5th Edition PMBOK Guide®–Chapter 11: Quantitative Risk Analysis and Modeling Techniques


Out of the six processes devoted to risk management in the 5th Edition of the PMBOK® Guide, five of them are planning processes and one is in the monitoring & controlling process group.    The five planning processes set up the guidelines for risk management of the project, identify risks, analyze risks quantitatively and qualitatively, and then set up risk responses.

Process 11.4 Quantitative Risk Analysis takes the risks that were ranked in terms of their probability and impact in process 11.3 Qualitative Risk Analysis, and now takes the analysis a step further by attaching an actual dollar amount to the risk based on the impact that it might have on the project weighted by its probability of occurring.

This post will discuss some of the tools & techniques used to do this quantitative risk analysis.

1.  Sensitivity analysis

How sensitive is the project to risks that might occur during the course of the project?   This is the question answered by sensitivity analysis.   The negative or positive impacts of risk events are estimated, and those risks that have the highest level of uncertainty, measured by the spread between the negative and positive impacts for those risks, are the ones that are the most sensitive.    Sometimes, a tornado diagramso called because of its tapering funnel shape, is used to represent these spreads between the negative and positive impacts of risks.

2.   Earned Monetary Value (EMV)

This is used for calculating the average outcome that might occur if an event happens.   This is done by multiplying the probabilities of various outcomes occurring times the monetary impact if they do occur, and then adding all of these up.  For example, if there is a 75% probability of a certain part costing $1000, and $25% probability of it costing $2000, the average weighted cost will be (75% X $1000) + (25% X $2000) = $750 + $500 = $1250.

3.  Modeling and simulation

Essentially, EMV is a “retail” version of finding the average outcome of a specific event; the Monte Carlo technique is a “wholesale” version of the same concept, except it finds the average outcome of an entire project.    The probabilities of various events occurring during the project are multiplied times their potential impacts on the project.    Since there will be a range of uncertainty for each event, the simulation is done over and over again and the average weighted impact over all of the simulations gives the range of impact for the entire project.

This is just a thumbnail description of these three techniques, but the important thing to understand is that these tools & techniques, particularly the second and third, give actual numbers that quantify the risk impacts on a project.   The results of these quantitative analyses can be used to justify expenditures to mitigate those risks, or to perhaps handle in a different way (transferring them to another company by having them do the risky activity, etc.).

The responses to the risks are developed in the next process, and that is the subject of the next post.

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