 # Quantitative Risk Analysis

15 March 2019 Off

# Quantitative Risk Analysis

This  process  is  the  second  step  in  risk  assessment.  The  purpose  of  this  process is  to numerically evaluate risks that need additional analysis after performing qualitative analysis. This evaluation is objective and the result is numerical but it depends on calculations and it lacks of human sense.   It means that these results tend to be the most accurate if revised by human sense after running the calculations.

## 1 –  Probability Distribution

Quantitative analysis utilizes computer software to model probability distribution. This can be depicted graphically using continuous distribution or discrete distribution.

Figure-1 Shows some types of continuous distribution. Figure-1 : Continuous distribution

In discrete distribution, data can be represented as bars. Each bar represents a possible outcome and each outcome is assigned a probability. Figure-2 Shows a model of discrete distribution. Figure-2 : Discrete Distribution

The  difference  between  continuous  and  discrete  distribution  can  be following comparison in table-1. Table-1 : Comparison between continuous and discrete distributions

## 2- Quantitative and Modeling Techniques

Quantitative analysis utilizes simulation and modeling techniques such as sensitivity analysis, expected monetary value, and Monte Carlo.

### 2-1 Sensitivity Analysis

This technique shows the range of outcomes for a risk by changing only one element each time (what-if analysis) and measuring its impact. This is being done several times to evaluate the range of outcomes. It’s possible to change multiple elements by designing of experiment (DOE). Table-2 shows an example on sensitivity analysis. A common way to display these results is tornado diagram as shown in figure-3. Table-2 : Sensitivity Analysis ### 2 -2 Expected Monetary Value

This technique calculates outcomes considering both probability and impact. It’s used to  make  decisions after  gathering  data  on  two  or  more  alternatives.  It  depends basically  on  the  impact  of  each  decision and  the  probability  that  it  may  happen.

Multiplying  impact by  its  probability  will  result  the expected monetary value.

Comparing  these values  will facilitate  taking  decision.  This  technique  uses the decision tree analysis as a  graphical method to represent alternatives and their impact/probability. Figure-4 shows an example of decision tree analysis. Figure-4  Decision Tree Analysis

### 2-3  Simulation & Modeling

This technique utilizes Monte Carlo technique as a simulation tool. Project model is computed several times with the input of cost estimates or activity durations that are being  choose  at  random  for  each  time  from  the  probability  distributions  of  these variables. A  chart of total cost or total duration is then drawn. Figure-5 shows an example of this chart. Figure-5 : Monte Carlo Simulation for Project Cost

## 3 –  Risk Register Update

At this point, the risk register will be updated with the followings
1- Probabilistic analysis of the project
2- Quantified probability of meeting project objectives
3- Prioritized list of quantified risks
4- Overall project risk (Risk Exposure)