Risk Analysis Techniques
Is used extensively in formative project planning and can also be used to advantage to identify and postulate risk scenarios for a particular project. It is a simple but effective attempt to help people think creatively in a group setting without feeling inhibited or being criticized by others.
The rules are that each member must try to build on the ideas offered by preceding comments. No criticism or disapproving verbal or nonverbal behaviors are allowed. The intent is to encourage as many ideas as possible, which may in turn, trigger the ideas of others.
2. Sensitivity Analysis
Sensitivity analysis seeks to place a value on the effect of change of a single variable within a project by analyzing that effect on the project plan. It is the simplest form of risk analysis. Uncertainty and risk are reflected by defining a likely range of variation for each component of the original base case estimate. In practice such an analysis is only done for those variables which have a high impact on cost, time or economic return, and to which the project is most sensitive.
Some of the advantages of sensitivity analysis include impressing management that there is a range of possible outcomes, decision making is more realistic, though perhaps more complex. And the relative importance of each variable examined is readily apparent. Some weaknesses are that variables are treated individually, limiting the extent to which combinations of variables can be assessed, and a sensitivity diagram gives no indication of anticipated probability of occurrence.
3. Probability Analysis
Probability analysis overcomes the limitations of sensitivity analysis by specifying a probability distribution for each variable, and then considering situations where any or all of these variables can be changed at the same time. Defining the probability of occurrence of any specific variable may be quite difficult, particularly as political or commercial environments can change quite rapidly.
As with sensitivity analysis, the range of variation is subjective, but ranges for many time and cost elements of a project estimate should be skewed toward overrun, due to the natural optimism or omission of the estimator.
4. Delphi Method
The basic concept is to derive a consensus using a panel of experts to arrive at a convergent solution to a specific problem. This is particularly useful in arriving at probability assessments relating to future events where the risk impacts are large and critical. The first and vital step is to select a panel of individuals who have experience in the area at issue. For best results, the panel members should not know each other identity and the process should be conducted with each at separate locations.
The responses, together with opinions and justifications, are evaluated and statistical feedback is furnished to each panel member in the next iteration. The process is continued until group responses converge to s specific solution.
5. Monte Carlo
The Monte Carlo method, simulation by means of random numbers, provides a powerful yet simple method of incorporating probabilistic data. Basic steps are:
a. Assess the range of the variables being considered and determine the probability distribution most suited to each.
b. For each variable within its specific range, select a value randomly chosen, taking account of the probability distribution for the occurrence of the variable.
c. Run a deterministic analysis using the combination of values selected for each one of the variables.
d. Repeat steps 2 and 3 a number of times to obtain the probability distribution of the result. Typically between 100 and 1000 iterations are required depending on the number of variables and the degree of confidence required.
6. Decision Tree Analysis
A feature of project work is that a number of options are typically available in the course of reaching the final results. An advantage of decision tree analysis is that it forces consideration of the probability of each outcome. Thus, the likelihood of failure is quantified and some value is place on each decision. This form of risk analysis is usually applied to cost and time considerations, both in choosing between different early investment decisions, and later in considering major changes with uncertain outcomes during project implementation.
7. Utility Theory
Utility theory endeavors to formalize management’s attitude towards risk, an approach that is appropriate to decision tree analysis for the calculation of expected values, and also for the assessment of results from sensitivity and probability analyses. However, in practical project work Utility Theory tends to be viewed as rather theoretical.
8. Decision Theory
Is a technique for assisting in reaching decisions under uncertainty and risk. All decisions are based to some extent on uncertain forecasts. Given the criteria selected by the decision-maker, Decision Theory points to the best possible course whether or not the forecasts are accurate.
The Quality Risk
This risk can best be expressed by the question: “What if the project fails to perform as expected during its operational life?” This may well be the result of less than satisfactory quality upon project completion, and is especially true if quality is not given due attention during the project life cycle. Since the in-service life of the resulting product is typically much longer than the period required to plan and produce that product, any quality shortcomings and their effects may surface over a prolonged period of time.
Consequently, of all the project objectives, conformance to quality requirement is the one most remembered long after cost and schedule performance have faded into the past. It follows that quality management can have the most impact on the long-term actual or perceived success of the project.
1. People do not, in fact, demand zero risk. They take risk every day, both consciously and subconsciously, and they are willing and able to take benefit/risk decisions, as in driving and speeding.
2. Peoples’ judgment of degrees of risk is not, however, coincident with most methodologies for measuring risk statistically. The public may greatly underestimate familiar risks (e.g. driving) while greatly overestimating unfamiliar risks (e.g. buying a home near a nuclear facility).
3. A variety of emotional, not logical, factors control risk perceptions:
a. Primary is the sense of personal control and the ability to mange the risk
b. Secondary are qualities of familiarity and conversely, dread. The greater the unfamiliarity and potential for connection to gruesome, the more it is likely to be judged as highly risky and therefore unacceptable.
4. Once established, risk perceptions are extremely hard to change. New information may be absorbed by the intellect, but it is not readily absorbed at an emotional level.
5. Risk perceptions reside fundamentally at an emotional level.