At work, I’m planning a big upgrade to my employer’s enterprise resource planning (ERP) system. It’s been a decade since the last upgrade, and our vendor support is running out next year.

It’s early in the project lifecycle. We just selected a partner to help us with the upgrade. We’re now going through the procurement phase (detailed SOW and contract).

Before we engage with this vendor to do discovery work, I’m doing project planning with my project team. We identified 11 major activities that this project will plan and execute. Our project sponsor wants to know, how long will this project take?

To answer that question, our project team has modeled the 11 activities using both SPERT Normal Edition, SPERT Beta Edition and we used Monte Carlo simulation (Palisade’s @Risk program).

We used this global heuristic for the 11 activities: the *minimum* duration is 25% less than the *most likely* duration, and the *maximum *duration is 50% greater than the *most likely *duration. If we wanted, we could alter the heuristic results for each activity. And because we’re using SPERT, we can apply our subjective judgment, too, to express *how likely *will the *most likely *outcome really occur.

This is going to be a year-long project.

What’s interesting to me is that, of course, the *beta* distribution is a better fit to the way we’ve modeled the duration uncertainty of each activity. And yet, the SPERT Normal Edition calculates nearly the same result as the SPERT Beta Edition (or a Monte Carlo simulation, too, for that matter).

## SPERT Beta Edition:

- 50% probable duration is
**245**days - 80% probable duration is
**276**days - 90% probable duration is
**292**days - 95% probable duration is
**304**days

## SPERT Normal Edition:

- 50% probable duration is
**247**days - 80% probable duration is
**278**days - 90% probable duration is
**294**days - 95% probable duration is
**307**days

The *normal* distribution can handle mild-to-moderately skewed duration uncertainties like these. You **don’t** necessarily have to use a *best-fitting *probability distribution to model your project’s uncertainties.

Sometimes, using the *normal* distribution is “good enough” to make a good decision. And the normal distribution is among the easiest to use in Excel (NORM.DIST, NORM.INV functions).