With the increased focus on the availability of complex, multifunction systems, modeling processes and analysis tools are needed that help the availability systems engineer understand the impact of architectural and logistics design choices concerning system availability. Because many fielded systems are required to achieve a specified minimal availability over a short measurement period, a modeling methodology must also support computation of the distribution of operational availability for the specified measurement period. This paper describes a two-part behavior-based availability achievement risk methodology that starts with a description of the system's availability related behavior followed by a stochastic activity network-based simulation to obtain numeric estimate of expected availability and the distribution of availability over a selected time frame. The process shows how the system engineer freed to explore complex behavior not possible with combinatorial estimation methods in wide use today