Conditional and Probabilistic branching allow you to model the impact of different types scenarios that could occur during the execution of a project
Probabilistic branching models how different activities will be executed probabilistically once a common successor activity is completed. A common use of probabilistic branching is to model how the results of a test will trigger one set of activities or another. Often, there is historical data to provides guidance on the probability that test will result in a pass or fail.
For example, during construction, a test is made to determine the underlying ground condition with three possible outcomes: 2. No additional actions need to be taken. 2. Minor issues found, minor additional work required. 3. Major issues found, complete remediation of underlying ground required. Historical data suggests that the probabilities for each outcome are : 35%, 55%, 10% respectively. The probabilistic branching would be set up as follows.
Conditional branching is used to model situations where an alternate path will be taken if activity parameters meet a certain condition (Cost, Duration, Start Time, or Finish Time). For example, if in a development project a specific deadline for delivering a new component is missed, the project will switch to an existing technology. The conditional branching could be set up as the following where the original schedule would proceed if a specific finish date deadline for delivering the new technology is met, otherwise the project will switch to Plan B and use existing technology.