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We have all heard about the many large publicly funded infrastructure projects that have gone far over budget and as a consequence have drawn great levels of negative attention. The reality is that not only do large public projects go over budget but the majority of large public, as well as private, capital projects are likely to end up over budget.
For executives, managers and their teams responsible for project cost estimation, it is a difficult challenge to produce an accurate project cost estimate that takes into account the risks and uncertainties capable of causing costs overruns. This whitepaper will introduce and discuss cost estimation techniques based on quantitative risk analysis1 that can improve the estimation of a project’s budget, including contingency and escalation costs, by taking into account risks and uncertainties. If applied correctly, quantitative risk analysis will allow a cost estimation team to not only forecast the range of possible costs but also the probability of those costs. In addition, this paper will discuss several of the hidden pitfalls that must be avoided when using quantitative risk analysis for cost estimation.
Let’s first consider and review some of the root causes for cost overruns:
While the above list is not inclusive, it illustrates that the actual costs of a project are often greatly affected by unknowns and risks. Ignoring such uncertainties and risks in the estimation process therefore often causes projects to overrun their budgets.
What is Quantitative Risk Analysis and How Can it Help You?
Typically, there are a large number of uncertainties involved in the generation of cost estimates for a large project. One way to take into account, understand and possibly manage these uncertainties is through a technique called Quantitative Risk Analysis. Quantitative risk analysis is a process that identifies and quantifies the uncertainties associated with a project and then develops a probabilistic model7 to represent the project. The output of this model then provides a view of the risk and uncertainty associated with cost of the overall project as well as the component parts of the project.
For example, the output from a quantitative risk analysis model can answer many questions such as: “What is the probability that the total cost of the project will exceed a specific value?” with the answer in the form of “There is a 20% probability that the total costs will exceed $10 Million.”
Uncertainties and risks in a quantitative risk analysis model are usually represented by a probability distribution. For example, the costs associated with structural steel are no longer represented in the cost estimate as a single point with an additional allowance for escalation as in traditional estimating. Instead, structural steel costs would now be represented by a range of possible costs, including escalation, along with the probability of each possible cost for structural steel.
If you have found the information above useful and would like to read the entire whitepaper, please complete the form below and the complete paper will be emailed to you (free of cost).