Now is the post where we discuss the data analysis techniques of quantitative risk analysis.

11.4.2 Perform Quantitative Risk Analysis: Tools and Techniques

11.4.2.5 Data Analysis

- Simulation–in the qualitative data analysis, you assigned a probability and an impact to each individual project risk. With simulation, it’s as if you roll the dice on each risk to see whether in a particular scenario the risk would occur. If it does occur, you count that impact towards the total. If it does not occur, then you don’t count it. You sum up the impacts that get added to the total by all the triggered risks for that scenario. Then you have the computer software do the calculation over and over again, and you get as a result a probability curve for how much the total impact will be. Then you choose a confidence interval, so that you can say as a conclusion that “within 90% confidence level, the potential impact of the project risks will be a total of X dollars.” For an example of such a probability curve associated with the simulation, see Figure 11-13 on p. 433 of the 6th Edition of the PMBOK® guide. Note that because such a simulation requires sometimes thousands of calculations, this is not something you will be required to do for the exam, but you will be required to know what this data analysis does and basically how it works.
- Sensitivity analysis–out of all the individual project risks, there will be some that have the most important potential impact on project outcomes. A typical way of representing these potential impacts is the “tornado diagram”, so called because the wedge shapes that represent the largest potential impact are put on top, and the smaller risks with progressively smaller impact are put underneath so that the diagram is vaguely funnel shaped. An example of such a tornado diagram is in Figure 11-14 on p. 434 of the 6th Edition of the PMBOK® guide.
- Decision tree analysis–the concept behind decision tree analysis is actually pretty simple. Say there is a scenario in which there are two possible decisions, decision A and decision B. For each decision, there are two possible outcomes, outcome A and outcome B. You calculate how much money it will cost to adopt decision A, and calculate the Expected Monetary Value (EMV) of scenario A by taking the impact of outcome A times the probability of it occurring plus the impact of outcome B times the probability of it occurring. The total EMV of scenario B is then calculated and compared to the EMV of scenario A. Which has the lower EMV, decision A or decision B? That is the decision that you should choose. Look at the example of Figure 11-16 of a Decision Tree problem on p. 435 of the 6th Edition of the PMBOK® guide.
- Influence diagrams–you take a particular situation within a project, most likely the ones you get from the sensitivity analysis described above that show the situations that have the greatest potential impact on the project. What are the factors that influence these individual project risks? You then do an analysis of the probability distributions effecting these influences, so you can model what the combined total of these influences will be on the probability and impact of the individual project risk you are looking into.

Although all of these techniques are quantitative, they all ultimately stem from the same concepts of probability and impact that you looked at in the previous process Perform Qualitative Risk Analysis. The only difference is that a) the impact has to have a specific dollar amount rather than a qualitative description, and b) the analysis of all the individual project risks is summed up to show a total potential impact on the overall project objectives, usually involving the three basic constraints of schedule (will the project be done within the deadline), cost (will the project be done within the budget), and scope (will all the requirements of the project be met).

The next post will discuss the outputs of this process…

Filed under: Uncategorized |

## Leave a Reply