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).