What is Sensitivity Analysis in Finance?

Financial forecasting and modelling is all about trying to predict the future of your business – and sensitivity analysis is just a single part of that. If you’ve just created your financial forecast, then sensitivity analysis is the next logical step in planning your business’ future.

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What is sensitivity analysis?

Sensitivity analysis is a method used across different industries to understand how changes in variables or assumptions affect the results of a model, system, or decision. It helps businesses to see the connection between input variables and output results and how uncertainties  in those variables can change the outcomes.

In simpler terms, sensitivity analysis helps us figure out which factors have the biggest impact on our results and how small changes in those factors can affect what we’re trying to achieve.

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What is sensitivity analysis used for?

Sensitivity analysis is a versatile technique with several applications. It is used in:

• Assessing the impact of changes in variables or assumptions on the outcomes of a model, system, or decision
• Gaining understanding of the relationships between input variables and output results
• Analyzing how uncertainties or variations in variables can influence the final outcomes
• Supporting decision-making processes by providing insights into the effects of different factors
• Identifying critical factors that have a significant impact on the results
• Enhancing awareness of model limitations and potential risks associated with the analysis.

How does sensitivity analysis work?

Here’s a simplified explanation of how sensitivity analysis typically operates:

1. Identify input variables: First, you need to identify the variables or assumptions that have an impact on the model or system you are analyzing. These are the factors that you want to examine in terms of their influence on the output.
2. Define the range: Determine the range or values that each input variable will take during the sensitivity analysis. This range can be based on expert judgment, historical data, or other relevant information.
3. Select a method: Choose a specific sensitivity analysis method based on your objectives. Common methods include one-way sensitivity analysis, multi-variable analysis, tornado diagrams, or Monte Carlo simulations.
4. Analyze the variations: Apply the chosen method to evaluate the effects of varying the input variables. This involves running the model multiple times while changing one variable at a time or simultaneously changing multiple variables.
5. Observe the output changes: Monitor and record the resulting changes in the output measures of each variation of the input variables. This allows you to see how the output is influenced by different values or assumptions.
6. Interpret the results: Analyze the collected data to identify trends, patterns, and relationships between input variables and output results. Determine which variables have the most substantial impact on the outputs and understand how changes in these variables affect the overall outcomes.
7. Draw conclusions: Based on the sensitivity analysis results, draw conclusions about the reliability, and stability of the model or system. This information can guide decision-making, risk assessment, and further analysis or adjustments.

Sensitivity analysis helps to enhance understanding of the relationships and dependencies between variables, aiding decision-makers in making informed choices and managing uncertainties.

An example of sensitivity analysis

Suppose you are a project manager planning to launch a new product. You have created a financial model that estimates the project’s profitability based on several input variables. These variables include the selling price of the product, the production cost per unit, the sales volume, and the marketing expenses.

To perform sensitivity analysis, you decide to vary each of these input variables to assess their impact on the project’s profitability. Here’s how the analysis may unfold:

1. Selling price: You start by analyzing the sensitivity of the selling price. You choose a range of possible prices, such as $50, $60, and $70 per unit, and evaluate the profitability for each price point.
2. Production cost per unit: Next, you examine the sensitivity of the production cost per unit. You consider different cost scenarios, such as $20, $25, and $30 per unit, and analyze the impact on profitability.
3. Sales volume: Moving on, you investigate the sensitivity of the sales volume. You explore various sales projections, such as 1,000 units, 1,500 units, and 2,000 units, and observe the profitability for each volume.
4. Marketing expenses: Lastly, you explore the sensitivity of marketing expenses. You consider different marketing budget allocations, such as $10,000, $15,000, and $20,000, and evaluate the corresponding impact on profitability.

By conducting sensitivity analysis on these variables, you can identify which factors have the most significant influence on the project’s profitability. This information helps you make informed decisions, prioritize your focus on key factors, and develop contingency plans to manage uncertainties effectively.

Sensitivity analysis vs scenario analysis

Sensitivity analysis and scenario analysis are both techniques used to assess the impact of changes or variations on the outcomes of a model or system. While they have some similarities, there are distinct differences between the two:

• Focus: Sensitivity analysis focuses on examining the impact of changes in individual input variables on the model’s outputs. It aims to understand the relationships between specific variables and the outcomes. In contrast, scenario analysis focuses on exploring different sets of input values or assumptions together, creating different scenarios to understand their combined impact on the outputs.
• Variation approach: Sensitivity analysis typically involves systematically varying one input variable at a time while keeping others constant, allowing for a more isolated analysis of each factor’s influence. Scenario analysis, on the other hand, involves creating and analyzing multiple scenarios by simultaneously changing multiple input variables, considering different combinations of values or assumptions for a holistic analysis.
• Range of possibilities: Sensitivity analysis often focuses on exploring a specific range of values for each input variable to understand how the output responds. In contrast, scenario analysis considers a broader range of possible scenarios, each with its own set of input values, to capture a wider spectrum of potential outcomes.
• Purpose: Sensitivity analysis primarily aims to identify the most influential factors and quantify their impact on the model’s outputs. It helps understand the model’s sensitivity to changes in input variables and supports decision-making and risk assessment. Scenario analysis, on the other hand, is more focused on exploring different plausible future scenarios and assessing their potential impact on the outcomes. It helps in evaluating the model’s robustness under different conditions and aids in strategic planning and contingency preparation.

In practice, sensitivity analysis and scenario analysis can be complementary and used together. Sensitivity analysis can provide detailed insights into the impact of individual variables, while scenario analysis allows for a broader examination of different combinations of variables to explore a range of potential outcomes. The choice between the two techniques depends on the specific objectives, available data, and the complexity of the model or system being analyzed. Take a look at the features of a scenario planning software today.

Sensitivity analysis advantages

Sensitivity analysis offers several advantages that make it a valuable tool for decision-making and analysis. Here are some key advantages of sensitivity analysis:

• Identifies critical factors: Sensitivity analysis helps identify the input variables that have the most significant impact on the model or system outputs. This allows decision-makers to focus their attention and resources on the most influential factors.
• Quantifies relationships: By systematically varying input variables and observing output changes, sensitivity analysis provides a quantitative understanding of the relationships between inputs and outputs. It helps quantify the degree of influence that each variable has on the results, enabling better assessment of potential risks and opportunities.
• Enhances robustness: Sensitivity analysis helps assess the robustness of a model or system. By identifying the variables that have the most significant impact, decision-makers can understand the potential vulnerabilities and uncertainties associated with the system, allowing for improved planning and risk management.
• Supports decision-making: Sensitivity analysis provides valuable insights into the potential outcomes associated with different variables or assumptions. It helps decision-makers understand the potential risks, benefits, and uncertainties associated with alternative courses of action, facilitating informed decision-making.
• Enables scenario exploration: Sensitivity analysis can be extended to explore multiple scenarios by varying multiple input variables simultaneously. This allows decision-makers to evaluate different combinations of variables and understand the range of potential outcomes under various conditions, enabling better scenario planning and analysis.
• Improves communication: Sensitivity analysis enables effective communication of complex relationships and uncertainties to stakeholders, promoting a better understanding of the analysis results and supporting collaborative decision-making.

Overall, sensitivity analysis enhances understanding, quantifies relationships, supports decision-making, and improves the robustness of models and systems. Its advantages make it a valuable tool for assessing the impact of input variables and assumptions on outcomes, helping to make more informed and effective decisions.

Sensitivity analysis disadvantages

While sensitivity analysis offers various advantages, it also has some limitations and potential disadvantages. Here are a few considerations to keep in mind:

• Simplifying assumptions: Sensitivity analysis often involves simplifying assumptions, such as holding other variables constant while varying one at a time. This simplification may not fully capture the complex interactions and dependencies among variables.
• Limited scope: Conducting sensitivity analysis on a limited number of variables may overlook important factors that could significantly impact the outcomes. If key variables are omitted or if the analysis does not capture all relevant uncertainties, the results may not accurately represent the real-world complexity.
• Linear relationships: Sensitivity analysis assumes linear relationships between variables and outcomes, which may not hold true in all cases. Nonlinear relationships and complex interactions among variables can lead to more intricate dynamics that sensitivity analysis alone may not fully capture.
• Lack of probabilistic information: Sensitivity analysis often focuses on deterministic changes in input variables, disregarding the probabilistic nature of uncertainties. This limitation can be addressed by integrating probabilistic methods, such as Monte Carlo simulation, into sensitivity analysis to account for the distribution and variability of input variables.
• Limited guidance for decision-making: While sensitivity analysis provides insights into the relative importance of variables, it may not offer clear guidance on specific actions or decisions. It highlights which variables have a significant impact, but additional analysis and judgment are often required to determine the most appropriate course of action.
• Data limitations: The quality and availability of data for sensitivity analysis can be a challenge. Lack of accurate or comprehensive data on input variables may affect the reliability and validity of the analysis results.
• Unrealistic assumptions: Sensitivity analysis relies on certain assumptions, such as linear relationships or static conditions, which may not always align with the real-world complexities of the system or model being analyzed. These assumptions can limit the applicability and accuracy of the analysis.

It is important to recognize these limitations and consider them when interpreting the results of sensitivity analysis. Sensitivity analysis should be used in conjunction with other analytical techniques and tools to gain a comprehensive understanding of the system or model under study.

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Sensitivity analysis in Brixx

Brixx allows users to create detailed financial models and perform various analyses, including sensitivity analysis, to assess the impact of changes in input variables on financial outcomes.

Within Brixx, you can define different scenarios by varying input variables and observing the resulting changes in the projected financials. By specifying ranges or specific values for variables like sales volume, prices, costs, or other relevant factors, you can analyze how these changes affect key financial metrics such as revenue, profit, cash flow, or valuation.

Brixx’s interface allows you to specify different values or ranges for the variables of interest. It then automatically calculates and presents the corresponding outcomes based on the defined scenarios. This allows you to explore the sensitivity of your financial forecasts to changes in different input variables, helping you understand the potential risks, opportunities, and uncertainties associated with your financial projections.