A Summary of Quantitative Risk Analysis Methods and Benefits
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If you're interested in project management, you may be familiar with quantitative risk analysis, which is a step in the risk management process. You can identify and mitigate risks, so they don't seriously affect the progress of a project. Understanding how to conduct this analysis can help you improve how you organize projects for your team.
In this article, we discuss quantitative risk analysis, review how you can use it in project management, outline various techniques to perform this analysis, examine the differences between quantitative and qualitative risk analysis, and describe the benefits of using this type of risk analysis.
What is quantitative risk analysis?
A quantitative risk analysis is a numeric assessment of the general outcome of risk on a project's key elements, such as the plan and budget objectives. The analysis outcomes can inform you of the likelihood of completing the project.
A company can use quantitative risk analysis to set contingency reserves for the project if the project ends up costing more if the materials you require for the project or the services you pay for end up costing more than initially projected. Conducting a quantitative risk analysis can help you establish precise and realistic cost, schedule, and scope targets.
Analyzing risk quantitatively means determining risk at each step as the project progresses. Using quantitative risk analysis can be helpful for projects with significant financial needs that require precise calculations.
Companies usually perform this analysis to determine the feasibility of a project's schedule or cost targets. The goal of quantitative risk analysis is to decipher the chances and effect of risk into a quantifiable amount. You can find the risk value in the structure of projects, and you can add the amount to the plan cost or estimated time as an exigency value.
Read more: What Is Quantitative Analysis?
Quantitative risk analysis in project management
Using quantitative risk assessment in project management is the practice of modifying the effect of risk on a project into numerical terms. You can use the numeric information you find in quantitative risk analysis to control the time and cost contingencies, which can help you reduce your risk of being over budget or late on completion of the project. Project risk management aims to acknowledge and mitigate the effects of risks on a project.
The difficulty with risk management is that most risks are unknown events. Businesses can try to minimize their exposure to unclear circumstances by managing risk when dealing with a project. You can do the process of risk management through a suitable management process that includes the following steps:
Design your risk response: identify and quantify potential risks to determine how much of an impact they can have on the project
Control potential risks: establish ways you can minimize, prevent, and avoid potential risks
Achieve a quantitative risk analysis: conduct a thorough risk analysis so you rarely encounter any surprises
Design a risk management plan: decide what's the plan if the risks identified manifest during the project so you can alert all team members and put the plan into action
Common quantitative risk analysis techniques
There are several ways to conduct a quantitative risk analysis. Here are the methods you can use to do your quantitative risk analysis:
Companies can use sensitivity analysis to help determine which risks have the most probable effect on a project and how to affect target variables based on changes to input variables. It can also help assess the risk of a strategy and identify the dependent output on a particular input value. For example, a tornado diagram is a typical presentation of outcomes using this type of analysis.
Expected monetary value (EMV) analysis
An EMV analysis is an arithmetical method of assessing risks that computes the average consequence when the future includes scenarios that might or might not happen. You can calculate EMV for a project by multiplying the value of each possible outcome by its probability of occurrence and adding the products together. For example, consider you're a manager of a software development project, and you've found a risk related to market demand. The probability of the risk occurring is 25%, and if it happens, you can lose $20,000. To calculate the EMV of the risk, you can use the following formula:
Probability of occurrence: 25%
Cost of risk: -$20,000
Formula: -$5,000 = .25 x -20,000
Decision tree analysis
A decision tree analysis can help you compare different courses of action and assess the probabilities of success, including the potential risks and benefits associated with each of them. A decision tree analysis usually starts with an initial proposed decision, which you can create after listing and reviewing the possible sequences of outcomes.
Once you have determined all the sequences and evaluated their respective probabilities, you can choose a course of action based on a combination of the most desirable outcomes and the likelihood of success. Below is an example of a decision tree analysis:
Decision tree analysis example
Here's an example of a decision tree analysis to help you understand how it works:
A manufacturer is deciding between two ways to make a new product. The COO starts the decision tree analysis by creating two branches, one for a physical product and one for a digital product. Once they have the first two branches, they make new branches to identify each option's possible outcomes. The COO completes the tree with an investor reaction branch from each of those outcomes, determining a monetary value that they can assign each result with a rating of good, moderate, or poor.
An organization can use simulation to analyze risk quantitatively. You can do this by using a project prototype that clarifies the overall risk level and how it can affect the project objectives stated at the total project level. Project simulations often use computer prototypes and risk evaluations shown as the chances of incurring additional costs at points during the project or periods where the work level increases.
Monte Carlo analysis
The Monte Carlo analysis, also known as the multiple probability simulation, models the predictability of different outcomes in a process that project managers can't easily predict because of the intervention of random variables. To understand the Monte Carlo analysis better, consider calculating the probability of rolling two dice. There are 36 possible dice rolls, and based on this knowledge, you can calculate the probability of a specific outcome.
Project managers can use this technique to understand the impact of risk and uncertainty in prediction and forecasting models. Project managers can also use it to tackle a wide range of problems in almost any field, including science, supply chain, engineering, and finance.
Empirical methods (benchmarking)
Empirical methods use past projects to define the factors that push risk. Once specified, you can apply the elements to the potential project to influence the possibility-based characteristics shared with past projects. The methods used include factor rating, which involves taking a large data set and shrinking it to a smaller data set to make it more manageable and easier to understand, and regression, which is a method of identifying which variables influence a particular subject.
Difference between qualitative and quantitative risk analysis
Analyzing risk quantitatively is often subjective because the data comes from personal perceptions of the likelihood and consequences of risk. This type of risk analysis aims to raise awareness of severe and most likely risks. It can also help you determine a project's weak spots and strategies to reduce the risk's impact. Quantitative risk analysis is objective, describing the same risks as qualitative risk analysis but using specific numerical metrics. For example, you can use dollar amounts to assess the costs of the risks on the project's targets.
Quantitative risk analysis is typically used on high-priority potential risks because it's often more time-consuming than qualitative risk analysis. You can save time and money by doing quantitative risk analysis on the risks that matter most. Quantitative risk analysis can help you create realistic cost and time targets and calculate the likelihood of meeting project objectives. You can also use quantitative risk analysis to determine the fines a company may incur, such as the specific cost of failing to obtain a building permit.
Quantitative risk analysis benefits
The following are some of the benefits of conducting a quantitative risk analysis:
allows you to determine the chances of accomplishing a particular project objective
quantifies the risk revelation of a project and determines the possible time and money that you may require
organizes risk by financial effect and possessions by their financial value
allows you to state outcomes in specific management terminology
helps you better determine the safety level of a project based on three basic requirements, privacy, veracity, and accessibility
allows you to do a cost analysis to choose the best procedures suited for the project
improves the precision of data as an organization grows
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