There are many tools and techniques for Risk identification. Documentation Reviews
• Information gathering techniques
o Delphi technique – here a facilitator distributes a questionnaire to experts, responses are summarized (anonymously) & re-circulated among the experts for comments. This technique is used to achieve a consensus of experts and helps to receive unbiased data, ensuring that no one person will have undue influence on the outcome
o Root cause analysis – for identifying a problem, discovering the causes that led to it and developing preventive action
• Checklist analysis
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See example in appendix B.
• Risk categorization – in order to determine the areas of the project most exposed to the effects of uncertainty. Grouping risks by common root causes can help us to develop effective risk responses.
• Risk urgency assessment - In some qualitative analyses the assessment of risk urgency can be combined with the risk ranking determined from the probability and impact matrix to give a final risk sensitivity rating. Example- a risk requiring a near-term responses may be considered more urgent to address.
• Expert judgment – individuals who have experience with similar project in the not too distant past may use their judgment through interviews or risk facilitation workshops.
Tools and Techniques for Quantities Risk Analysis
• Data gathering & representation techniques
o Interviewing–You can carry out interviews in order to gather an optimistic (low), pessimistic (high), and most likely scenarios.
o Probability distributions– Continuous probability distributions are used extensively in modeling and simulations and represent the uncertainty in values such as tasks durations or cost of project components\ work packages. These distributions may help us perform quantitative analysis. Discrete distributions can be used to represent uncertain events (an outcome of a test or possible scenario in a decision tree)
• Quantitative risk analysis & modeling techniques- commonly used for event-oriented as well as project-oriented analysis:
o Sensitivity analysis – For determining which risks may have the most potential impact on the project. In sensitivity analysis one looks at the effect of varying the inputs of a mathematical model on the output of the model itself. Examining the effect of the uncertainty of each project element to a specific project objective, when all other uncertain elements are held at their baseline values. There may be presented through a tornado diagram.
o Expected Monetary Value analysis (EMV) – A statistical concept that calculates the average outcome when the future includes scenarios that may or may not happen (generally: opportunities are positive values, risks are negative values). These are commonly used in a decision tree analysis.
o Modeling & simulation – A project simulation, which uses a model that translates the specific detailed uncertainties of the project into their potential impact on project objectives, usually iterative. Monte Carlo is an example for a iterative simulation.
• Cost risk analysis - cost estimates are used as input values, chosen randomly for each iteration (according to probability distributions of these values), total cost will be calculated.
• Schedule risk analysis - duration estimates & network diagrams are used as input values, chosen at random for each iteration (according to probability distributions of these values), completion date will be calculated. One can check the...