Quantitative risk analysis assists (QRA) in identifying factors impacting individual projects or organizations by applying research methodologies with the purpose of measuring and projecting future conditions.
This type of analysis takes the probability and impact of risks on a project or organization using numerical data, thus creating a quantitative risk assessment (QRA). In addition to numerical data, the analysis is supplemented with statistical tools and analytic techniques
An example of this includes but is not limited to decision trees, simulation modeling, Markov chains, Monte Carlo simulations, expected monetary value (EMV) calculations, and actuarial risk analyses.
Even though QRA falls under the category of qualitative analysis in most organizations, it has become so large that it now is considered one of the most important project management processes for quantitative forecasting.
Quantitative Risk Analysis PMP Overview
The first step to risk management on any project is to identify the risks that could happen, then identify which of those are most likely to occur and work on mitigating them. Quantitative risk analysis (QRA) is how you do this.
QRA is most effective when applied at the business or portfolio level, and less effective when used for project-specific risk analysis. With QRA, it is possible to estimate the probability of various risks occurring in a future time period and how they will affect performance.
It can provide an overall picture of the likelihood of achieving project objectives based on one or more scenarios. It provides insights into what could go wrong and why in order to establish a better course of action (or inaction). This approach has been shown to help leaders make better decisions that have a direct impact on their ability to achieve strategic results.
In most cases, PMI encourages organizations to utilize quantitative forecasts for resources required over time periods rather than just hard numbers with a high degree of accuracy. Furthermore, QRA helps determine the most effective strategy to use for risk response planning, which is why it’s important to utilize this process when developing a portfolio.
What is the Purpose of Quantitative Risk Analysis:
The purpose of quantitative risk analysis is to give insight into the probability of potential future events by providing numerical data, rather than merely assessing qualitative risk.
This type of risk analysis calculates quantitative forecasted values for uncertain vital variables at specific points in time. It applies statistical techniques and simulation modeling to measure future conditions using historical data, expert opinions, or assumptions before making projections about the probability of certain events.
With this knowledge it is possible to assess if plans are integrated with one another (and how they work together), identify the likelihood that each project objective will be met, determine which risks pose the highest risk to performance, assess whether action should be taken, and assess how an organization can best respond.
This process often helps companies decide between two or more courses of action in order to increase organizational success. Furthermore, with this information, users can plan and prepare in order to manage risks and spread the momentum created by positive results.
Quantitative risk analysis helps identify risk factors that could occur in a future time period and which of those pose the greatest risk. Lastly, quantitative risk analysis is based on probability (the chance that something will happen with numerical odds) rather than possibility (what might happen if certain conditions are met with varying degrees of probability).
The reason organizations use this process is to determine how effective their current strategies for success would be under different conditions.
Quantitative Risk Analysis Techniques/ Methods:
There are several quantitative risk analysis (QRA) techniques and methods that can be used:
- Monte Carlo Simulation: This technique has been shown to create more accurate risk forecasts than other models. It is typically used with simulation modeling by creating a range of values for each variable in the model, then using probabilities as an input to run simulations thousands or millions of times so as to generate possible outcomes.
- Expected Monetary Value Calculation: This type of QRA calculates the expected value based on risk probabilities and impact, so it only considers those risks deemed most likely. It can be applied whether there’s one individual project or multiple projects and organizations involved. The expected monetary value calculation essentially means multiplying likelihood of a certain event occurring times the impact of that event.
- Scenario Analysis: This type of QRA calculates the possible range and likelihood of different project outcomes, and it’s useful for determining whether a desired outcome can be achieved within an organization’s risk tolerance level. With this technique, management teams create multiple scenarios (or stories) about what could happen—good or bad—in order to make decisions. It helps answer questions such as how many resources are needed and when will they be needed (if at all)?
- Stakeholder Impact Analysis: A qualitative approach, stakeholders’ satisfaction is often measured by gauging their burnout according to factors such as workload balancing and work-life balance. Stakeholders’ expectations may also need be taken into consideration since there always seems to be a degree of uncertainty when predicting the future.
The most effective way to conduct quantitative risk analysis is by using a structured and standardized approach. This approach has been shown to help leaders make better decisions that have a direct impact on their ability to achieve strategic results.
Quantitative Risk Analysis Example:
When developing a portfolio, QRA can be used to determine the most effective strategy to use for risk response planning. This is why it’s important to utilize this process when developing a portfolio.
For instance, an organization may choose to use the quantitative risk analysis method of scenario analysis in order to assess whether or not projects are integrated with one another. A leader would start this process by asking questions about what could happen—good or bad—in order to make decisions based on possible outcomes. From there, managers could decide between two or more courses of action that will increase organizational success. The benefit here is that all scenarios (or stories) about what could happen are considered in determining which option provides the greatest chance for positive results.
Once the person conducting the QRA has identified all sources of risk, they should work with management to develop a plan for each source of risk. For example, if one area is likely to have three negative events that could occur during the next year within an organization, then perhaps having ten resources on hand would be sufficient. On the other hand, if there are four areas that are likely to have eight positive events in a given year, having 15 resources available would be more suitable for this scenario. Because scenarios are being considered at this stage, it’s important for management teams to establish clear expectations before implementing any changes.
If risks cannot be removed or transferred and they exceed the organization’s threshold levels (i.e., acceptable risk or not), then resources may need to be reallocated. For example, if negative events outweigh the positive within an organization’s portfolio, and it becomes clear that there’s too much risk, then management teams can determine whether or not something needs to change – such as dropping certain projects altogether.
When performing a quantitative risk sensitivity analysis, it’s important to remember that such things as poor assumptions (i.e., uncertainty) and unknown circumstances could affect the results. Because of this, it’s important for managers to keep in mind all possible outcomes when conducting QRA so they can make decisions based on data rather than emotions. This is done by identifying sources of risk and devising appropriate strategies for achieving success
Qualitative Risk Analysis vs Quantitative Risk Analysis:
- A) Quantitative risk analysis helps leaders make informed decisions by providing them with two or more courses of action that are based on data. Qualitative risk analysis, on the other hand, is less structured and relies more heavily on the decision-making abilities of managers.
- B) A qualitative risk analysis can provide insights into what could happen positive or negative while also helping decision-makers determine the best course of action to take in response. Quantitative risk analysis lacks this level of detail since it focuses on projecting financial data rather than gaining insights from stakeholders.
- C) Quantitative risk analysis helps leaders make more strategic decisions by giving them access to specific details about what could happen in various situations. More importantly, this method provides greater opportunities for positive outcomes to take place as managers can “write the story” about what could happen and gather data to support such conclusions.
- D) While a quantitative risk analysis is more structured than qualitative risk analysis, it doesn’t provide leaders with detailed information about what could happen in certain scenarios. Qualitative risk analysis, on the other hand, lacks structure and focuses solely on understanding stakeholders’ opinions.
In conclusion, the PMBOK recommends that all projects have a risk management process that includes identifying and mitigating risks. In order to identify potential threats, qualitative analysis is used first, then, when it is time to decide which threats are most likely to occur, quantitative analysis comes into play. The final product of this methodology is a project plan with a better understanding of how much the project will cost and how long it will take. This information can be beneficial in determining if current conditions could place the company’s business continuity at risk before making any decisions.