Network model representations have been used extensively to define process systems, such as scheduling problems, transshipment problems, etc. These representations are attractive because they provide an effective visualization of the interaction between the various activities in the system. Furthermore, advanced graph-theoretic techniques and heuristics for networks can be used to enhance problem solving. In this paper, we will show how graph-theoretic techniques can be used to translate a probabilistic network model of a sequence of process activities into a spreadsheet model. By incorporating graph-theoretic concepts such as connectivity, cardinality, dependency, cycles, critical paths, etc. into a novel software tool, the tedious and time consuming process of building the spreadsheet model is reduced by allowing the user to build the spreadsheet model directly from the network model of the process. Editing the network model is fast and easy. Once the user is satisfied with the network model, the tool converts it into a spreadsheet, which preserves the precedence relationships and attributes of the activities including their uncertainties. To illustrate the utility of the concept, we will show how the tool has been used to translate probabilistic network models describing the process of new product development into a spreadsheet for managing risk. Various criteria such as robustness and reliability are used to compare the spreadsheet model created by the tool with spreadsheet models built in a traditional way. The results indicate a 20-fold increase in speed while simultaneously increasing the robustness.