×
iFour Logo

Risk assessment through quantitative analysis

iFour Team - April 28, 2017

Listening is fun too.

Straighten your back and cherish with coffee - PLAY !

Sustainable

What is quantitative analysis?

While analyzing risks in software development companies, we use two approaches-Quantitative or Qualitative. Qualitative analysis is the technique wherein we use proper mathematical models. Whereas, qualitative analysis is done by assessing the quality of work which does not contain any sort of measurement. Qualitative analysis involves the analysis of statistical and mathematical data with numerical results.

What is quantitative analysis in risk management?

As quantitative risk analysis deals with a numerical solution, risks are assigned numbers on which the assessments are made. Usually this analysis is made on the basis of cost or time. That is, risk is measured in terms of the loss in cost or time.

Necessity of Quantitative risk assessment

Ideally, a quantitative risk assessment is a compulsion when the policies involved in a project demands it. Apart from all this, there are situations where quantitative techniques become essential. These situations vary from project to project.

Some cases where quantitative analysis is a must for proper risk assessment are:

 
  • When single point estimates are used for project planning.
  • Understanding the maturity in risk management process of the project undertaken.
  • Deciding between two or more Project approaches.

Quantitative analysis also aids to characterize organizations and leaders to analyze project risk. The ultimate aim of quantitative analysis is to minimize the annual loss and can be used to:

  • Identify the threats that can be reduced to optimize costs.
  • Prevent the loss of assets.
  • Optimize the ROI.

Difference between Qualitative and quantitative analysis

  • Qualitative analysis considers all the risks in the identifying process while quantitative risk analysis process considers those processes which are marked for further analysis in the qualitative risk analysis.
  • Qualitative analysis is done by creating a probability/impact matrix whereas quantitative risk assessment techniques predict the outcomes in terms of money or time based on combined effects of risk.
  • All projects use qualitative risk analysis but not all projects use quantitative analysis.
  • Qualitative analysis does not use mathematical models to analyze risks whereas quantitative models do use mathematical models to analyze risks.

Various techniques used in Quantitative risk analysis

The various techniques used in quantitative risk analysis are:

  • Estimated monetary value
  • Monte Carlo Simulation
  • Sensitivity analysis

Estimated monetary value (EMV):

According to PMBOK, this is a process used to identify, analyze and respond to project risk.

EMV is used to analyze as to how much risk can be tolerated by the stakeholders and how much fallback plans do they want. Stakeholders often want to know the monetary value of the risk occurring. For such situations EMV is the best technique.

EMV= Probability*Impact

Example:

 Suppose there is a project where there are the following risk probabilities:

  • 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.

Looking to Hire the Best Web Development Company? Contact Now

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

Conclusion

Irrespective of it being a quantitative or qualitative process, risk assessment, in software outsourcing companies , is the most important phase of project management. The various ways of Quantitative assessment serves assured ways of risk management after qualitative assessment is made. So, the importance of qualitative analysis is very high as it gives exact values for risk assessment.

Risk assessment through quantitative analysis Table of Content 1. What is quantitative analysis? 2.What is quantitative analysis in risk management? 3.Necessity of quantitative risk assessment 4.Difference between qualitative and quantitative analysis 5.Various techniques used in quantitative risk analysis 5.1. Estimated monetary value (EMV) 5.2. Example 5.3. Monte carlo simulation 5.4. Sensitivity analysis 6. Conclusion What is quantitative analysis? While analyzing risks in software development companies, we use two approaches-Quantitative or Qualitative. Qualitative analysis is the technique wherein we use proper mathematical models. Whereas, qualitative analysis is done by assessing the quality of work which does not contain any sort of measurement. Qualitative analysis involves the analysis of statistical and mathematical data with numerical results. What is quantitative analysis in risk management? As quantitative risk analysis deals with a numerical solution, risks are assigned numbers on which the assessments are made. Usually this analysis is made on the basis of cost or time. That is, risk is measured in terms of the loss in cost or time. Necessity of Quantitative risk assessment Ideally, a quantitative risk assessment is a compulsion when the policies involved in a project demands it. Apart from all this, there are situations where quantitative techniques become essential. These situations vary from project to project. Some cases where quantitative analysis is a must for proper risk assessment are:   When single point estimates are used for project planning. Understanding the maturity in risk management process of the project undertaken. Deciding between two or more Project approaches. Read More: Learning From Our Mistakes With Causal Analysis And Resolution Quantitative analysis also aids to characterize organizations and leaders to analyze project risk. The ultimate aim of quantitative analysis is to minimize the annual loss and can be used to: Identify the threats that can be reduced to optimize costs. Prevent the loss of assets. Optimize the ROI. Difference between Qualitative and quantitative analysis Qualitative analysis considers all the risks in the identifying process while quantitative risk analysis process considers those processes which are marked for further analysis in the qualitative risk analysis. Qualitative analysis is done by creating a probability/impact matrix whereas quantitative risk assessment techniques predict the outcomes in terms of money or time based on combined effects of risk. All projects use qualitative risk analysis but not all projects use quantitative analysis. Qualitative analysis does not use mathematical models to analyze risks whereas quantitative models do use mathematical models to analyze risks. Various techniques used in Quantitative risk analysis The various techniques used in quantitative risk analysis are: Estimated monetary value Monte Carlo Simulation Sensitivity analysis Estimated monetary value (EMV): According to PMBOK, this is a process used to identify, analyze and respond to project risk. EMV is used to analyze as to how much risk can be tolerated by the stakeholders and how much fallback plans do they want. Stakeholders often want to know the monetary value of the risk occurring. For such situations EMV is the best technique. EMV= Probability*Impact Example:  Suppose there is a project where there are the following risk probabilities: 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. Looking to Hire the Best Web Development Company? Contact Now See here 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 Conclusion Irrespective of it being a quantitative or qualitative process, risk assessment, in software outsourcing companies , is the most important phase of project management. The various ways of Quantitative assessment serves assured ways of risk management after qualitative assessment is made. So, the importance of qualitative analysis is very high as it gives exact values for risk assessment.

Categories

Ensure your sustainable growth with our team

Talk to our experts
Sustainable
Sustainable
 

Blog Our insights

A comprehensive guide on how to hire Frontend developers in 2022
A comprehensive guide on how to hire Frontend developers in 2022

Table of Content 1.What frontend stack to choose for your project? 2.Where to find the right frontend developers? 3.Hiring frontend developers: a step-by-step guide 4.Technical...

Read More
Why construction software is essential for your construction business?
Why construction software is essential for your construction business?

Table of Content 1. Increased profits with reduced risks 2. Powerful integration with accounting software 3. Streamlines key operations with the power of automation 4....

Read More
Why do .NET developers choose Angular over React?
Why do .NET developers choose Angular over React?

Table of Content 1. Angular offers several built-in libraries similar to .NET 2. Angular's syntax is much closer to C# 3. Angular uses TypeScript, a strongly-typed...

Read More