Data gathering and representation techniques
“Data Gathering and Representation Techniques” is a tool/technique for the process “Perform Quantitative Risk Analysis “.
– Interviewing. Interviewing techniques draw on experience and historical data to quantify the probability and impact of risks on project objectives. The information needed depends upon the type of probability distributions that will be used. For instance, information would be gathered on the optimistic (low), pessimistic (high), and most likely scenarios for some commonly used distributions. Examples of threepoint estimates for cost are shown in Figure 11-13. Additional information on three-point estimates appears in Estimate Activity Durations and Estimate Costs. Documenting the” rationale of the risk ranges and the assumptions behind them are important components of the risk interview because they can provide insight on the reliability and credibility of the analysis.
Range of Project Cost Estimates WBS Element Design Build Test Total Project $4M $16M $11M $31M $6M $20M $15M $41M $10M $35M $23M $68M Interviewing relevant stakeholders helps determine the three-point estimates for each WBS element for triangular, beta or other distributions. In this example, the likelihood of completing the project at or below the most likely estimate of $41 million is relatively small as shown in the simulation results in Figure 11-17 (Cost Risk Simulation Results).
Low Most Likely High
Figure 11-13. Range of Project Cost Estimates Collected During the Risk Interview – Probability distributions. Continuous probability distributions, which are used extensively in modeling and simulation, represent the uncertainty in values such as durations of schedule activities and costs of project components. Discrete distributions can be used to represent uncertain events, such as the outcome of a test or a possible scenario in a decision tree. Two examples of widely used continuous distributions are shown in Figure 11-14. These distributions depict shapes that are compatible with the data typically developed during the quantitative risk analysis. Uniform distributions can be used if there is no obvious value that is more likely than any other between specified high and low bounds, such as in the early concept stage of design.
Beta and triangular distributions are frequently used in quantitative risk analysis. The data shown in the figure on the left (Beta Distribution) is one example of a family of such distributions determined by two “shape parameters”. Other commonly used distributions include the uniform, normal and lognormal. In these charts the horizontal (X) axes represent possible values of time or cost and the vertical (Y) axes represent relative likelihood.
Beta Distribution Triangular Distribution 0,1
This definition was found in the PMBOK V5
Go back to the Glossary or to the Mapping