- Weather: A 25 percent chance of excessive snow fall that will delay the construction for two weeks which will, in turn, cost the project to $80,000.
- Construction cost: A 60 percent probability that the price of the project will go down by $100,000 because of lowering construction cost.
- Labor turmoil: A 15 percent probability of construction coming to a halt if the workers go on strike. The impact would lead to a loss of $150,000. Consider your industry and geographic area to determine whether this risk would have a higher probability.
To find out the EMV, the following calculations are made:
Weather: (25/100)*(-80000), negative since loss
=-20,000
Construction cost: (60/100)*(100,000), positive since gain
Labor turmoil: (15/100)*(-150,000)
=-22,500
Therefore Project EMV = -20000+60000-22500=15,500, hence the project will gain $15,500 as the final value is positive.
Monte Carlo simulation:
Monte Carlo analysis uses simulations to determine the risks for various scenarios. Here, certain variable inputs are considered to generate the range of outcomes with a confidence level for each outcome. This is done by establishing a mathematical model. This technique is used for forecasting the likely outcome of an event and thereby making project decisions.
Let us take an example. Suppose there are 3 modules for a project. Monte Carlo analysis determines the best case (optimistic), most likely and worst case (pessimistic) scenarios as follows:
Modules
|
Best case completion
|
Most likely case completion
|
Worst case completion
|
Module1
|
2 days
|
4 days
|
5days
|
Module2
|
3 days
|
4 days
|
6 days
|
Module3
|
1 day
|
3 days
|
5 days
|
Total duration
|
6 days
|
11 days
|
16 days
|
Let us assume that we run the simulation for 500 times. We see that the project can be completed between 6 to 16 days. We get the following results from the simulations:
Total Duration
|
No. of times the simulation result was less than or equal to the total duration
|
Percentage simulation where the result was less than or equal to the total duration
|
6
|
5
|
1
|
7
|
20
|
4
|
8
|
75
|
15
|
9
|
90
|
18
|
10
|
100
|
20
|
11
|
125
|
25
|
12
|
140
|
28
|
13
|
200
|
40
|
14
|
440
|
88
|
15
|
475
|
99
|
16
|
500
|
100
|
This simulation shows that there are 18 % chances that the project gets completed in 9 days, 40 % chances of it being completed in 13 days and so on.
Sensitivity Analysis:
Sensitivity analysis is a technique used to show the effects of changing one or more variables on an outcome. For example, in project management, it may be used to determine the change in ROI if the output of a certain variable process is changed.
In project management, the purpose of sensitivity analysis is to:
- Help identify the key variables which influence the project cost and benefit streams
- Investigate the consequences of likely adverse changes in these key variables
- Assess whether project decisions are likely to be affected by such changes
- Identify actions that could mitigate probable adverse effects on the project