Allocating Contingencies to Uncertain Cost Elements in a Cost Risk Analysis Model

In a previous entry to this blog I discussed how to assess the contingency required in a cost risk analysis study. The next step is to allocate the calculated contingency to uncertain cost elements that drive the variation in the total cost of the project. In this way, the contingency can be better managed and controlled throughout the life of a project.

While reviewing literature on this topic, I found a practical way to do this. This methodology uses the percentage contribution of each uncertain variable (usually 3 point estimate distributions) to the variance of the resulting distribution of the total cost.

To apply this method, we need to report the variance of each input distribution and the variance of the end result. In case that input distributions are independent from each other, we can just add up individual variances to estimate the variance of the total. However, this is hardly the case since correlation between input variables is expected in cost models.

@RISK allows reporting statistics from an input distribution without running a simulation as well as statistics that describe an output. These functions are from part of the @RISK functions library: Statistic Functions> Theoretical and Statistic Functions>Simulation Results, respectively. These functions can be accessed using the fx icon from the @RISK toolbar. 

To report the variance of input distributions we can use the RiskTheoVariance and for the output RiskVariance. The construction of the allocation model is shown below.

In the project risk management model above, it can be observed that the % Contribution to the Variance of the Total Cost is calculated as a proportion of the input variance to the total variance. Once these percentages are determined we can use them to allocate the management contingency to each cost element. It can be also observed that the engineering allowance is also calculated, and the decision maker now has criteria to manage and control contingencies.

Javier Ordóñez, Ph.D
Director of Custom Solutions

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