Oracle Crystal Ball

Pros: The tool allows the user to work on their projects within Excel (the gold standard) and utilize the Oracle Crystal Ball tools without changing the platform. Although the UI is pretty basic, it delivers exactly what is needed to perform quick Monte Carlo simulations and sensitivity analysis.

Cons: The Excel add-on can be a little problematic from time to time but you can easily find answers on the web to back on track. The user interface is dated and the charts are basic.

Overall: I found Crystal Ball during an executive course at Wharton over 8 years ago. I have continued to use the tool ever since.

Pros: The ability to perform powerful analysis and intuitive Monte carlo simulations using the existing Excel infrastructure is exceptional.

A solid GUI that allows for rapid changes and adjustments, that once learned allows for near-real time adjustments and rapid analysis of numerous data sets, with excellent visualization of aggregated outputs.

Cons: Learning curve is steep at the very initial point, but there is excellent documentation from both Oracle and third party sources.

Ultimately, the only real capability missing would be to incorporate this capability into non-Microsoft spreadsheet tools - i.e. GoogleSheets. The majority of the limitations on ease of use for middleware of this type are really inherited constraints coming from the platform it has to run on, in this case Excel.

Overall: If you need something that falls under this application set, this is arguably the best tool on offer.

Pros: It provides a means to do cost & schedule risk analysis for projects. It has a wide range of inputs/functionality that make it adaptable.

Cons: The more advanced parts of the software required a good deal of learning.

Overall: Very solid software that provides good input for project teams.

Pros: Crystal Ball makes Monte Carlo Simulation very easy. It is impressively easy to learn and is capable of advanced finance/forecasting techniques.

Cons: The biggest challenge with Crystal Ball is understanding its inner workings before applying it to problem sets. It is crucial to understand the implications of how the software performs its calculations before use.