Introduction to @RISK for Project - More advanced decision-making features

Continuing our theme, this is the last in a series of three webcasts where we show how estimates and forecasts can be dramatically ‘tightened up’ using @RISK for Project. 

Project managers and stakeholders are constantly looking for ways to improve the quality of estimates and to find an equitable way of sharing of risk.  Most project managers develop a project plan for costing and resourcing purposes.  They might also develop a risk register with their client in order to prioritize and manage the risk events likely to occur.

However, there’s nothing like boiling all this analysis down into one key measure of confidence – the probability of success. 

We demonstrate the weakness of using overly simplistic extremes such as Best Case, Most-likely and Worse Case and instead assign probability distributions to the same 3-point estimates.  Running a Monte Carlo simulation then allows you to plot the results of thousands of scenarios and derive a vastly improved perspective from the distribution of expected project outcomes.

Armed with this information, you can then make decisions on whether to accept the risk or find cost efficient ways of reducing the uncertainty and thereby improve the plan.