Perform Quantitative Risk Analysis in project management (PMP) is the process of looking at how different project risks and other sources of uncertainty affect the project’s general goals using numbers. The main benefit of Quantitative Risk Analysis process is that it measures the general risk of the project and can also give you more quantitative risk information to help you plan how to respond to risks. You don’t have to do this for every job, but when you do, you do it the whole time. Figure below shows what goes into , tools and techniques ,and comes out of this process.
Perform Quantitative Risk Analysis: Inputs
Project Management Plan
The following are some of the parts of a project management plan:
Risk management plan
The risk management plan states if the project needs Quantitative risk analysis or not. It also lists the tools that can be used for the analysis and how often the analysis is expected to happen.
Scope baseline
When looking at the effects of different project risks and other sources of uncertainty, the scope baseline shows where things start.
Schedule baseline
The schedule baseline shows where we can begin to look at the effects of individual project risks and other sources of uncertainty.
Cost baseline
The cost baseline shows where to begin when looking at the impact of individual project risks and other sources of uncertainty.
Project Documents
It’s possible to use the following project documents as inputs for this process:
Assumption log
You can use assumptions in a numeric risk analysis if you think they could hurt the project’s goals. When doing a quantitative risk analysis, you can also consider how constraints might affect the results.
Basis of estimates
The figures that were used to plan the project may show up in the variability that is modeled during a quantitative risk analysis process. This could include details about the estimate’s goal, classification, expected accuracy, method, and source.
Cost estimates
The starting point for figuring out how much costs vary is the cost estimate.
Cost forecasts
You can compare forecasts like the project’s estimate to complete (ETC), estimate at completion (EAC), budget at completion (BAC), and to-complete performance index (TCPI) to the results of a quantitative cost risk analysis to find out how likely it is that these goals will be met.
Duration estimates
The starting point for evaluating plan variability is an estimate of how long something will take.
Milestone list
Important events in the project set the schedule goals that are compared to the results of a quantitative schedule risk analysis to find out how likely it is that these goals will be met.
Resource requirements
If you want to assess variability, you start with the resource requirements.
Risk register
As an entry for quantitative risk analysis, the risk register has information about each project risk.
Risk report
The risk report talks about the sources of overall project risk and the current state of overall project risk.
Schedule forecasts
If you want to know how likely it is that you will meet your goals, you can compare your forecasts to the results of a quantitative schedule risk analysis.
Enterprise Environmental Factors
Some things in the business world that can affect the Perform Quantitative Risk Analysis process are, but aren’t limited to: studies of similar projects in the same industry; and published materials, such as commercial risk databases or checklists.
Organizational Process Assets
Information from similar finished projects is an example of an organizational process asset that can affect the Perform Quantitative Risk Analysis process.
Perform Quantitative Risk Analysis: Tools And Techniques
Expert Judgment
in perform quantitative risk analysis ( in PMP ) tools and techniques , You should look at the information and experience of people or groups who are experts in the following areas:
- Adding information about individual project risks and other sources of uncertainty into the quantitative risk analysis model in the form of numbers,
- Figuring out which way to show uncertainty is best for modeling certain risks or other types of uncertainty,
- Applying modeling methods that are right for the project,
- Figure out which tools will work best with the chosen modeling methods, and
- How to understand the results of quantitative risk analysis.
Data Gathering
You can use interviews to gather information for the quantitative risk analysis. This information can include risks specific to the project and other sources of doubt. This is especially helpful when you need information from pros. During the interview, the reporter should create an atmosphere of trust and privacy to encourage people to give honest and fair answers.
Interpersonal And Team Skills
For this process, interpersonal and team skills like coaching can be useful. A skilled facilitator can help get information from people on the project team and other parties during a risk workshop. Facilitated workshops can be more useful by making sure everyone knows what the purpose of the workshop is, getting everyone to agree on something, keeping everyone’s attention on the job at hand, and coming up with creative ways to handle interpersonal conflict or sources of bias.
Representations Of Uncertainty
To do quantitative risk analysis, you need to feed information into a model that shows the risks that come with each project and other sources of uncertainty.
If the planned activity’s length, cost, or resource needs aren’t known, the model can show the range of possible values as a chance distribution. This could look different ways. They are triangular, normal, lognormal, beta, regular, and discrete. When picking a probability distribution, it’s important to make sure that it covers the full range of possible outcomes for the planned action.
Data Analysis
During perform quantitative risk analysis ( in PMP ) tools and techniques , you can use the following data analysis methods, but they are not limited to them:
Simulation
To figure out how likely it is that different project risks and other sources of uncertainty will affect meeting project goals, quantitative risk analysis uses a model that simulates these effects. It is common to use a Monte Carlo analysis when running simulations. It uses the project cost figures when it runs a Monte Carlo analysis for cost risk. You use the schedule network diagram and estimated lengths of time when you do a Monte Carlo study for schedule risk. Combined quantitative cost-schedule risk analysis uses both of these. A mathematical risk analysis model is what comes out of it.
A computer program runs the quantitative risk analysis model over and over again a few thousand times. Each iteration picks random input values, like cost estimates, duration estimates, or the likelihood of probabilistic branches. Outputs are the different things that could happen with the job, like when it will end and how much it will cost to finish. One common output is a chart that shows how many times a certain outcome happened in the simulation, or an S-curve that shows the chance of getting any outcome or less.
Sensitivity analysis
Sensitivity analysis helps figure out which project risks or other sources of uncertainty could have the biggest effect on how the project turns out. It connects changes in parts of the quantitative risk analysis model to changes in how projects turn out in perform quantitative risk analysis ( in PMP ) tools and techniques.
Decision tree analysis
Decision trees help people choose the best course of action from a list of options. The decision tree displays different possible routes through the project, with each branch representing a different choice or event. Each path can come with its own costs and project risks, which can include both threats and opportunities. If you go down a certain road in the decision tree, each branch’s end point shows what will happen, which could be good or bad.
Influence diagrams
Influence diagrams are pictures that can help you make decisions when you don’t know what to do. There are entities, outcomes, and influences in an influence diagram, along with the connections and effects between them. The diagram shows a project or situation within the project. Using ranges or probability distributions can show in an influence diagram an element that isn’t certain because of individual project risks or other sources of doubt. Simulating the diagram with a method like Monte Carlo analysis shows which parts have the biggest effect on important results. The results of an influence diagram are like those of S-curves, tornado diagrams, and other quantitative risk analysis methods.
Perform Quantitative Risk Analysis: Outputs
Project Documents Updates
Project documents like the risk report are one example of an output from this process. There will be a change to the risk report to include the results of the quantitative risk analysis. In most cases, this will include:
Assessment of overall project risk exposure
In terms of overall project risk, two main factors stand out:
- There are chances that the project will succeed based on how likely it is that it will meet its main goals (like the needed end date, interim milestones, cost target, etc.) despite the known risks and other unknown factors; and
- The range of possible project outcomes shows the degree of inherent variability that was still in the project at the time the study was done.
Detailed probabilistic analysis of the project
We show some of the most important results from the quantitative risk analysis, like S-curves, tornado diagrams, and criticality analyses, along with a story explanation of what they mean. Some specific outcomes that could come from a quantitative risk study are:
- Amount of contingency reserve needed to give a certain amount of confidence;
- Figure out which project risks or other sources of uncertainty have the most significant impact on the project’s critical path; and
- The main factors that affect the general risk of a project and have the most impact on the uncertainty of the project outcomes.
Prioritized list of individual project risks
According to sensitivity analysis, this list has the project risks that are the most risky or provide the most opportunities for the project.
Trends in quantitative risk analysis results
Repeating the study at various times in the project’s life cycle may reveal patterns that help with planning how to handle risks.
Recommended risk responses
Based on the results of the quantitative risk analysis, the risk report may talk about how to deal with the general project risk or with specific project risks. Plan Risk Responses will use these suggestions as feedback.
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