Overview of @RISK for Project
Uncertainty is the inherent weakness of project planning. In this session, we explain uncertainty and show how estimates and forecasts can be dramatically ‘tightened up’ within existing risk management procedures using @RISK for Project.
Project clients and contractor managers alike are constantly looking for ways to improve the quality of their estimates and to find an equitable share 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, it’s a well known fact that the earlier risk management starts, the more effective it will be. One very important step in this process is to start measuring uncertainty so that you have a reference point and an easy-to-understand indicator of confidence.
We demonstrate the weakness of using overly simplistic extremes like Best Case / Most Likely Case / Worse Case. By adding 3-point probability distributions for task durations and costs and running a Monte Carlo random simulation you can plot the results of thousands of scenarios and derive a range of vastly improved measures for a project.
Armed with this information, you can then make a decision on whether to accept the risk or ‘go back to the drawing board’ to find cost efficient ways of reducing the uncertainty and thereby improve the plan.