analytics with simulations helps to normalize analytic results by creating a repeatable approach through which a variety of data may be processed and interpreted for decision support. In particular, discrete event simulation (DES) is a proven methodology that enables the effective combination of modeling and analytics to create repeatable processes for many applications, ranging from aeronautics to health care to transportation.