There are three answers to this:

- Do you really want to be using important models in such an error-prone and difficult to test environment as Excel? There's a whole Spreadsheet Risks Interest Group (http://www.eusprig.org/horror-stories.htm) that collects tales of billion-dollar errors that are attributable to (poor use of) Excel.

- A simple sensitivity analysis such as a tornado plot (while clearly much better than the common practice of reporting nothing on sensitivity of uncertain model inputs) is a local approach: it's telling you which variables are important around one specific point in the input space (generally the median for each input). A global sensitivity analysis method such as implemented here gives you information on the entire input space (which can be quite different if your input model has non-linear features).

- A tornado plot corresponds to a "one-at-a-time" sensitivity analysis (modifying each input variable individually), whereas a global method varies all inputs simultaneously and can reveal potential interactions between inputs, potentially very important if your model is non-linear.

Some background material on these points from a course that I run: https://risk-engineering.org/sensitivity-analysis/

You can run a simple sensitivity analysis using Excel with data tables (vary one input, keep the others constant). Large data tables usually slow down you Excel file a lot. In addition to these (first-order) sensitivities our tool also looks at global sensitivities. This also takes into account how certain inputs interact (we’re giving an example in the description on the site). We’re working on a new modelling tool that will provide a lot of this stuff out-of-the-box (sensitivity analysis, dealing with uncertainties, ...).