Modeling and Simulation (M&S) users who require complex M&S typically do not have a long lifecycle for an experiment, analysis initiative or simulation-based event. To reduce cost, they need to use well-established simulation architectures and robust models that are easy to integrate with other simulations. This desire for a short lead time for system design, development, integration, execution and data analysis forces the system definition and design to happen very quickly.
In addition to having limited time and financial resources, analysts are being forced to address ever increasingly multifaceted problems. These problems require resources far beyond the simple spreadsheets of the past. With the advent of multicore desktop computers, cloud architectures and data mining tools, analysts have the opportunity to leverage vast amounts of data in order to conduct their analyses. But manipulating output data is not the same as analyzing data. Truly analyzing data requires understanding the linkages among the input data, the design assumptions and the intricacies of the systems producing the data.
The United States (US) Army Research Laboratory (ARL) has developed tools and processes that will help M&S users with their goals of understanding the simulation capabilities that are available and executing complex M&S environments as needed rather than when technical staff is available. A description of the users’ needs will provide the context of our efforts.
Needs of the User
The majority of analysts will agree that there never seems to be enough time when preparing for an experiment, test, analysis initiative or simulation-based event. A long planning cycle is a luxury they are not afforded. The analysts desire the ability to obtain key information in an effortless manner and to be able to employ tools that do not require a steep learning curve. Ultimately, the analysts want to spend more time examining the findings and less time learning to utilize the simulation tools.
There is seldom a single simulation that will accomplish the analysts’ goals on its own; rather engineers will integrate multiple systems together. Each system represents specific aspects of the synthetic environment being used. These M&S users rely on standards and simulation developers to get the systems to communicate using the same syntax. This often works to instantiate a System of Systems (SoS) architecture [1] and to get models to share information. A SoS environment is an assembly of applications that together provide more capability than the sum of their individual capabilities. Within the M&S community, the applications assembled are each focused on representing a specific warfare function (or functions) based on data and models from an organization considered to be the center of excellence for that aspect of warfare. The SoS architecture provides many benefits when compared to executing a single monolithic model, including performance, model management and information transparency for analysis.
The United States Department of Defense (DoD) acquisition community is focused on creating viable materiel solutions. Figure 1 shows the DoD Acquisition Life Cycle [2] . While a formal Materiel Solutions Analysis occurs prior to Milestone A, a Project Manager (PM) can be faced with the challenge that the materiel solution they are developing is not meeting its required specification(s). However, this materiel may arguably be better than what is fielded for the same purpose. The challenge becomes how to make that case to senior acquisition decision makers who determine if a system is acquired or not.
Figure 1 – DoD Acquisition Life Cycle.Â