Section Summary – Quantitative Risk Analysis

In this section, we had taken a detailed look at the Quantitative Risk Analysis process. In this chapter, we are going to Summarize whatever we have learnt so far with respect to Quantitative Risk Analysis

Before we begin, let me warn you that, this chapter is going to be one of the longer summary chapters as we have covered a lot of ground in the past few chapters about Quantitative Analysis.

• Quantitative Analysis is usually performed on a sub-set of the identified risks that are of high impact/priority
• While Qualitative Analysis is quick and cost effective, Quantitative analysis can be more time consuming and costly.
• In smaller projects, we may even opt to ignore this step whereas for large projects, Quantitative analysis is extremely valuable
• Quantitative Analysis is repetitive and may occur multiple times throughout the life of the Project
• The Inputs used in Quantitative Analysis are:

o Risk Register
o Risk Management Plan
o Schedule Management Plan
o Cost Management Plan and
o Organizational Process Assets

• The Perform Quantitative Risk Analysis activity has two groups of tools & techniques. They are:

o Data Gathering & Representation Techniques

 Interviewing
 Probability Distributions

• Continuous Distributions
• Discrete Distributions
• Uniform Distributions
• Etc

o Quantitative Risk Analysis & Modeling Techniques

 Sensitivity Analysis
 Expected Monetary Value Analysis
 Modeling & Simulation

• Interviewing in general, is used to quantify the probability and impact that risks may have on our projects objectives. The information gathered during these interviews would be dependent on the type of probability distributions that we are looking to use. Typically, we will conduct such interviews with SME’s (Subject Matter Experts) to get their take on the risk and its impact
• 3 point estimate = (O + 4 * ML + P) / 6 where O is the optimistic estimate, ML is the most likely estimate and P is pessimistic estimate
• Standard Deviation (SD) = P – O / 6. Here again – P is the pessimistic and O is the optimistic estimate
• A Probability Distribution graphically displays data and represents both the probability as well as time/cost elements. So, by seeing a distribution, we can not only get an understanding of the probability but also the impact it will have on other elements like time or cost
• Continuous Distributions are typically used for cost, time and quality metrics
• The values shown in a continuous distribution are infinitely divisible (time, mass, distance etc.)
• Actually speaking, there are many different types of continuous distributions. For the RMP Exam we will need to know about:

1. Beta
2. Triangular
3. Uniform
4. Normal
5. Lognormal and
6. Cumulative

• Discrete Distributions are used to show uncertain events where the probability of occurrence can be calculated accurately and are based on a whole number
• There are several types of discrete distributions, like:

a. Discrete Uniform
b. Binomial
c. Hypergeometric
d. Etc…

• The purpose of sensitivity analysis is to determine which risks have the highest potential impact on project objectives. Our goal is to single out those important risks so that we can respond in a more effective manner
• During Sensitivity Analysis, we will be using all the quantitative information gathered for the risks up until now, along with other information from the Project Management Plan. Remember that these risks are expected to have a significant impact on the project objectives like cost, time, quality etc. So, the corresponding plans from the Project Management Plan too may be consulted when the analysis is performed
• Tornado Diagrams are a very common way of displaying results of sensitivity analysis. They compare the importance of variables that have a higher degree of uncertainty to the more stable variables
• Expected Monetary Value Analysis calculates the average outcome of future scenarios that may or may not occur
• EMV Analysis calculates the Monetary Value of the Impact of this scenario if it occurs in future – Today
• Formula for Expected Monetary Value: EMV = Probability * Impact
• Decision Tree Analysis is used to make decisions based on the risks that could impact us in the various possible scenarios we may encounter in future. It calculates the Expected Future Value of an activity based on the current impact & probability of all risks
• Modeling and Simulation – Converts uncertainties into potential impacts on Project Objectives that are specified at a detailed level
• In simpler terms – We are going to try to understand the potential impact risks will have from the whole project’s perspective
• The most commonly used technique under the Modeling & Simulation category is the “Monte Carlo Technique”. It is performed using Software to perform iterative Simulations

Now that we have successfully completed our Quantitative Analysis, the next step is to update the Risk Register with all our findings just like we did after finishing Qualitative Analysis. Updates to the Risk Register will be our next section.

Prev: Modeling and Simulation

Next: Introduction - Updates to Risk Register after Quantitative Analysis

Tools and Techniques used in Quantitative Risk Analysis - An Introduction

In the previous chapter, we took a detailed look at the Inputs that we will be using in our Quantitative Risk Analysis activity. So, just like any other process, when we have a bunch of inputs, we apply some tools and techniques on them in order to accomplish the task that we started. 

The Perform Quantitative Risk Analysis activity has two groups of tools & techniques. They are: 

a. Data Gathering & Representation Techniques 
b. Quantitative Risk Analysis & Modeling Techniques 

An important consideration here is that, Expert Judgment is used throughout quantitative risk analysis. It validates all the data and techniques used by this process. It includes information gathered from both internal and external Subject Matter Experts (SMEs). Since Expert Judgment essentially uses the manager’s expertise, we won’t be considering that as an explicit tool or technique. 

Data Gathering & Representation Techniques: 

The title of this group of techniques is self-explanatory. It clearly explains what we are planning to do with these tools or techniques. We will be gathering all the data that we will require or rather all the data that will help us with our quantitative analysis activity. These include: 

a. Interviewing & 
b. Probability Distributions 

Probability Distributions in itself include: 
1. Continuous Distributions
2. Discrete Distributions 
3. Uniform Distributions and
4. Other frequently used distributions like Beta or Triangular distributions. 

Quantitative Risk Analysis & Modeling techniques

In this group of techniques, we will be working on the data gathered using the previous set of tools & techniques. The purpose here is to get a clearer picture of the impact that certain risks will have on our project and its competing objectives. These include: 

1. Sensitivity Analysis
2. Expected Monetary Value Analysis 
3. Modeling & Simulation 

I have just listed down these tools/techniques above and haven’t said anything more about them. This is because, the following few chapters will touch upon each of these in great detail. In the following chapters we will learn the following about each of these tools and techniques: 

a. The purpose of each technique
b. What it is used for
c. What information is needed and
d. How results are displayed 

Trivia: 

A very common question that almost every new project manager has is “What Technique to use and When/Where?” This is very hard to answer considering the fact that each and every project could have its own complexities. Different projects may have needs/risks. For smaller projects with little or no risks, a majority of the analysis activities can be skipped while for large & complex projects, we may have to perform as much analysis as possible to ensure that we are well prepared for any risk that may affect our project. 

At the end of Quantitative Analysis, if you are unable to numerically quantify or rate a risk, then it means that additional analysis is required. It would be a good idea to go back and re-analyze those risks to ensure that we have a clear-cut idea of what we are dealing with. 

Some Last Words: 

Quantitative Risk Analysis is a very complex topic. In this blog, we will be covering only what is required for you to learn/understand from the PMI RMP Exam perspective. This is not an exhaustive resource on quantitative risk analysis. If you want to learn more about quantitative analysis, you can try to pick up one of the many books that specialize in Risk Analysis. 

Prev: Inputs Used in Quantitative Risk Analysis

Next: Interviewing