Kevin Ian Schmidt

The Business of Safety

There are two methods often used to assess business risks, quantitative and qualitative. According to research, most businesses utilize the quantitative assessment model in order to identify and categorize probable problems with their business practices. A quantitative risk assessment approach means that numbers are used to calculate and predict hazards. Each hazard is then prioritized according to the level of their risk value. The higher the risk value, the higher it is in the priority totem pole.

On the other hand, a qualitative risk assessment uses characteristics of each scenario to determine a course of action. Unlike the quantitative approach, the qualitative methodology of risk assessment can be subjective and require more work than the former.

Crunching Numbers of Risk

To determine the value of risk (R), two critical components—loss (L) and probability (p)—are calculated to arrive at a final numerical value. The component loss (L) represents the amount of loss incurred in the event of an accident, encompassing everything from financial costs to physical damages and injuries. The probability (p) component refers to the likelihood or chance of a specific scenario occurring. Multiplying these two values (L and p) provides the product (R), which represents an objective assessment of potential risks.

Quantitative risk assessments are particularly useful in complex situations, where they can offer a clear, numerical representation of risk. These situations often involve severe consequences, such as the potential loss of life, damage to machinery, and significant environmental impacts. By using quantitative methods, organizations can make more informed decisions about risk management and mitigation strategies, ensuring that all potential hazards are thoroughly evaluated and addressed.

Check Out: How to Conduct a Risk Assessment

Success and Losses Based on Tested and Quantified Statistics

Critics of this practice have expressed concerns about its reductive and purely numerical nature. Notable figures such as Barry Commoner and Bryan Wynne have criticized its reductive approach, arguing that it fails to capture the nuanced differentiation available in qualitative risk assessments. These detractors maintain that numerical values cannot fully describe hazard scenarios as effectively because they often omit the human element, which is crucial for understanding the broader context and implications of risks.

However, while these criticisms have merit, qualitative risk assessments can be time-consuming and costly. In contrast, quantitative methods provide a more efficient way to evaluate potential hazards in the workplace, offering sufficient breadth and definition through statistical analysis. Quantitative assessments are particularly beneficial in scenarios where clear patterns and historical data exist, as they can provide a more objective and streamlined evaluation of risks. This objectivity often makes quantitative analysis superior to the more tedious and subjective nature of qualitative assessments, particularly in environments where swift and clear decision-making is essential.

Qualitative Risk Analysis

The purpose of qualitative risk analysis is to identify which risks require detailed analysis and to determine the necessary controls and actions based on the risks’ effects and impacts on objectives. This approach is particularly useful for prioritizing risks and developing an initial response strategy. Two simple and well-known methods are commonly applied in qualitative risk analysis:

1. Keep It Super Simple (KISS)

The KISS method is ideal for small or narrowly scoped projects where unnecessary complexity should be avoided. This approach is especially beneficial for teams that lack maturity or extensive experience in assessing risk. The KISS method involves a straightforward, one-dimensional technique for rating risk on a basic scale, such as:

  • Very High
  • High
  • Medium
  • Low
  • Very Low

By keeping the assessment simple, teams can quickly identify and categorize risks without becoming overwhelmed by intricate details, ensuring that even less experienced teams can effectively participate in the risk management process.

2. Probability/Impact Analysis

The Probability/Impact method is suited for larger, more complex projects and issues, involving teams with experience in risk assessments. This two-dimensional technique rates risks based on two key factors:

  • Probability: The likelihood that a risk will occur.
  • Impact: The consequence or effect of the risk, typically related to its impact on schedule, cost, scope, and quality.

In this method, both probability and impact are rated using a numerical scale, such as 1 to 10 or 1 to 5. The risk score is then calculated by multiplying the probability rating by the impact rating. For example, a risk with a probability of 3 (on a scale of 1 to 5) and an impact of 4 would have a risk score of 12.

This method allows for a more nuanced assessment of risks, enabling teams to prioritize risks based on their potential severity and likelihood. It helps ensure that resources are allocated to address the most significant risks first, improving the overall effectiveness of the risk management process.

Benefits of Qualitative Risk Analysis

By employing these methods, qualitative risk analysis provides several advantages:

  • Prioritization: Helps identify which risks need immediate attention and which can be monitored or addressed later.
  • Simplicity and Accessibility: The KISS method makes risk assessment accessible to all team members, regardless of their experience level.
  • Comprehensive Evaluation: The Probability/Impact method offers a detailed evaluation, suitable for complex projects with multiple stakeholders.

Overall, qualitative risk analysis is a vital tool in the risk management toolkit, offering both simplicity for straightforward projects and depth for more complex scenarios. It ensures that risks are systematically identified, assessed, and managed, aligning with project objectives and organizational goals.

Quantitative Risk Analysis

Quantitative risk analysis is an advanced method used to evaluate high-priority and high-impact risks by assigning numerical or quantitative ratings. This approach develops a probabilistic assessment of business-related issues, translating the probability and impact of risks into measurable quantities. Its application is more limited and depends on factors such as the type of project, project risk, and the availability of relevant data for analysis.

Purpose and Benefits

The primary purpose of quantitative risk analysis is to provide a detailed, measurable evaluation of risks. This method:

  • Quantifies Outcomes: It quantifies the possible outcomes for business issues and assesses the probability of achieving specific business objectives.
  • Supports Decision-Making: It provides a quantitative approach to making decisions in the face of uncertainty.
  • Sets Realistic Targets: It creates realistic and achievable cost, schedule, or scope targets.


Quantitative risk analysis is particularly useful in several contexts, including:

  • Schedule and Budget Control: Essential for business situations that require meticulous schedule and budget control planning.
  • Complex Projects: Ideal for large, complex issues or projects that require critical go/no-go decisions.
  • Management Insights: Valuable for business processes or issues where upper management demands detailed information about the probability of on-time and within-budget completion.


The advantages of using quantitative risk analysis are numerous:

  • Objectivity: Provides an objective assessment of risks.
  • Management Tool: Serves as a powerful selling tool to management by clearly illustrating risks and benefits.
  • Cost/Benefit Projection: Allows for direct projection of cost and benefit, aiding in financial planning.
  • Flexibility: Can be tailored to meet the needs of specific situations and industries.
  • Reduced Disagreement: Less likely to provoke disagreements during management reviews, as it is based on quantifiable data.
  • Fact-Based Analysis: Often derived from irrefutable facts, enhancing the credibility of the analysis.

In summary, quantitative risk analysis is a robust tool for evaluating risks in a structured, numerical manner. It supports informed decision-making, realistic target setting, and provides a clear, objective view of potential outcomes, making it indispensable for complex projects and situations where precise risk assessment is crucial.

Minimizing Loss

Quantitative risk assessments are not absolute, despite their seemingly definitive nature. The quantities attributed to loss (L) and probability (p) are not entirely fixed or certain, and as these values increase, there is a higher risk of arriving at inaccurate conclusions. However, with accurate data and careful calculation, predicting potential outcomes and associated costs is a risk worth taking for any business.

The quantitative method assigns a numerical value to signify “risk,” yet it is important to recognize that not all hazards are equivalent. Different scenarios can yield the same risk value; for example, a scenario with a low loss and high probability versus one with a high loss and low probability. In such cases, businesses must conduct a feasibility study to determine which hazard requires immediate action. Typically, businesses prioritize scenarios where the overall loss is minimal, ensuring that resources are allocated efficiently to mitigate risks.

Quantitative risk assessments provide a clear and tangible representation of risk. By assigning numerical values to potential hazards, solutions can also be quantified. This approach allows business owners and investors to rely less on trial-and-error methods, which could incur additional losses if unsuccessful. Using equations to determine the probability of failure and success offers a structured and data-driven approach to risk management, facilitating more informed decision-making and potentially reducing the overall impact of risks.

Low-Cost High-Reward Investment

Implementing safety precautions at work should be at an overall low-cost and high-reward situation. By using numerical data to determine which course of action is the most beneficial, business can get ahead of the risk game and actually win. In this case winning means minimal losses and increase in business profitability.

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The Business of Safety


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