Quantitative Risk Analysis- Concept, Formula, Examples

Editorial Team

Quantitative Risk Analysis PMP

When designing a project, combatting risks is unavoidable. With advancing technologies and developing working methodologies, risk management has become extremely vital. Risks need to be recognized and moderated appropriately or they can seriously damage your progress. These risks need to be tackled with extreme precision and transparency. Nowadays, many organizations focus on eliminating risks as early as possible. Quantitative risk analysis is a proven technique that can help to combat risks. . in project management and might be asked during PMP exam. Quantitative risk analysis is also a concept which is used in Project Management Professional (PMP) exam. PMP is a product of Project Management Institute (PMI). This article explores quantitative risk analysis. 

What is Quantitative Risk Analysis

Quantitative risk analysis is a numeric evaluation of the general effect of risk on the project intents such as budget and agenda objectives. The outcomes offer an understanding of the probability of project accomplishment and are used to advance contingency reserves. The quantitative risk offers a numerical approach to make decisions when there is ambiguity and make an accurate and attainable cost, agenda, or scope aims. 

Quantitative risk analysis tries to allocate expressive figures to all elements of the risk analysis progress. It is proposed for large, budget projects that need precise calculations. It is normally performed to study the practicality of a project’s cost or schedule aims. The drive of quantitative risk analysis is to interpret the likelihood and effect of risk into a quantifiable quantity. The value of the risk, in the framework of projects, is added to the project cost or time estimation as a contingency value. 

Quantitative Risk Analysis in Project Management

Quantitative risk analysis in project management is the process of altering the effect of risk on the project into arithmetical terms. This arithmetic information is often used to control the cost and time contingencies of the project. The purpose of project risk management is to recognize and minimalize the effect that risks have on a project. The challenge with risk management is that risks are indefinite events. 

In the management of projects, organizations try to lessen their revelation to these indefinite events through risk management. This is typically done through a proper management process which consists of the following steps: design risk management, recognize risks, achieve quantitative risk analysis, design risk rejoinders, and control risks.

Quantitative Risk Analysis vs Qualitative Risk Analysis

Quantitative risk analysis is objective. It uses provable data to examine the effects of risk in terms of cost overflows, scope slinks, resource depletion, and schedule interruptions. Qualitative risk analysis, on the other hand, tends to be subjective. It focuses on identifying risks to measure both the probability of a specific risk event happening during the project life span and the effect it will have on the whole schedule should it occur. The objective being to determine sternness. Outcomes are then documented in a risk assessment matrix to communicate unresolved risks to shareholders. Eventually, the drive is the same: the difference is that it takes a more logical, data-intensive method.

Quantitative risk analysis relies on precise statistical data to create actionable perceptions. Instead, they used a more subjective, qualitative approach to risk management, which had one key benefit: it was faster and laid-back to apply. Unlike quantitative analysis of risk, which relies on robust risk models, a high capacity of data, and in some cases, expert software, qualitative risk analysis can be achieved at any phase of the project.

Nonetheless, quantitative risk analysis is fundamental, both in its accuracy and as a way of steering further analysis on existent risks. This comprises: quantifying probable consequences, clearing up any persistent ambiguity neighboring the outcomes of your original qualitative analysis, setting attainable aims in terms of schedule and cost, and Evaluating the possibility of fruitfully accomplishing these objectives. High-risk businesses in particular- mining, construction, oil and gas, anything that shows a very real hazard to the wellbeing of frontline workforces on a day-to-day basis- rely deeply on quantitative risk analysis. Luckily, as technology advances, so too has the way we achieve quantitative risk analysis. New gears are accessible to aid improve the legitimacy of your risk analysis and comprehend the stages required to moderate possible problems.

One clear thing is that none is better than the other. If anything, Quantitative risk analysis must be steered simultaneously, giving you the best probable intuition into risks and their possible effect on the fruitful accomplishment of your project. Risk manager takes an accurately complete method to risk management and assimilates effortlessly, providing you with all the data required to achieve effective analysis. Irrespective of the scope or intricacy of your project, you are equipped with everything required to make the best verdicts for your organization.

Quantitative Risk Analysis Techniques/ Methods

You will be required to comprehend and acquaint yourself with this quantitative analysis of risk techniques for the PMP certification EXAM. The following are some of the techniques:

Sensitivity analysis: A quantitative risk analysis and exhibiting technique used to aid determine which risks have the most probable effect on the project. It scrutinizes the level to which the ambiguity of each project element affects the objective being scrutinized when all other indefinite elements are held at baseline standards. The typical presentation of outcomes is in the form of a tornado diagram.

Expected Monetary Value (EMV) analysis: An arithmetical method that computes the average consequence when the future includes scenarios that may or may not happen. A common use of this technique is within decision tree analysis.

Decision tree analysis: A schematization and calculation method for gauging the consequences of a chain of multiple choices in the presence of ambiguity.

Simulation: A simulation uses a project model that interprets the reservations quantified at a detailed level into their probable effect on objectives that are articulated at the level of the total project. Project simulations use computer models and evaluations of risk, usually articulated as a possibility distribution of possible costs or durations at an exhaustive work level, and are normally performed by using Monte Carlo analysis.

Empirical Methods (benchmarking): these techniques use historical projects to determine factors that drive risk. These factors are then applied to a prospective project to determine the contingency-based characteristics that are shared with the historical projects; these methods include:

  1. Regression 
  2. Factor rating 

Quantitative Risk Analysis Examples 

Here are two examples of how to determine the EMV of a risk.

Example 1: 

Assume you have bought an off-the-rack software, though you have risk linked to customization. There is a 60% chance that you will have to do only a little customization, which would bring the total cost to $ 120,000. And there is a 40% chance that you will have to do a lot of customization, which would bring the total cost to $160,000. To calculate the expected value, add the values of each alternative.

EMV= probability x cost = (60% x $120,000) + (40% x 160,000)

=$20,000 + $ 40,000

EMV= $60,000.

Example 2:

A company may have a risk probability that may result in the laying of workers. In this case, there is a 50% chance that you will keep the staff which would bring the total cost to $100,000. On the other hand, there is a 50% chance that you will lose staff which would bring the total cost to $200,000. Calculate the EMV.

EMV= Probability x cost = (50% x $100,000) + (50% x $200,000)

= $20,000 + $40,000

EMV= $60,000

Quantitative Risk Analysis Benefits & Limitations

Some of the advantages of quantitative risk analysis are: to determine the likelihood of accomplishing a particular project objective. Quantify the risk revelation of the project, and determine the scope of cost and schedule possibility that may be required. Risk is organized by their financial effect, possessions by their financial value. The outcomes can be articulated in detailed management terminology. The safety level is better determined grounded on the three basics: accessibility, veracity, and privacy. A cost analysis can be executed for choosing the best-suited procedures. Data precision progresses as the organization increases the experience.

On the other hand, Quantitative risk analysis has limitations: the approaches of calculation are complex and without an automatic tool the progression can be really difficult to compute. There are no values and generally accepted information for executing this method. The values of risk effects are founded on the subjective sentiments of the people involved. The process handles a long time. The outcomes are offered only in monetary values and are hard to comprehend by persons without experience. In general, the process is very complex.


If the outcomes of quantitative risk analysis are well comprehended and the right procedures are executed, the organization will not only vanish from the market but also advance and more easily gain the targeted outcomes. Risk identification should be done with greater maintenance, and all risks must be recognized and treated cautiously. The estimation and calculation of probable threats, susceptibilities, and possible harm are very important. After this valuation is done, essential controls should be applied in terms of cost efficiency and level of risk reduced by the execution. To recognize the most suitable controls a cost analysis has to be done. Its outcomes aid managers implement the most effective controls that bring the greatest profit to the organization.