Perform Quantitative Risk Analysis

After ‘Qualitative‘ risk analysis, you must have seen this coming – a ‘Quantitative’ risk analysis.

Here’s the all important PG-KA mapping,

From a definition standpoint, this process is all about numerically analyzing the identified risks. ‘Numerically’ being the keyword here.

You might remember that we had numbers even in the Qualitative Risk Analysis when we used the Probability Impact Matrix. We take a step further in Quantitative Risk Analysis in a bid to understand how much the risk will end up costing you. To do so, we make use of the Quantitative Risk Analysis and Modeling Techniques. Below are three such techniques,

1. Expected Monetary Value Analysis

Before discussing EMV, its important to note that EMV is just one of the tools and techniques of this process. Other tools and techniques revolve around data gathering and analyzing. The output, just like Perform Qualitative Risk Analysis process, is Project Document Updates.

With the help of EMV analysis you will be able to calculate the costs of all the paths you might take during the course of your project. The main method of EMV analysis you should know from PMP exam perspective is Decision Tree Analysis. You basically draw all the decisions you think you need to make while dealing with risks with their respective costs. Then you can add all the numbers to come up with what you need to spend on decision making.

2. Sensitivity Analysis

This is where you look at one variable or risk factor in complete isolation and analyze the impact it might have. Let’s say you want to do a sensitivity analysis of the risk of procurement delay. You will only consider the delay it will have on your project and not the extra team resource cost that you might have to incur due to this. Tornado diagrams are generally used to look at a project’s sensitivity to only one risk factor.

Check out this article for more on Tornado Diagrams

3. Modeling and Simulation

Monte Carlo analysis tops the list when we talk about simulation. Monte Carlo analysis is basically a tool that can randomize the outcomes of project risks and the probabilities of their occurrence to help you get a good sense of handling the risks

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