Inputs Used in the Plan Risk Responses Process

In the previous chapter, we took a high level look at the Plan Risk Responses process. Just like any other process, this process too takes a set of inputs, applies some tools and techniques and generates an output which in this case will be the “Responses to the Identified Risks”.

So, what are the items we need in order to formulate responses to the risks we have identified so far? They would be:

1. Risk Register
2. Risk Management Plan

Risk Register

This one is a no-brainer, isn’t it? What sort of response will be created if you do not know what the risk is, or the other details surrounding the risk? So, without the Risk Register, this whole activity will be plain & simple – A waste of time.

The Risk Register as you must remember by now contains he most up-to-date information about all of the risks in our project. After it gets created in the “Identify Risks” process, it is used as an input to every risk management activity that happens afterwards and gets updated in each of those processes. You may want to revisit the chapter titled Contents of the Risk Register to revise and remember what the Risk Register contains.

So, coming back to topic, by the time we reach the Plan Risk Responses Process perspective, the following items will be present in our Risk Register:

1. List of identified risks
2. Root causes of risks
3. List of potential responses
4. Risk owners
5. Symptoms and warning signs
6. Relative rating and/or priority list of project risks
7. List of risks chosen for additional analysis and/or response
8. Trends that emerged as a result of risk analysis
9. A watchlist of low priority risks

As the risk analysis processes are repeated, the risk register will continue to get updated and will start containing more and more information. So, assuming that you have reached the plan risk responses process in the first iteration, it must at a bare minimum contain the above mentioned items in order for us to identify proper responses to the risks that have been identified so far.

Risk Management Plan

Remember the chapter titled Contents of the Risk Management Plan that was covered sometime back? The risk management plan is going to contain all the information that we would require to perform the risk management activities in our project. From the plan risk responses process perspective; we will use the following from the risk management plan:

a. Risk management methodology
b. Roles and responsibilities
c. Budgeting information
d. Timing information
e. Risk categories
f. Definition of probability and impact
g. Probability and impact matrix
h. Stakeholder risk tolerance
i. Reporting formats and
j. Tracking information

The crucial element that we will require in order to formulate appropriate responses for the risks is the “Stakeholder Risk Tolerance” which basically decides how we respond to each risk. Let us say, the customer for whom you are executing the project is extremely averse to risk and is not willing to take any, all your responses will be aimed at avoiding or eliminating the risks. If you propose a scenario where a risk could cause a slight damage, the customer may get upset and case issues.

So, as a good project manager it is our duty to ensure that the responses we formulate take into account the risk tolerance and thresholds of all our important customers.

Prev: Introduction - Plan Risk Responses

Next: Introduction to Tools & Techniques in Plan Risk Responses

Introduction - Plan Risk Responses

In the previous sections, we learnt how to analyze all the risks that we have identified in our project so far. So, what is the next logical step now?

You know what the risk is, its probability, and its impact including the monetary impact and hence are in a great position to decide how to handle the risk appropriately, isn’t it? So, the next logic step would be to decide – what to do in response to each of those risks. That is what we are going to do in this section which is titled “Plan Risk Responses”

Purpose of Plan Risk Responses:

The purpose of this process is to develop options and actions that will reduce the threat of negative risks and/or increase the opportunity of the positive risks.

Simply put, you just decide what you are going to do for each of the risk. Plus, an owner is assigned for each risk so that he/she can track the risk appropriately.

Trivia:

Assigning a risk owner is better than a single person keeping track of an unmanageable quantum of risks. In real life, if you perform Risk Identification for a large Project the list will run into numerous risks. So, if you ask a single person to track all of them, it is quite possible that some of the risks maybe missed. So, its better we assign a manageable number of risks to different people and let them stay on top of those risks.

Once the possible responses for each of the risks are formulated, we typically must ask ourselves the following questions before finalizing the response for each risk:

1. Is the response appropriate in comparison to the significance of the risk?
2. Is the response cost effective in meeting all the challenges posed by the risk?
3. Is the response realistic?
4. Have all parties (stakeholders) agreed to the response?
5. Has an owner responsible for the risk been assigned?
6. Is the response planned for execution at the right time?
7. Has the best response been selected from the available options?

If you can answer yes for all of at least a majority of these questions, then we can be pretty sure that the response we have selected would be good.

A point to remember here is that, the responses we develop and when we actually implement those responses depends on various factors including the probability and impact of the risk. Also, in some smaller projects, quantitative risk analysis may or may not happen. In such cases, the “Plan Risk Responses” activity happens right after Qualitative Analysis. However, it would be a good idea to do Quantitative Analysis before planning responses.

This whole Plan Risk Responses activity can be summarized using the image below:




We take a bunch of inputs which include your risks, their details, the analysis you have performed so far and any other relevant information, we apply some response strategy for each of those risks and then formulate the Risk Response Plan for the project.

Remember that there are different strategies for different risks. For ex: If a risk could cause losses (A Negative Risk) we may want to find out options to avoid it whereas if a risk could cause profits or benefits (A Positive Risk also known as an Opportunity) then we may want to capitalize on it to ensure that the benefits are achieved.

Don’t worry just yet about the strategies because there will be whole chapters dedicated to them shortly…

Prev: Section Summary - Updates to Risk Register after Quantitative Analysis

Next: Inputs used in the Plan Risk Responses process

Section Summary – Updates to Risk Register after Quantitative Analysis

In this section we covered all the updates that happen to the Risk Register after we complete the Quantitative Analysis process. Let us quickly summarize what we have learnt in this section before moving on to the next topic… 

• There are four updates that happen to the Risk Register after we complete Quantitative Analysis. They are: 

o Probabilistic Analysis of the Project
o Probability of meeting Project cost and time objectives
o Prioritized list of quantified risks 
o Trends in quantitative risk analysis results 

• The focus or purpose of “Probabilistic Analysis of the Project” is to identify the probabilities relating to the whole project. 
• How probable are we to achieve our project schedule? Or How probable are we to complete the project within budget? Are some questions that get answered at the end of quantitative analysis.  
• The probabilities are typically displayed as a Probability distribution
• Risks in the Risk Register are ordered based on either of the following: 

o Risks that pose the greatest threat or Opportunities to the Project 
o Risks that are most likely to impact the Project’s Critical Path
o Risks that require the largest contingency reserves

• Trends emerge whenever we perform an activity multiple times over the project’s lifecycle
• These trends can be used to create better and more efficient risk responses 

Prev: Risk Register Contents - After Quantitative Analysis

Next: Introduction - Plan Risk Responses

Risk Register Contents - After Quantitative Risk Analysis

From the Exam syllabus perspective, we have already covered all that we needed to about the Quantitative Risk Analysis process. But, I would like us to take a moment, go back and revisit what we have done so far with the Risk Register. During the course of the sections we have covered so far, we have made multiple updates to the Risk Register. So now, we are going to go back and summarize the current status of the Risk Register… 

The Risk Register Contents – As of Now 

Up until now, we have Identified Risks, Qualitatively and Quantitatively analyzed our risks. So, as of now, the Risk Register is loaded with tons of information that we have captured so far. 

Trivia: 

If you have a copy of the Risk Register Update Cycle image, it will be very useful now. Remember the Risk Register Update Cycle we covered in the chapter “Contents of the Risk Register”

Up until now, we have done the following updates to the Risk Register: 


During Risk Identification: 

1. List of Identified Risks
2. List of Potential Responses
3. Root Causes of Risks
4. Updated Risk Categories 

During Qualitative Risk Analysis: 

1. Priority list of Project Risks
2. Risks grouped by Categories
3. List of Risks that require a response in the near term
4. List of Risks for Additional analysis and response
5. Watchlist of low priority Risks
6. Trends in Qualitative Analysis Results 

During Quantitative Risk Analysis: 

1. Probabilistic Analysis of the Project
2. Probability of Achieving Time & Cost objectives
3. Prioritized list of quantified risks
4. Trends in Quantitative Analysis Results 


If you cannot remember when and how a particular update happened, pause here, go back, read that chapter again before you proceed further. 

Prev: Trends in Quantitative Risk Analysis

Next: Section Summary

Trends in Quantitative Risk Analysis

In the last few chapters, we have covered 3 of the updates that happen to the Risk Register, at the end of Quantitative Risk Analysis. The last and final update is, capturing of the “Trends in Quantitative Risk Analysis”. 

This kind of capturing trends is something we did at the end of qualitative analysis too, didn’t we? 

Remember that several of the risk management activities & processes are iterative which means, they occur multiple times throughout the Project esp. Quantitative Risk Analysis. 

Whenever a process/activity is repeated, a trend usually emerges and when a trend emerges, it is important that we document them properly. 

When trends emerge, they may guide the Risk Management team in the following ways: 

• In developing more efficient responses
• Lead to Additional Analysis
• Lead to Risk Reassessment 
• Identify risks that pose the greatest threat to the Project
• Indicate the need for more or less risk management action

Let me repeat that, the most important use/purpose of gathering these trends is that, it helps us in developing more efficient and effective responses. We can even create a quantitative risk analysis report to document these trends. This report could be a part of the risk register or a separate entity altogether. But, when created, it must always showcase and highlight the valuable insights we learnt as a result of the Quantitative risk analysis process. 

Prev: Prioritized List of Quantified Risks

Next: Risk Register Contents - As of Now

Prioritized List of Quantified Risks

Do you remember that we had prioritized lists at the end of Qualitative Analysis and then picked up the ones at the top of the list and/or ones marked as “Needs more analysis” for Quantitative Analysis? 

If you don’t remember, I strongly suggest you go back, read the qualitative analysis, its output/updates to the Risk Register before proceeding further.

The purpose of this update/list is similar to the one we actually did at the end of Qualitative Analysis. At the end of Quantitative Analysis, we will try to arrange risks by either of the following: 

a. Risks that pose the greatest threat or Opportunities to the Project 
b. Risks that are most likely to impact the Project’s Critical Path
c. Risks that require the largest contingency reserves

Remember tornado Diagrams that we learnt about a few chapters ago? If we had created Tornado diagrams for our risks, this task of identifying those at the top of the list becomes pretty easy. 

Some important points to remember while doing this activity: 

a. Risks could be listed separately – we could have multiple lists (for ex: grouped by project objectives) 
b. All the information that went into our analysis must be properly documented and archived including details like who participated in the analysis, what info was used and how etc 
c. It would be a good idea to create Tornado Diagrams. It will help us identify the higher priority risks 

Do you remember that during our initial chapters on the Risk Management Framework, we had learnt the fact that a numerical rating can be assigned to our risks as a part of the Quantitative Risk Analysis process? The basis on which this rating was arrived at must be documented too. 

Trivia: 

If your Organization does not have established processes/framework/templates for risk management, then you will have a lot more work to do when compared to organizations that have well established Risk Management processes. 

You may be wondering, why must I take the pain of doing all the documentation, why can I just do what is required for my current project and let the other PM’s worry about their projects? 

PMI expects us not only to follow sound Risk Management practices, but also expects us to create artifacts and share our knowledge so that, people who may take up project similar to ours need not reinvent the wheel that you have already invented. 

Prev: Probabilistic Analysis of the Project

Next: Trends in Quantitative Analysis

Probabilistic Analysis of the Project

In the previous section we took a detailed look at the Quantitative Risk Analysis process, its inputs and tools. As we saw in the previous chapter, the output of the process is a series of updates to the Risk Register and the first update in the list is “Probabilistic Analysis of the Project” which is the topic of our discussion in this chapter. 

The focus or purpose of this update is to identify the Probabilities relating to the Project as a whole. 

How probable are we to achieve our Project Schedule? How probable are we to complete the project within budget? These are some of the probabilities that we aim at identifying and updating the Risk Register during this step. Once our quantitative analysis is complete, we should be in a position to show possible completion dates and costs that are associated with our Project, along with the confidence levels with which we predict the aforementioned information. 

In all practical situations, by the time you start your quantitative analysis, your projects schedule would already be ready or would in its final stages of finalization. So, based on the risks that could affect our project, we will try to figure out if our schedule is realistic. We try to figure out if it takes into account the cost limitations as well as any other risk that could affect it. 

The biggest problem in most failed project is either Unrealistic Schedule or Unrealistic Budget. Many PMs miss out this part and pay a dear price towards the end of the project, where they are forced to see their project sink. The whole purpose of risk management is to minimize this kind of a scenario. 

The PMBOK guide says that, the result of this kind of analysis is typically displayed as a Cumulative Distribution. These distributions are then used along with Stakeholder Risk Tolerances to allow for the quantification of Cost and Contingency Reserves. As with any value that is calculated by us, these reserves must be appropriate and realistic. 

Trivia: 

Do you remember where these Contingency Reserves are developed??? 

If you haven’t done your PMP Certification already you may not know the answer. But, nonetheless, if you are already PMP Certified, do you remember??? 

Plan Risk Responses is the process where we calculate these contingency reserves.

Contingency Reserves are extremely important for any project because, whenever a project overruns its budget or schedule, the Project Manager may need to take corrective action and this would involve some additional unplanned expenses (both time and effort) which may not be possible if appropriate reserves aren’t available. The presence of proper contingency reserves can help both the Project and the Project Manager try to achieve the project goals in case of an unfortunate event that threw the whole project off-track. 

In General – After Quantitative Analysis, we must remember that: 

• Experts are an important source of Risk Related information 
• Expert Judgment is important in quantifying risks 
• Often information from experts is expressed as a probability distribution and it forms the basis to the whole risk analysis process 
• It is always a good idea to document all the information obtained from experts because, everyone must know how the estimates and values were derived. This adds a lot of credibility to the information
• Three Point Estimates are frequently used to arrive at overall project estimates
• Remember three point estimates? We use the Optimistic, Most Likely and Pessimistic values to arrive at an estimate. Here these 3 numbers are given to us by experts 
• Majority of the output information is displayed using probability distributions
• Based on the distribution we intend on using, we must determine and prepare the information that is required to come up with that report.
• All information we used/considered must be properly documented in the Risk Register 

Probability of Achieving Cost and Time Objectives

In this process, we are going to quantify the probability of achieving the cost and time objectives of our project.

This comes about by using all of the tools and techniques that are used by the Quantitative Analysis process. It requires a thorough understanding of the current project’s objectives as well as a thorough knowledge/understanding of all the risks. Our focus here is to find out the probability of achieving our project’s objectives under the existing plan – for both the Cost and the Schedule

The results of this activity too are displayed as a probability distribution. It is simply a representation of the probability of meeting the project’s cost and time objectives.

Ex: let us say the current project budget is USD 5 million and based on quantitative risk analysis, it is determined that there is a 65% probability that our project will be within budget. If the budget were USD 7.5 million then, there will be a 80% probability that our project will be within budget and if the budget were USD 10 million, then there will be a 100% probability that our project will be within budget.

In all practical scenarios, project managers don’t have the luxury of determining the best possible budget (10 million here) and give 100% accuracy on cost estimates. They are probably given a number that would result in a very low probability of meeting the budget and are asked to work around it. So, in real life, don’t expect your sponsor to say, ok buddy, I need 100% probability of meeting the budget, so here you take this 10 mil and work with it. Most probably he will say “5 million is the max you will get and if you finish within 4.5 you will get a small incentive and if you exceed 5.5 million start uploading your resume in job sites!!!”

Prev: Introduction - Updates to Risk Register after Quantitative Analysis

Next: Prioritized List of Quantified Risks

Introduction – Updates to Risk Register – After Quantitative Analysis

We have covered the Quantitative Risk Analysis topic in great detail in the previous section. As with any other activity in Risk Management, the output of this activity too would result in updates to the “Risk Register”.

To recap, the Risk Register is going to contain the most up-to-date information regarding all of the risks that have been identified for our Project. As we progress through each stage in Risk Management, we update the Risk Register so that it remains as the one comprehensive document that contains all the details we need in order to perform our Risk Management Activities.

So far, we have done the following – In Order:
1. Identified Risks
2. Updated the Risk Register with the preliminary risk information identified in step 1
3. Performed Qualitative Risk Analysis
4. Updated the Risk Register with the output of the analysis performed in step 3
5. Performed Quantitative Risk Analysis
6. WE ARE HERE NOW!!!

Common Sense would tell us that the next logic step here would be “Do Updates to the Risk Register with output of the Analysis performed in step 5” and this is exactly what we are going to do now.

Remember that, we did not perform quantitative analysis on all the risks we identified. Instead, we selected a sub-set of high priority (probability/impact) risks and have analyzed them further. So, as a result of this step, the updates to the risk register would affect only those risks that we selected…

Quantitative Risk Analysis typically results in the following updates to the Risk Register:



In the following chapters, we will be covering those updates one by one…

Prev: Section Summary - Quantitative Risk Analysis

Next: Probabilistic Analysis of the Project

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

Modeling and Simulation

In the previous chapters we had covered a lot of tools and techniques that are used in Quantitative Risk Analysis. The last one in the list is “Modeling and Simulation” which is part of the Quantitative Analysis and modeling Techniques sub group.

Modeling and Simulation – Converts uncertainties into potential impacts on Project Objectives that are specified at a detailed level.

The definition may sound complicated. But, in simpler terms – We are going to try to understand the potential impact risks will have from the whole project’s perspective. Simulations provide a way to look at a risk at a specific point in time (In future, of course) in the project and help us to understand how that particular risk would affect our project objectives.

It allows a more detailed representation/understanding of the Risk because, it generates many possible scenarios so that we can monitor and examine the outcome. When we analyze the scenario and the outcome, we will get a good idea of what combination of values resulted in what outcome and this information can be extremely valuable while trying to come up with a response for the Risk under question.

Uses of Modeling & Simulation:

The following are some uses of Modeling and Simulation techniques:

1. Determine which activities are likely to be on the Critical Path. Remember the Critical Path that we try to find out once we create the Project’s Schedule using the Develop Project Schedule process?
2. Calculate the Probable Cost of the Project
3. Calculate the Probable Value of the Project, especially to the Customer
4. To perform Cost Benefit Analysis while deciding on a potential response. For ex: If you want to Crash your project schedule to shorten the overall schedule or to make up for lost time, you can use simulation to evaluate the probability of success
5. Calculate probable time to complete the Project or a Sequence of Activities.

There are many benefits of using this technique. But, the PMBOK goes a step further and explicitly states that this technique has much lesser chances of misuse than the EMV Technique.

Monte Carlo Analysis/Technique

The most commonly used technique under the Modeling & Simulation category is the “Monte Carlo Technique”. It is performed using Software to perform iterative Simulations. We use software to do this because of the complexities involved in the calculation and more importantly the iterative nature of the analysis.

The input values are chosen at random from a possible set of values (A Probability Distribution of values like Cost or Schedule parameters) with the idea of simulating possible/potential outcomes.

For ex: Let us say, we want to analyze a Project Network Diagram that we created during the Develop Project Schedule process. This analysis simulates many scenarios, determining all the existing possibilities. It can help us identify the Critical Path, the impact delays in one activity could have on the others or the overall project schedule, how much chances we have on completing the project work within the planned time etc. In short, it will help us identify the overall Risk we are carrying with us with respect to the Projects Schedule.

The output/result is typically displayed as a Probability Distribution Chart as well.

As suggested in the previous section, Monte Carlo Analysis is typically done to identify Cost or Schedule Risk. For cost risk analysis we will use Cost Estimates as input. Whereas, for schedule risk analysis, we will use network diagrams and duration estimates as inputs. Based on the outcomes we can determine the likelihood of reaching our target (Finishing our project within cost and on schedule)

Trivia:

Just before starting on Monte Carlo Technique, I referred to a point about PMBOK suggesting that Modeling and Simulation has lower chances of misuse when compared to EMV. Did you pause and think why? If you totally missed that statement in a hurry to finish this chapter, please go back and read it again and then think why they would possibly make such a statement…

Steps in Monte Carlo Analysis:

The following are the steps in a typical Monte Carlo Analysis:

Step 1: plug in the input variables like Schedule & cost variables for each work package (From the Work Breakdown Structure) or the specific Risk that you are evaluating

Step 2: Use the Software that has been specifically created/designed for this purpose and simulate the potential outcomes repeatedly

Step 3: The output and results tell us the impact each variable has on our project as a whole. Store the Results.

Step 4: The results are analyzed using EMV or Probability Distributions

Step 5: The results of the Analysis is displayed using a Cumulative Distribution. Remember Cumulative Distributions from a few chapters ago?

Trivia:

Did you figure out why PMBOK made such a bold statement? If you haven’t re-read either the section or thought through the reason, spend some time to figure it out. It is pretty straight forward.

In EMV Analysis, We (Humans) actually do the analysis and hence based on our individual selves, we could easily fudge with the analysis or the outcome of the analysis. Whereas, in Monte Carlo analysis, the computer is doing the simulation and hence chances of misuse are considerably reduced. You may argue that, you could fudge the inputs and have a negative effect on the analysis, but even if you do that, the effect you would have on the outcome of the analysis will not be as severe as it would be if you intentionally performed incorrect EMV analysis.

Prev: Decision Tree Analysis

Next: Section Summary

Decision Tree Analysis

In the previous chapter, we took a look at Expected Monetary Value or EMV Analysis. The Decision Tree Analysis is another tool/technique that we use in Quantitative Risk Analysis that directly uses this EMV Analysis. In this chapter, we are going to take a detailed look at Decision Tree Analysis

Decision Tree Analysis

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.

Decision Tree Analysis uses a Decision Tree Diagram. In the tree, we start at the starting point and go through the tree and take a decision based on the EMV for the Alternatives that are available for us. It shows a sequence of inter-related decisions and their respective EMVs so that you can take a good and properly thought-out decision. Decision Tree Analysis is used typically to take decisions dealing with Time or Cost.

Let us now take a look at some examples to understand Decision Tree Analysis:

Example 1:

Let us say, we are given the task of deciding between Vendor A and Vendor B. Vendor A has a Success Probability of 55% and an Impact of $ 70,000 while there is no impact on Failure. Similarly Vendor B has a 75% probability of Success and has an impact of $ 55,000 and he too has no impact on Failure. Based on this information, how would you choose the Vendor?

The simple Answer would be – Use Decision Tree Analysis. So, based on this question, if I were to create a Decision Tree, it would look like below:



So, here:

EMV for Vendor A: = 70000 * 55% = 38,500

EMV for Vendor B = 55000 * 75% = 41,250



Now, you know the EMV for each vendor. So, the wiser choice would be to choose Vendor B because the Expected Monetary Value of choosing Vendor B is greater than Vendor A.

Trivia:

If we had just considered the Impact, Vendor A would look like a better choice because he has a higher impact. But, he has a lower probability. So, Vendor B, even though has a lower impact, is selected because the combination of both probability and impact makes him the better choice.

Example 2:

Example 1 was an all positive scenario where there is no Impact for failure. What would you do in an all Negative Scenario?? Look at the Decision Tree Below:



In this example, in both cases, there is no impact if the outcome is a success. But, both Vendors A & B have an impact on failure and have a probability of failure too. So in this case, you may be wondering why I chose Vendor B instead of A, even though the EMV for A is higher. Are you???

In example 1, we were looking at positive EMV (For Success or Profits) so, we chose the vendor with higher profitable EMV. Whereas, in this case we are calculating Negative EMV (For Failure or Losses). So, choosing the vendor who would cause lower losses in case of a failure would be a better choice. Wouldn’t it??

Example 3: In examples 1 & 2, we took a look at trees that either had a positive or negative impact only. What must we do if we have both? Look at the tree below:



In this case, both Vendors A & B have Impact on both Success & Failure. So, the EMV for each vendor is the sum of the Individual EMV’s.

For Vendor A:

EMV for Failure = 1000 * 30% = 300

EMV for Success = 6000 * 70% = 4200

Total EMV for Vendor A = $ 4,500/-

For Vendor B:

EMV for Failure = -1200 * 40% = -480

EMV for Success = 7500 * 60% = 4500

Total EMV for Vendor B = $ 4,020/-

So, based on the total EMV, Vendor A is the better choice…

Trivia:

Did you note that Vendor B has a negative monetary impact in case of failure??? Be careful and note the – sign… If you did the calculation in a hurry and ignore the – symbol, your EMV for Vendor B would’ve been $ 4,980 suggesting that Vendor B is the better choice. Whereas, the truth was that, because of the –ve impact on failure, Vendor A is the better choice.

Example 4:

In all of the examples above, our decision was based solely on the impact and probability of the scenario’s outcome. What must we do in cases where the decision should also take into account the initial expenses incurred for the activity?

Let’s say, your company has grown hugely in the past couple of years and your current office does not have enough space to accommodate the new guys. So, now you have two choices – Either to construct/purchase a new office or expand the current premises to accommodate the newer guys. In either case, there is cost involved in completion of the office premises. Plus, there could be a high demand for the new space which results in high profits or there could be a low demand resulting in lower profits. This scenario is outlined in the Decision Tree below:




In Tree’s where an initial investment is present, we proceed just like the other scenarios wherein we calculate the EMV for each alternative and sum it up. After that, we deduct the Initial Expenses to arrive at the actual monetary value of the alternative.

Trivia:

In simpler terms, let’s say, I invest 5,000 rupees today and earn 10,000 after 6 months, my profit is 5,000 whereas, if I invest 10,000 rupees and earn 12,000 at the end of 6 months, my profit is only 2,000. Though the eventual money I get at the end of 6 months is higher in the second case, it also means that I invest a larger amount up front thereby reducing profits. So, option 1 where in invest 5000 and get 10000 at the end of 6 months is the better choice. Isn’t it?

So, the EMV Calculation works out as follows:



For Build:

Total EMV = 522,000

If we include Initial Cost – Net EMV = $ 292,000/-

For Expand:

Total EMV = 393,000

If we include Initial Cost – Net EMV = $ 298,000/-

So, the decision here would be to expand the current office premises. Even though the profits that we may earn if we move to a new office are higher, there is a higher impact if people are not willing to move and a high initial cost. As a result, the EMV of expanding the current office is more profitable and hence it is selected.


Some Important Decision Tree Related Terms:

For the exam, actually speaking, whatever we have covered so far is more than sufficient. But, for the same of completeness, I want to cover one last topic related to Decision Trees. There are 3 key terms that we will use while using Decision Tree’s in real life. They are:

a. Decision Node – The Point where an action or decision needs to be made – Signified by a solid black square
b. Chance Node – The Point where events that cannot be controlled by the person who is taking the decision happen – Signified by a solid black dot
c. End of Branch - The end point with nothing connected on one end – Signified by a solid tilted Triangle.

The sample Decision Tree below can help you understand better – as to what I am trying to convey here:



I repeat, knowing these terms or these symbols are not required for the Exam. But, we are not studying just to become certified. Our aim is to become better Risk Managers. So, knowing these will only help you perform your duties better…

Prev: Expected Monetary Value Analysis

Next: Modeling and Simulation

Expected Monetary Value Analysis (EMV)

Expected Monetary Value Analysis or EMV Analysis in short is the 2nd tool and technique in the Quantitative Risk Analysis and Modeling Techniques sub-group. As you might remember, the 1st tool was Sensitivity Analysis that resulted in a Tornado Diagram, which was covered in the previous chapter. 

Expected Monetary Value Analysis calculates the average outcome of future scenarios that may or may not occur. The may or may not occur indicates the fact that, the future scenario is a risk and hence has a probability % based on which it occurs. Isn’t it? 

EMV Analysis calculates the Monetary Value of the Impact of this scenario if it occurs in future – Today. 

If you cannot understand the concept of EMV Analysis clearly yet, don’t worry. We will take a look at a few examples next that should help you understand clearly what EMV Analysis is. 

Some Points to Remember: 

• EMV Analysis is used to make decisions (Ex: Decision Tree Analysis). Don’t worry about Decision Tree Analysis just yet. That is going to be the next chapter
• EMV Analysis looks at both probability and impact together 
• When using EMV we must be in a “Risk Neutral” mind-set and not Risk Averse or Risk Seeking. Remember the chapter titled Factors that Influence Risk Communication where we took a look at people and their Risk Attitudes? 
• When you have multiple scenarios, you add the EMV of all the possible outcomes together before taking the decision. 

Formula for Expected Monetary Value: 

EMV = Probability * Impact 

Some Examples: 

Example 1: 

Impact of a Risk = $ 10,000 
Probability of Risk Occurring = 15% 

EMV = ??? 

EMV = Impact * Probability = 10,000 * 15% = $ 1500/-


$1500 is the Expected Monetary Value of this scenario. i.e., if this risk were to materialize, the monetary value of the occurrence will be $1500. 

In the exam, it is easily possible that the same question as Example 1 could be worded into a long paragraph to check if you understand the concept. The following example does just that. 

Example 2: 

Rajesh is the Project Manager for Project A and is doing EMV Analysis. For one of the top-priority risks he can see that the impact will be $10,000 if it occurs. The Risk Register also states that, as per the initial analysis, the probability of this risk materializing is 15%. What would be the Expected Monetary Value of this Risk, if Rajesh decides to perform EMV Analysis? 

Can you guess what the Answer will be? 

The same $1500. All this question does is, put the camouflage the important details like impact and probability that are required to calculate the final answer along with loads of irrelevant information which can easily confuse a novice Risk Manager. 

In most cases, we will be working with multiple risk related scenarios and hence, the eventual or total EMV is the sum of all the individual EMV’s. The following example does just that. 

Example 3: 

Project X has a 60% Probability of success with an impact of $50,000 and has a 40% chance of failure with an impact of $-20,000. What is the Expected Monetary Value of this Project? 

EMV of success = 50000 * 60% = $ 30,000

EMV of failure = -20000 * 40% = $ -8,000 

Total EMV = $ 22,000/-

Trivia: 

In all cases, a positive EMV indicates that it is an opportunity while a negative EMV indicates a Risk/Threat. 

Now that we know about EMV Analysis, we are well equipped to learn about Decision Tree Analysis which will be the topic of discussion in the next chapter. 

Prev: Sensitivity Analysis

Next: Decision Tree Analysis

Sensitivity Analysis

In the previous chapters, we have learnt about the tools and techniques of Quantitative Risk Analysis that come under the Data Gathering and Representation Techniques sub-group. In this chapter, we are going to take a look at Sensitivity Analysis that falls under the Quantitative Risk Analysis & Modeling Techniques sub-group.

Purpose of Sensitivity Analysis:

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 our analysis, we will find out things like: 

a. How imp certain elements are to the project? 
b. Which variables require special attention?
Etc... 

The Use of Sensitivity Analysis

Any analysis we take up should have some direct or indirect use, otherwise, what is the point of taking it up? Similarly, Sensitivity analysis can help us gather information to back up our recommendations. Sensitivity Analysis shows us a range of outcomes for a risk event. It also helps us identify those risks that can have the greatest effect on the project plan and the overall project objectives. 

Once we know which risk would impact us more, we can use the Sensitivity Analysis as the information backing up our recommendation to give additional consideration for that risk. 

How Sensitivity Analysis is performed:

We measure the effects of a project element on the project objective, when all the other elements are held at their baseline values. By modifying one element and keeping the others static, we are trying to determine the level of uncertainty each element could pose to our project objectives. 

Information required to perform 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. 

Another point to note here is that, Sensitivity analysis can be done on a risk even if it does not fall under the Important or High Impact category. But, in real life, we may not have the time or the resources to take up such analysis on lower impact/priority risks. 

What happens after Sensitivity Analysis? 

We establish a range of variation for each of the risk event and also determine a level of acceptance. Our focus is on changing a single element only. This is also called “What-If Scenarios” wherein we understand what happens if a particular element that could impact the project objective is altered while all others remain stable. We typically play around with the variables and see what happens. Let me repeat, during normal sensitivity analysis, only one element/variable is changed at any given time. 

If you are thinking, why should I change only one variable, why can’t I change more than I at the same time, then fear not? Whatever you thought is perfectly valid and possible. If you do that, it’s perfectly fine but, it just won’t be called Sensitivity Analysis. It is called “Design of Experiments” 

Design of experiments is a statistical method to find the factors that may influence specific variables that may affect the specific outcomes of our project. We determine the combined effect of uncertainty as well as interaction between the various factors. 

Once all your analysis is complete, you need to represent your findings in some form so that the other people in your team as well as your management can understand it. Isn’t it? This final representation is called a “Tornado Diagram” 


Tornado Diagrams

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. As you might remember from the previous paragraphs, during sensitivity analysis, one variables impact on the project objectives is understood while all other variables are set at the baseline. 

A sample tornado diagram:



As you can see, the greater the effect of a variable, the higher up it will feature on the diagram. This means that we should focus on the elements that are higher up in the image. 

When you say that a particular risk would have a higher impact on the project and back it up with the sensitivity analysis and tornado diagram, it would be easier for the people higher up the org hierarchy to understand your recommendation. 

Typically we use tornado diagrams to represent impact on cost, time or quality objectives. 

Prev: Discrete Distributions

Next: Expected Monetary Value Analysis

Discrete Distributions

In the previous chapter, we learnt about the Continuous distributions. As a continuation, we are going to cover the other type of probability distributions which is “Discrete Distribution” 

Remember that, just like the continuous distributions, the discrete distributions too are part of the Data Gathering and Representation Techniques sub-group of the tools and techniques in quantitative risk analysis. 

Another point to note here is the fact that, the PMBOK Guide gives only a very high level or brief description of discrete distributions. We too will be covering only the basic facts (probably in a bit more detail when compared to the PMBOK but not in great detail) that are essential for you to know and understand from the PMI RMP Examination perspective. 

Discrete Distributions are 

• Used to show uncertain events where the probability of occurrence can be calculated accurately
• Based on a whole number 

Discrete Distributions are used to represent: 
• Possible scenarios in a decision tree
• Results of a prototype or
• Results of a test 

The following is a sample discrete distribution: 



As you can see, it contains various bars, each of which represents a possible outcome/option. Each outcome is assigned a probability and the sum of all of the outcomes must add up to 1 or 100% 

For ex: If you flip a coin, there is a 50% chance that you will get a head and another 50% for the tail. So, if we sum up the probability of both these outcomes you get 100%. If you create a Discrete Distribution for this case, you will see two equal sized bars each towering up to 0.5 respectively. 

There are several types of discrete distributions, like:
a. Discrete Uniform
b. Binomial
c. Hypergeometric
d. Etc… 

The point here is that, these are not part of the exam syllabus and hence are out of scope of our current discussion. From the PMI RMP Exam perspective, all you need to remember about the discrete distributions is: 

• They show uncertain events such as outcome of decisions or tests
• They represent several possible outcomes
• Each outcome is assigned a probability and each bar in the image represents an outcome
• The sum of all these probabilities works out to 1 or 100% 
• They are used in decision tree analysis. 


Prev: Continuous Distributions

Next: Sensitivity Analysis

Continuous Distributions

In the previous chapter, we learnt the basic details about Continuous Distributions. In this chapter, we are going to take a detailed look at some of the continuous distributions that are part of the PMI RMP Examination Syllabus. 

There are many other types of continuous distributions. Before we jump into the details about each of these, let’s look at some general points about them. 

• Continuous Distributions are typically used for cost, time and quality metrics
• They show Uncertainty in values
• The values shown in a continuous distribution are infinitely divisible (time, mass, distance etc.)
• They may have a probability of 0 as well

If something is infinitely divisible (like time which can be in years, months, weeks, days, hours, minutes, seconds etc.) you can probably express that as a continuous distribution

Some Important Points to Remember – About Continuous Distributions: 

• Beta and Triangular are the most common types of distributions used in Quantitative Risk Analysis
• Three-Point estimates are displayed using Triangular distributions. They can also be displayed using beta distributions
• Standard Deviations are displayed using normal or lognormal distributions
• Modeling and Simulation frequently use continuous distributions 

Trivia:

In real life you may feel that some other types of distributions are widely used in quantitative risk analysis but from the exam perspective we will consider that beta and triangular are the most commonly used distributions because the PMBOK says so... 

Types of Continuous Distributions:

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 

We will also be covering the definitions of Exponential and Gamma distributions for the same of completeness. However, those two are not part of the RMP Exam syllabus. 

Beta Distribution: 

The Beta Distribution 
• Is used to describe the uncertainty about the probability of occurrence of an event 
• Is based on two shaped parameters
• Uses a range from 0 to 1 and can take several types of shapes. 

The following is a sample Beta Distribution: 




Triangular Distribution: 

The Triangular Distribution:
• Uses the estimate values based on the 3 point estimates that we covered during the chapter on Interviewing. The Optimistic, Most Likely and Pessimistic values from the 3 point estimate will be used here 
• Use only 3 values 
• Is used to quantify risks for each of the WBS elements 

Remember WBS? WBS stands for Work Breakdown Structure. 

The following is a sample Triangular Distribution


Normal and Lognormal Distributions

Normal and Lognormal Distributions are very similar to one another. Their common features include: 

• They use Mean and Standard Deviation to quantify risks
• They gather the 3 point estimates, just like the Triangular distribution
• Standard Deviation is usually displayed using either the Normal or Lognormal distributions


The Normal distribution:
• Is shaped like a bell curve
• The peak of the bell is the Mean/Average 
• Is typically used for variables that cluster around the Mean value 
• Is used to show confidence level as well as variations 

The following is a sample Normal Distribution



A lognormal distribution uses any random value, and those values plot just like a normal distribution. The following is a sample lognormal distribution. As you can see, the shape is pretty similar to the bell-curve of the normal distribution but distinctly different in terms of the actual shape. 



Uniform Distributions

• Are considered to be the simplest form of distributions
• Have all values of the same length which signifies equal probability
• Require you to know the upper and lower limits (the range) in order to use it 
• Show scenarios where no obvious value is more likely to happen than the other

For ex: if you were to throw a dice that isn’t rigged in any way, the chances of either of the 6 numbers turning up are equal and hence you will show that using this distribution. 

The following is a sample uniform distribution



Cumulative Distributions

Cumulative Distributions can convert a set of data values into a distribution that can be analyzed. They are typically S shaped. A sample Cumulative Distribution would look as follows:



There are two other types of distributions that would come under the blanket of Continuous Distributions. Though the PMBOK does not cover it, our chapter wouldn’t be complete if I don’t mention at least the definition of these two distributions. 

Exponential Distributions

The exponential distribution is used to represent the time between the arrival of random events that occur continuously and independently at an average rate

Gamma Distribution 

The Gamma distribution models the amount of time it would take for certain events to occur if the event occurs randomly with an average time between the events. Typically, gamma distributions are used to display waiting times. 

Trivia: 

Exponential and Gamma are not part of PMBOK or the PMI RMP Exam Syllabus

Before we wrap up this chapter on continuous distributions, let me reiterate that covering all possible details reg. each of these distributions is beyond the scope of the RMP exam and so I did not do it. If you have the time or the personal interest you can search Wikipedia or pick up a book on probability distributions to learn more about that. 


Lastly, if you know or rather remember whatever we have covered here, that should be more than sufficient from the PMI RMP Exam perspective. 


Prev: Introduction to Probability Distributions

Next: Discrete Distributions

Introduction to Probability Distributions

In the previous chapter, we looked at one of the tool and technique used in quantitative risk analysis which was called “Interviewing”. In this chapter, we are going to start with another tool that is used in this quantitative analysis process called “Probability Distributions”.

As with Interviewing, the probability distributions too are part of the data gathering and representation techniques sub-group.

We all know what Probability is, isn’t it?

In general, probability refers to the likelihood that a risk or any event for that matter will occur. It is usually represented numerically as a number value between 0 and 1. The closer the value is to 1, the greater the probability of the event occurring. Similarly, the closer the value is to 0, the lower the chances/probability of that event happening.

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.

This chapter is just the introduction, if you aren’t too clear on what these distributions are, don’t worry. As we start looking into each of these distributions in detail, you will get a good idea of what these are and how they are used in the quantitative risk analysis process.

In the subsequent chapters, we will be covering two types of distributions, namely:

1. Continuous Distributions and
2. Discrete Distributions

Let us wrap up this chapter with a disclaimer that, there are numerous types of probability distributions. We won’t be covering all of them as part of this series. Also, the whole topic of probability distributions is very complicated. We will only cover those distributions that are part of the RMP Exam syllabus, as well as, whatever is required to be known from the RMP Examination perspective.

We will be covering a lot more than what the PMBOK tell us about these distributions but this isn’t an exhaustive reference material in this topic.

Let us start with Continuous Distributions which is the topic of the next chapter.

Prev: Interviewing

Next: Continuous Distributions

Interviewing

Interviewing is one of the techniques that are used in Quantitative Risk Analysis. As mentioned in the previous chapter, it is part of the Data Gathering and Representation Techniques sub-group. 

At a high level, we all know what Interviewing is, isn’t it? But, in this chapter, we are going to learn what we need to know about interviewing from Quantitative Analysis perspective. What information we are looking to get through interviewing and what we are supposed to do with that information are a couple of questions that will get answered in this chapter. 

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. Don’t worry too much about probability distributions just yet. We will be covering it very soon. For now remember that the data we gather could vary based on the type of distribution we use. That should be sufficient to understand the forthcoming section. 

For commonly used distributions, we gather three point estimates through these interviews. The three point estimate is calculated using the PERT Formula. Are you wondering what PERT is? If you are someone who has not studied the PMBOK Guide fully yet, I strongly urge you to do that because, the PMI RMP Exam uses a lot of terms and concepts that are part of the standard Project Management Framework as per the PMBOK. Anyways, PERT stands for Program Evaluation and Review Technique. It is a technique used to estimate durations for activities. In my earlier series on PMP Certification I had covered it in great detail in the chapter titled Estimating Activity Duration. 

In short – As per the 3 point formula, we try to arrive at 3 different estimates: 
a. The Optimistic Estimate (O) 
b. The Most Likely Estimate & (ML) 
c. The Pessimistic Estimate (P)

The 3 point estimate is a weighted average of these 3 estimates and is usually more accurate. The formula is as follows: 

3 point estimate = (O + 4 * ML + P) / 6 

The range here between the optimistic and pessimistic numbers is the range of uncertainty for our estimate. The more the uncertainty, the greater the range. Remember, the chapter titled Managing Uncertainty? Uncertainty causes risks and the whole purpose of Risk Management is to keep this uncertainty to the minimum. 

Trivia: 

Whenever we calculate any numbers or values, we must always document how those values were derived, along with any assumptions or constraints that went into the calculation. 

Standard Deviation: 

The Standard Deviation (SD) is a measure of how far the actual estimate we have calculated is from the mean value. The formula to calculate SD is:

SD = P – O / 6 

Here again – P is the pessimistic and O is the optimistic estimate. 

An Example: 

Let us say, you are talking to an Engineer about construction of a brick wall. He is an expert and is going to give you his feedback during the interview. He feels that, the brick wall could be constructed in 10 days if everything goes smoothly but, if it rains then the construction may get extended to up to 20 days. He also feels that, the construction should be complete in around 15 days. 

So, as per this discussion: 

O = 10 days
P = 20 days and
ML = 14 days 

3 point estimate = (10 + 4 * 14 + 20) / 6 = 14.33 days 

SD = 20 – 10 / 6 = 1.667 days 

As per the 3 point estimate we can say that, the construction of the wall will be completed in 14.33 days while the standard deviation will be 1.667 days. The SD signifies the fact that the construction may get shortened or extended by this value based on the prevailing conditions. So, the work could complete 1.667 days prior to the planned 14.33 days or it could take 1.667 days more than the planned days. 

So, the expected construction duration will be 12.663 days to 16 days. 

Trivia:

All estimates provided must be justifiable. You can’t just take the word of the SME for the numbers. Ask them for justifications and note them down for future reference. 

In this chapter, we have covered a lot of items pertaining to interviewing, estimates etc. From the PMI RMP Exam perspective, you need to remember the following: 

1. Interviewing is a tool and technique that is part of the Perform Quantitative Analysis Process and a part of the Data Gathering & Representation Techniques sub-group
2. We use this technique to gather information using historical data (or SME’s) and help quantify the probability and impact of Risks on project objectives
3. Information gathered using this technique depends on the type of probability distributions used 
4. The 3 point estimate formula is: (O + 4 * ML + P) / 6
5. The Standard Deviation formula is: P – O / 6

Prev: Tools & Techniques Used in Quantitative Analysis - Introduction

Next: Introduction to Probability Distributions

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

Inputs Used In Quantitative Risk Analysis

In the previous chapter, we took a high level introductory look at Quantitative Risk Analysis. Next, we are going to look at the inputs that we will be using in this process.

The inputs that we will utilize for performing Quantitative Analysis are:

1. Risk Register
2. Risk Management Plan
3. Cost Management Plan
4. Schedule Management Plan &
5. Organizational Process Assets

Trivia:

If you are someone who is still using the 3rd edition of the PMBOK guide you will find some major differences between the guide and what you are seeing above. First off, the 3rd edition just lists the Project Management Plan as an input while the 4th edition (Which I am following & strongly urge you do too) splits them up and lists down the individual plans. In fact, the 3rd edition lists down the Risk Register & the Risk Management Plan as separate inputs while they too are part of the Project Management Plan.

Let us now take a detailed look at each of these items…

Risk Register

In one of our previous sections, we took a detailed look at the Risk Register. To refresh our memory, the Risk Register contains all the information about the risks we have identified so far. Almost all the information in the risk register will be useful for our analysis but, from a quantitative analysis perspective, we will focus especially on:
a. Risks set aside for Additional Analysis &
b. Risk Categories used

Trivia:

I have said this numerous times but let me repeat – Remembering how each item will be used for a certain activity/process will help you remember them because, if you know what it is used for and when it will be used, you can relate to it better. So, don’t just try to memorize the inputs, try to understand them.

Risk Management Plan

The Risk Management Plan, as we all know is the heart of all Risk Management activities in our project. During quantitative risk analysis, we will use the following elements of our Risk Management Plan:

a. Risk Management Methodology
b. Roles & Responsibilities
c. Budgets available
d. Timing Information
e. Risk Categories
f. Risk Breakdown Structure
g. Stakeholder Risk Tolerance
h. Reporting Formats

As you might remember from our earlier section that was dedicated to the Risk Management Plan, the plan actually contains a lot more information that what is listed above. If you can’t remember all of them, you can go back to that section to review them once again.

Trivia:

From a layman perspective, the Risk Management Plan is the background and the Risk Register is the front & center of our quantitative analysis.

Cost Management Plan

The Cost Management Plan provides the necessary information we need to establish the criteria for controlling the project costs. Before we can numerically analyze the risks, we need to analyze and identify the best approach possible. We can use the cost management plan to select the best structure and techniques that will suit our project, from the ones available. Without the Cost Baseline information (which is present inside the cost management plan) taking this decision could be very difficult.

We can also use the cost management plan to analyze the numeric impact of the risks that we have identified on our project’s costs.

Schedule Management Plan

The Schedule Management Plan provides the necessary information we need to develop and control the Project’s Schedule. It will help us develop controls on how we will approach or rather handle our Project’s schedule. Things like the overall schedule, network diagrams etc. will be required to understand the impact that the risks we have identified will have on our Project.

Trivia:

Think of a scenario where we have uncovered a potential positive risk that can help reduce the project schedule by 3 months and improve profits by 25%. Would you want to pass-up on an opportunity like that? I am sure your answer would be, No Way. I would want to capitalize on the opportunity. This is exactly where Quantitative Analysis comes into picture. If you have all your facts readily available about the threat or the opportunity, we can take better informed decisions.

Organizational Process Assets

Organizational Process Assets are used as input to almost every single activity that you may take up as part of Project Management or Risk Management. So, it is no wonder that you see it here as well. We will use the following items from the Organizational Process Assets during Quantitative Risk Analysis:

a. Information from previous similar projects, including actual outcomes and risk analysis performed
b. Techniques used, lessons learned etc.
c. Studies of similar projects conducted by Risk Specialists
d. Industry or Proprietary Risk Databases

Items c & d, may or may not be available for everyone but if your organization uses proper Project Management processes, items a & b should be readily available. Using these can help save time as well as improve the efficiency of our current activities.

Trivia:

Though not explicitly listed as an input to Quantitative Analysis, the Project Scope statement gives us the boundaries of what the project is supposed to accomplish. We need to keep this in perspective to ensure that we do not deviate from our boundaries while performing our Risk Management activities.

Prev: Introduction to Quantitative Risk Analysis

Next: Tools & Techniques in Quantitative Analysis