Our government clients need to achieve their missions while operating in an environment characterized by global technology proliferation, rapidly emerging requirements, budget cutbacks and the high costs and long timelines of acquiring new system capabilities. As a result, they are investing in integrated solutions and new technologies to cut acquisition and support costs and to exploit multiple system capabilities more effectively across traditional organization, mission and domain boundaries. If we make poor investment decisions in the acquisition marketplace and the operational environment, we risk not just billions of dollars but precious lives as well.
We wanted to improve our understanding of the real capability gaps and shortfalls and investigate new alternatives and provide insights to the power of new technologies and operating concepts before significant acquisition resources are committed and major system capabilities are fielded. For example, what would happen if the information flow to decision makers (friendly or adversary) is denied, disrupted or delayed? We wanted a model that would find out.
Through Engility’s Modeling and Simulation ENnovation Center we maintain a Dynamic Modeling, Simulation and Analysis (MS&A) Lab where we focus 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 Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) architectures and their dynamic interactions with operators, systems and users at an enterprise level. Our solution seeks to quantify the attributes and capabilities of C4ISR systems and the impact of those systems on decision making and the effect they have in realistic operational scenarios.
The Whole Picture:Other analytic efforts tend to focus either on the singular need for evaluating the delivery of specific data (quality) to decision makers or on measuring the overall volume of data (quantity) collected by a system or delivering information within a specified timeframe (timeliness).
- The first approach (quality) focuses on specific information attributes or sensor phenomenologies but ignores the context and full extent of data flooding the decision maker, which might include errors, uncertainties and inaccuracies or redundant and obsolete information.
- The second approach (quantity) analyzes the capacity or amount of data available or throughput, but it does not qualify that data in terms of applicability to specific missions and information needs occurring in today’s multifaceted operations centers.
- The third approach (timeliness) defines the timelines for systems and architectures in static architecture frameworks but does not measure their temporal performance, benefit or utility in the context of dynamic missions and campaigns.
We take a great deal of pride in knowing that our own company comes to us for helpAlthough our solution has supported various C4ISR studies and analyses of alternatives for several federal customers, we take a great deal of pride in knowing that our own company comes to us for help. We were recently asked to support the development of a simulation environment to evaluate the utility of new Engility technology under development using internal research and development funds. Our solution continues to operate as the functional core of the new capability’s demonstration system, providing a robust, adaptable simulation environment with a wealth of data and analytic tools to use in demonstrating the performance benefit of artificial intelligence (AI) to human analysts.
Posted by Dr. Ken Myers and Paul Vogel
Paul is a mathematician and solutions developer who has been the technical lead on the Joint Force Operational Readiness Combat Effectiveness Simulator (JFORCES) program for 30 years. He led the technical and software development of various integrated projects using modeling, simulation and artificial intelligence (AI) techniques. These projects include the Agile Networking – Component Architecture & Simulation Environment (AN – CASE) Behaviors Module and the Knowledge-Decision-Consequence Management (KDCM) simulation system. He develops and employs customized solutions to answer key operational questions within comprehensive contextual representations.