SLAM takes a systems dynamics approach to cost modeling for the U.S. Navy’s early-stage life-cycle cost estimation. This innovative approach combines discrete, linear, and hierarchical cost-estimating methods with activity based costing and non-linear system dynamics modeling tools to create cost models that refine themselves.
Cost estimating methodologies and tools currently used by the U.S. Navy do not accurately determine the impact of early-stage design decisions on either acquisition or life-cycle sustainment costs. Early-stage cost estimation in ship building is an inherently difficult task due, in part, to the absence of firm definitions for specific ship components, including those for the structural design, propulsion system design and components, combat system design and components, and most auxiliary systems definitions. Many of these details, in fact, are not known until later design stages.
The RISE initiative is focusing on identifying/defining best practices for simulation modeling and analysis support for work center planning and decision making at the Oklahoma City Air Logistics Center (OC-ALC). The RISE findings will demonstrate the use of simulation modeling best practices in moving OC-ALC to a best-practice state.
This initiative, a ProPlan™ 21 modification initiative at OC-ALC, is focusing on identifying/defining best practices in the application of simulation modeling and analysis support for work center planning activities and decision making. This initiative will demonstrate, using a current OC-ALC work center planning problem, the application of the discovered simulation modeling best practices and develop a recommended set of requirements and actions that will allow OC-ALC to move towards that best-practice state.
Effective emergency response requires more than getting equipment and personnel to the scene. As important is determining what supplies are available and where they are available, as well as ensuring that required supplies are staged and deployed in a manner that meets the needs of the situation and any contingencies that might arise. Providing this kind of responsiveness requires two important capabilities: an awareness of what supplies are available, and the ability to communicate, easily and comprehensively, the status of those supplies. As any supply chain manager will tell you, inventory planning and awareness is critical to successful emergency response.
MERCURY is computer modeling technology that helps U.S. and coalition forces improve their counter insurgency measures. The technology provides computer modeling of counter insurgency message dissemination that considers the heterogeneity of population response and contact rate.
Predictive models have been used to help the U.S. and Coalition partners achieve force superiority in Iraq and Afghanistan. While these statistical and physically quantifiable measures have been successful in delivering optimized kinetic and electronic results in conventional warfare theaters, insurgent-based conflicts were proving more difficult as environments in which to repeat this success, particularly in influence and information operations campaigns.
FIEA is a language, tool framework, and methodology that enables the Air Force and DoD to share data among the many vendor specific tools and applications used in global joint service and multinational operations.
These tools, at best, provide a means for government agencies and their contractors to document their enterprise architectures: they don’t facilitate the model-based analysis of architectures, a critical step in improving systems capability, acquisition, and investment returns.
The PLANDROID™ technology is an automated asset employment and redeployment planning framework for world state information management, asset capabilities characterization, mission and task requirements planning, plan assessment, and decision support.
The advent of advanced sensors and remote control technologies has expanded the use of robotic elements and unmanned combat vehicles for a variety of roles and mission types. The increased use of robotic elements, however, also adds to the complexities involved in the planning and decision making regarding these asset deployments.