Our government clients responsible for national security are operating in an environment characterized by global technology proliferation, rapidly emerging mission and operational requirements, budget cutbacks, and the acquisition of new system capabilities.
As a result, they are investing in integrated solutions and new technologies that cut acquisition and support costs and exploit multiple system capabilities across organization, mission, and domain boundaries. If poor investment decisions are made, they risk not just billions of dollars but the lives of Americans as well.
A lot must be understood about new technologies and operating concepts before committing significant resources. We have to help our customers understand their capability gaps and shortfalls, investigate new alternatives, and gain insight into the technologies’ strengths as well as weaknesses. For example, what would happen if the information flow to decision-makers — friendly or otherwise — is denied, disrupted, or delayed? We want a model that finds that out.
Through SAIC’s modeling and simulation center, we maintain a dynamic modeling, simulation, and analysis (MS&A) lab focused on client requirements, including the need to objectively measure the impact of information on decision-making and mission outcomes. We provide an integrated simulation framework to model C4ISR (communications, computers, intelligence, surveillance, and reconnaissance) architectures and their dynamic interactions with operators, systems, and users at an enterprise level.
Our solution quantifies the attributes and capabilities of C4ISR systems and their impact on decision-making and in realistic operational scenarios.
The whole picture
Analytic efforts tend to focus on the singular need for evaluating the delivery of specific data to decision-makers, or on measuring the overall volume of data collected by a system, or on delivering information within a specified time frame. It’s a classic quality versus quantity scenario.
The first approach — quality — focuses on specific information attributes or sensor purposes, but it ignores the context and full extent of data flooding the decision-maker, which might include errors, uncertainties, and inaccuracies or redundant, obsolete information.
The second approach — quantity — analyzes the capacity or amount of data available, but it does not qualify that data in terms of application to specific missions or information needs occurring in today’s multifaceted operations centers.
The third approach — timeliness — defines the time lines for systems and architectures in static architecture frameworks, but it does not measure their performance, benefit, or utility in the context of dynamic missions and campaigns.