The ATLAS™ technology is a unified modeling and experimentation framework that uses simulation, tradeoff analysis, and data mining analysis and optimization to enhance maintenance and maintenance manpower planning.
The ATLAS™ technology provides a collection of extendible and rapidly configurable (“plug and play”) software components that include easy-to-use tools for process and domain ontology capture, an embeddable simulation engine library, an extensive library of maintenance process descriptions, a model repository enabling transfer of process and domain knowledge through common interchange formats (e.g., XML), a simulation-based schedule generation capability, and an experiment management framework. The ATLAS™ technology bridges the gap between domain and simulation expertise, enabling novice users who are familiar with a domain to successfully apply and leverage simulation technology without having to become experts in simulation modeling techniques, languages, or environments. The technology also makes it possible to rapidly integrate maintenance process knowledge with decision support applications.
The technology provides an ideal solution for DoD and commercial maintenance organizations who want to leverage a simulation-based approach for their maintenance processes. The ATLAS™ modeling and simulation capabilities will allow these users to pinpoint inefficiencies in their maintenance processes and provide a method for determining the impact of personnel training and capabilities changes to their servicing equipment bottom line.
Consulting organizations can also benefit from the ATLAS™ technologies. ATLAS™ provides tools for collecting data, defining and organizing experiments, and running simulations to support funded consulting activities. The ATLAS™ framework provides a unique capability for rapidly collecting, organizing, and analyzing information about the client domain, allowing consulting firms to maintain and exploit knowledge about the industries in which their clients operate.
The ATLAS™ Initiative
The ATLAS™ technology gives the Army analysis and directed insight-based simulation at all levels of their logistics system for current and future combat systems maintenance and maintenance manpower planning. A central capability of the technology is the ability to experiment with changes in maintenance doctrine, work content variability, human performance, and process design variables to determine their effects on critical maintenance unit performance variables (e.g., cycle time, cost, logistics footprint, etc.).
The primary product of the ATLAS™ initiative was the ATLAS™ Go-to-War (GTW™) simulator. The GTW™ simulator has been used by AMCOM as a Class IX (spare parts) demand forecasting engine. Forecasting Class IX demands and warfighting assets availability in a dynamic environment with evolving mission needs requires that planners account for the interactions among the key factors that drive demand. The GTW™ simulator uses these key factors–optempo, asset employment policies (e.g., bank time curve-based usage), operating environment (e.g., fine sand, high altitude, severe temperature), maintenance practices and policies (e.g., time-life replacement, fixed-phase inspections, controlled substitution), accumulated age or wear, and probabilistic conditions (e.g., maintenance-induced damage, battle damage)–when forecasting demand. The simulator has shown significant payoff potential by enabling increased operational availability/readiness, a smaller logistics footprint, and considerable Class IX acquisition cost savings.
The effectiveness of the GTW™ simulator led AMCOM IMMC to select the technology as its Class IX demand forecasting tool of choice for the web-based Collaborating Online Between Resupply Activities (COBRA) system. With GTW™, AMCOM will be able to generate more accurate spare parts needs forecasts and communicate those needs to the supplier chain at greater lead times, significantly reducing costs and dramatically improving operational readiness.
The versatile ATLAS™ technologies also have a variety of uses in various other DoD analysis and decision-making applications, including deployment planning, analyzing surge impacts on sustainable readiness, force structure needs determination, tradeoff analysis, and various what-if analyses (e.g., maintenance doctrine, operational use policy, budget impacts, operating environment changes, etc.).
This material is based upon work supported by the U.S. ARMY RESEARCH LABORATORY under Contract No. W911QX-04-C-0020. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. ARMY RESEARCH LABORATORY.