Question

Compare and contrast the different techniques to analyze total project risk.

• What are the strengths and weaknesses of each technique?

Answer 1

Some of the risk analysis techniques:

• Brainstorming: this can be used in formative project planning & can be used to identify & postulate risk scenarios for a particular project. The advantage of this method is that it is very simple & effective & the weakness here is that each member must build ideas on the preceding comments.
• Sensitivity analysis: this technique seeks to place value on the effect of change of a single variable within the project by analyzing the effect on the project plan. This is considered to be simplest form of risk analysis. Other benefits are realistic decision making, impressing management, range possible outcomes. Weaknesses are that variables are treated individually & the sensitivity diagram would give no indication of probability of occurrence.
• Probability analysis: this specifies the probability distribution of each variable & then considers situations where any or all of these variables can be changed at the same time. The befit here is that this method overcomes the method of sensitivity analysis. The weakness of this method is Defining the probability of occurrence of any specific variable may be quite difficult, particularly as political or commercial environments can change quite rapidly.
• 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
• Decision tree analysis: in this case there would be number of opinions that would be available in the course of reaching the final results. The strength of this method is that it considers the probability of each outcome. The likelihood of failure is quantified & value is given to each decision. The weakness is that this decision is usually applied to cost & time considerations.
• 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.