The staff of the Chief of Naval Education and Training (CNET) is working to develop and implement simulation-based decision support processes for the enterprise. This capability will streamline critical training support processes and eliminate data redundancy, resulting in improved utilization of resources. This paper will describe the approach CNET analysts have used to improve decision support capability through simulation, data warehousing, and web-enabled technology. This integrated approach provides a standard methodology that can be replicated throughout Navy organizations, improving decision support processes, and reducing future resource requirements.

While great progress has been made in the application of information technology to decision making for Navy training, the implementation of narrowly focused applications has resulted in a whole new set of problems and challenges. Multiple systems, implemented independently, meant data redundancy and lack of integration across the enterprise. Many of these systems contained the same data elements, but with different values, leaving managers searching for ground-truth information for decision-making.

The ability to access quality data is a major obstacle in building simulation-based decision support capability. Since most processes cross functional boundaries, the data required to create simulation models is frequently found in multiple data sources that were never meant to connect outside the application. Other data required for development of business simulations, such as processing times and resource allocations, is simply not captured anywhere. Data warehouse technology became critical for addressing the data migration problem. The challenge was how to seamlessly integrate multiple data sources contained in these many systems.

Through the use of a well-defined architecture, structured methodology, web-based data mart development, and simulation technology, CNET analysts and information technologists are building decision support capability to greatly enhance training support processes and systems. A hybrid approach using structured High-Level Architecture (HLA) data modeling techniques and rapid prototyping provided timely answers while maintaining the necessary structure to capture data for knowledge sharing and reuse. The standard methodology incorporated and lessons learned described in this paper will be beneficial to any organization attempting to build simulation-based decision support capability in today's dynamic environment.