The Beta Edition of Statistical PERT uses the beta probability functions inside of Microsoft Excel. Specifically, the Beta Edition of SPERT uses BETA.DIST (beta distribution) and BETA.INV (beta inverse).
The key advantage of using the beta distribution over the Normal Edition of Statistical PERT is that, using the beta distribution, you can more accurately model skewed bell-shaped uncertainties. With the Normal Edition of SPERT, you can still model bell-shaped uncertainties that are skewed, but only to a certain point before the resulting probabilistic estimates are not reliable. For example, when the range between the minimum point-estimate and the most likely point-estimate (the mode) is half or one-third or even one-fourth the range between the most likely point-estimate and the maximum point-estimate, the resulting probabilities are still pretty accurate within a few percentage points. (This assumes that the standard of truth is using the PERT beta distribution as found in Palisade’s @Risk RiskPERT function — which is a key assumption that may or may not be true for a given estimate).
With the Beta Edition of Statistical PERT, you can model many skewed, bell-shaped uncertainties even to the point where you are dealing with a triangular distribution on either the left-side or the right-side of the curve.
If you download the development build of the Beta Edition of SPERT, play around with the ‘SPERT Beta Curve’ worksheet. By changing the values for the Minimum, Most Likely and Maximum point-estimates (cells B4, C4 and D4) and also the Most Likely Confidence column (cell J4), you’ll see different representations of the bell-shaped curve on the chart that’s in that worksheet.
The Beta Edition of SPERT uses 150 combinations of the two key parameters required by the BETA.DIST function: alpha and beta. Each combination has been carefully calibrated so the resulting bell-shaped curve still retains the mode as found in the Most Likely point-estimate in cell C4. The mode may not be exactly, precisely the same in the bell-shaped curve, but it will be very close — close enough that it should be fine for most estimation scenarios.
Try using the Beta Edition of SPERT! Just be aware that this is still a development build.