sensitivity analysis of business plan

Sensitivity Analysis Explained: Definitions, Formulas and Examples

Sensitivity analysis is an indispensable tool utilized in corporate finance and business analysis to comprehend how the variability in key input variables influences the performance of a business. By methodically adjusting the inputs and observing the ensuing effect on outputs, analysts can discern which variables have the most profound impact on the bottom line. This enables companies to concentrate on managing the most sensitive factors to enhance profitability and mitigate risk.

Article Contents

What is a sensitivity analysis, sensitivity analysis formula, how to do a sensitivity analysis in excel, sensitivity analysis methods, advantages and disadvantages of sensitivity analysis, exercises and examples for sensitivity analysis, key takeaways, sign-up for our free sensitivity analysis template.

A sensitivity analysis measures how susceptible the output of a model is to alterations in the value of the inputs. It aids in identifying which input variables drive most of the variation in the output. For example, in a financial model measuring a company’s profitability, key inputs typically encompass sales growth, cost of goods sold, operating expenses, interest rates, inflation and tax rates. By increasing and decreasing each of these inputs and observing the impact on profits, you can determine which inputs are most sensitive – where minor changes instigate major swings in profits.

While there isn’t a single formula for sensitivity analysis, the general approach is to select an input, modify it by a specified amount, and ascertain the impact on the output. Analysts typically vary inputs up and down by a fixed percentage, such as 10%, to assess sensitivity. The simplistic formula is:

New Output = Base Output x (1 + Change in Input)

For instance, if revenue is amplified by 10% from $100 to $110, the formula is:

New Profit = Base Profit x (1 + 10%) = Base Profit x 1.10

Note: This formula represents a straightforward scenario and actual scenarios may exhibit more complex relationships between input changes and output results.

Typically, in reviewing client forecasts as a credit analyst, the “base case” provided by the client will show steady growth in sales and margins.  The analyst will typically sensitise this, making a no growth and no margin improvement case, to see if debt ca still be serviced satisfactorily. A separate Combined downside will also typically be modelled where the company is deemed to have experienced difficult trading such as might occur in a recession.

Data services like S&P Capital IQ and FactSet allow analyst to look back and see exactly how variable sales and margins have been in previous recessions.  This can provide a very concrete and rational basis for designing a “downside/recession” scenario.

Excel is a practical tool for conducting sensitivity analysis. Here are the general steps:

• Build a financial model to calculate the baseline output, such as net income.
• Create input variables for the major value drivers, like unit sales, price per unit, variable costs per unit, fixed costs , tax rate, etc.
• Save a copy of the baseline model. Then change one input variable at a time by a fixed amount, like 10%. Recalculate the new output.
• Repeat step 3 for each input variable. Record the new output values each time.
• Compare the range of outputs to determine which inputs had the greatest impact. Produce charts in Excel to visualize the sensitivity analysis.
• Optionally, automate the process using Excel Data Tables.
• More complex inputs can be modelled in Excel using tools like index or choose together with data validation or VBA tools such as combo boxes.

Below, we’ve created an example of a Sensitivity Analysis for an operating income statement, using Excel’s data analysis functions to perform the analysis:

To implement the sensitivity analysis DATA TABLE:

• Input a cell reference for the operating income (=D14) in as the starting value for the table (D17), and your sensitivity variance factors in below (C18 to C21).
• Select your sensitivity factors and operating income column (C17:D21)
• Navigate the Excel menu ribbon to Data, What if analysis, Table, and you will see the following dialog box.

• Input the cell for your initial Sensitivity Factor (D9) into the “Column Input cell box”. Press OK.

Excel will then perform your sensitivity analysis: it will take your sensitivity factors (from C18 to C21) one by one, enter them into your given sensitivity factor (D9) and then return the corresponding result from (D17, the cell at the top of the table). It will output the result into the cell next to the input tested. Try them out individually by typing them one by one into D9 using the initial table.

There are several common methods and techniques for performing sensitivity analysis:

• One-at-a-time (OAT) analysis: Alter one input variable while maintaining others constant. This method is straightforward but can miss interactive effects between variables.
• Differential analysis: Calculate the rate of change in output based on minute changes in input, thereby allowing ranking of sensitivity.
• Scenario analysis: Adjust multiple inputs simultaneously to model various scenarios, like worst-case and best-case, which offers a spectrum of possible outcomes.
• Monte Carlo simulation: Utilize repeated random sampling of input variables to generate a probability distribution of potential outcomes. This is especially useful for models incorporating uncertainty.
• Tornado diagrams: Graphically illustrate the sensitivity ranking of inputs. The wider the bar, the larger the impact.

Advantages:

• Identifies pivotal value drivers upon which to focus management attention.
• Helps in quantifying the risk in a project or forecast.
• Guides decisions and mitigates risk.
• Explores scenarios and formulates contingency plans.
• Enhances comprehension of the nature of the key success variables.
• Static analysis might overlook dynamic interactions.

Disadvantages:

• Can be time-consuming when testing numerous scenarios.
• Necessitates resources and specialized skills.
• Does not optimize inputs.
• Limited to model inputs, even if the model itself is incomplete or inaccurate.

Here are some examples to practice conducting sensitivity analysis:

• A company has fixed costs of $100,000. Unit variable costs are $50, and units sold are projected at 5,000
• Calculate operating income sensitivity to a 5%, 10%, and 15% variation in units sold.
• A loan has a principal of $500,000, an interest rate of 6%, and a term of 10 years. Calculate the sensitivity of total repayments to a 0.5%, 1%, 1.5% change in interest rate.
• An oil company’s net income is based on revenue of $2 million, operating costs of $1.2 million, and a tax rate of 40%. Test sensitivity to 10% changes in revenue, costs, and tax rate.
• For a capital budgeting project with: NPV = -$1250, Investment = $5000, Lifespan = 5 years, and Discount Rate = 15%, determine the sensitivity of NPV to changes in each input.

Sensitivity analysis is a critical financial modelling technique in the sphere of corporate finance. By discerning which inputs have the most substantial impact on outcomes, companies can hone their efforts on the value drivers that matter most. Performing sensitivity analysis leads to better-informed, data-driven decisions, providing a structured approach towards understanding financial variability and risk.

Learn Essential Skills Needed to Build Robust Financial Models

Sensitivity analysis faqs, what is an example of a sensitivity analysis.

A sensitivity analysis is a technique used to determine how changes in the values of input variables affect the output or outcome of a model or decision. A common example is varying the interest rate assumptions in a financial model to see how it impacts the net present value or internal rate of return.

How do you conduct a sensitivity analysis?

To conduct a sensitivity analysis, you typically:

• Identify the key input variables that have the greatest impact on the output.
• Determine the likely range of values for those input variables.
• Systematically change the values of the input variables within their ranges and observe the resulting changes in the output.
• Analyze the sensitivity of the output to changes in each input variable.

What is a sensitivity analysis for P&L?

A sensitivity analysis for a profit and loss (P&L) statement involves examining how changes in revenue, expenses, or other key factors would impact the overall profitability of a business. This can help identify the most critical drivers of financial performance and inform strategic decision-making.

What is DSS sensitivity analysis?

DSS stands for Decision Support System. A DSS sensitivity analysis is the process of evaluating how changes in the input variables of a decision support system model affect the outputs or recommended decisions. This helps quantify the uncertainty and risk associated with the model’s recommendations, allowing decision-makers to make more informed choices.

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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.

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.

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:

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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:

• 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.
• 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.
• 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.
• 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.

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.

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A Guide on Sensitivity Analysis for Startup Founders

• November 6, 2022

Sensitivity analysis is an integral part of financial modeling and business planning. It helps startups analyze how different values of an independent variable will impact a dependent variable under a given set of assumptions.

However, many startup founders are unfamiliar with sensitivity analysis and its potential benefits. As a result, they often make decisions without considering how sensitive their business plans are to changes in key assumptions.

The guide will introduce startup founders to sensitivity analysis and its usage. Keep reading to learn more.

What Is Sensitivity Analysis?

Sensitivity analysis studies how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided into different sources of uncertainty. A model is said to be sensitive to an uncertain parameter if a small change in that parameter results in a significant change in the model output.

In the context of financial modeling, sensitivity analysis shows how the value of a financial model output changes in response to changes in certain inputs, known as drivers. Some drivers include interest rates, commodity prices, exchange rates, and company-specific variables such as revenue growth.

How Does Sensitivity Analysis Work?

Sensitivity analysis is also often termed a what-if analysis. The name is apt since it is often used to analyze what would happen to the results of a given decision if one or more assumptions underlying the original decision were changed.

An early-stage startup can use sensitivity analysis in its financial models to do the following:

• Forecast : Since a startup is typically a high-growth company, its financial situation can change rapidly. By plugging in different values for key drivers of revenue and expenses, a startup can get a better idea of how its financial situation might change in the future.
• Plan for Different Outcomes : A startup can use sensitivity analysis to see how different changes in assumptions might affect its financial forecast. What if the marketing campaign doesn’t deliver the expected results? What if a government regulation impacts business processes? It can help the startup plan for different outcomes and make more informed decisions.

So how does this work? The basic idea is to vary one or more inputs to a model and see how the outputs change. For example, in a financial model, the inputs might be assumptions about revenue, expenses, and interest rates. The outputs might be profit, cash flow, or return on investment.

To do a sensitivity analysis, you must choose the inputs you want to vary. Then, you need to decide how much you want to vary them. For example, you might want to increase revenue by 10% and decrease expenses by 5%.

Once you’ve chosen the inputs and the amount you want to vary them, you can run the model with the new inputs and see how the outputs change.

Benefits of Sensitivity Analysis

Sensitivity analysis can help startups make their financial models more dynamic. Here are some benefits of sensitivity analysis.

Helps Determine Impactful Variables

When inputs in a financial model are changed, not all changes will have an equal impact on the model’s results. Sensitivity analysis can help determine which inputs impact the model most. It can help startups focus on the most important inputs and make more accurate predictions.

Allows for Reliable Predictions

Since sensitivity analysis considers multiple scenarios, it can give startup founders a more accurate idea of what to expect. For instance, what will happen if the number of customers decreases? What if costs go up?

Making reliable predictions is imperative for startup success. It also helps create pitch decks that convey the most accurate story to potential investors.

Creates a Cohesive Financial Model

No investors will be confident in your business if you only present a single scenario. They want to see best and worst-case scenarios. They want to know that you’ve thought about the potential risks and rewards of your business.

Sensitivity analysis helps startups create a cohesive financial model by running multiple scenarios. In addition, it gives startups a well-rounded view of their business, which is essential for attracting investors.

Limitations of Sensitivity Analysis

While sensitivity analysis is a powerful tool, it does have limitations. Here are some of them.

Doesn’t Account for the Probability

A sensitivity analysis shows you how far a variable must change to give a certain output. However, it doesn’t consider the probability of this change occurring.

For example, a sensitivity analysis might show that a 10% increase in the price of a product will lead to a 5% decrease in demand. However, there is no indication of how likely this scenario is.

Is Not Relative

In most cases, a sensitivity analysis only focuses on a single variable. However, in real-world scenarios, variables are interconnected.

For instance, inflation is connected to wages, which is, in turn, connected to the cost of living. However, a sensitivity analysis of inflation would not take this into account. It would only focus on the effects of inflation without considering the other variables. That can lead to oversimplified results that don’t reflect reality.

Requires Detailed Data

To be effective, a sensitivity analysis requires detailed data. It can be difficult and time-consuming to obtain so much information.

Should You Include Sensitivity Analysis In Your Financial Model?

As a startup, you must look for ways to optimize your financial model. One way to do this is through sensitivity analysis, which allows you to see how changes in certain variables impact your business.

Therefore, you should include it in your financial model. But then again, a sensitivity analysis for a startup is different from that of an established business. Thus, you must take a custom route rather than a generic approach.

Incorporate Sensitivity Analysis in Your Custom-Made Financial Model

Customized financial models are more comprehensive than generic ones since they consider your specific business situation and goals. At Numberly , we create financial models tailored to your level of maturity and financial situation.

Since our models forecast cash flow requirements well in advance, there’s no room for surprises. Plus, our models answer all the ‘what if’ questions stakeholders may have about your business.

The dashboard has all the ready-to-go KPIs that you can present to potential investors to show your company’s progress. Schedule a 30-minute free call with our experts to learn more about how we make financial modeling a breeze for early-stage founders.

Get investor-ready with a simple and easy to follow, yet fully customized financial model.

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Understanding Sensitivity Analysis: A Comprehensive Guide

Introduction

If you’re making critical decisions, it’s critical to understand the sensitivity of the key variables and inputs. This is where sensitivity analysis comes in. Sensitivity analysis is a powerful tool that allows you to test “what-if” scenarios by systematically varying key inputs or assumptions, usually to see how a given output of interest is affected. In this article, we’ll take a comprehensive look at sensitivity analysis, including its importance, its methodology, and best practices.

The Importance of Sensitivity Analysis in Decision Making

One of the most important things to understand about sensitivity analysis is how it can help with decision making. Sensitivity analysis provides an opportunity to evaluate how a change in one input affects an output of interest. By better understanding the relationship between inputs and outputs, decision-makers become equipped to make more informed and confident decisions.

For example, if you were making a decision about a manufacturing investment, sensitivity analysis would be useful for testing the effects of factors such as raw material costs, labor, and tariffs. By conducting a sensitivity analysis on these variables, you can determine which variable has the greatest impact, which variables you have control over, and the appropriate steps to take to mitigate risks and optimize your outcomes.

Another way sensitivity analysis is helpful is in assessing the potential consequences of not conducting the analysis. For instance, if a company made a critical business decision without conducting sensitivity analysis on the risks, it could lead to disastrous consequences such as financial losses, missed opportunities, and reputation damage.

Breaking Down Sensitivity Analysis: A Beginner’s Guide

If you’re new to sensitivity analysis, you might be intimidated by the complex terminology and concepts involved. However, with the right guidance, you’ll soon find that sensitivity analysis is a straightforward process. Essentially, sensitivity analysis involves modifying the inputs of a model or a decision-making process to see how these changes affect the output or outcome.

Some key terms to understand in sensitivity analysis include:

• Inputs: The variables that are used to determine the outcome of the model or decision-making process
• Outputs: The results or outcomes that are generated from the model based on the inputs
• Scenarios: The sets of different values that are used to test how the model responds to different input values
• Parameters: The values of inputs or outputs that are used to summarize the results after running the sensitivity analysis
• Range: The minimum and maximum values of the input variables. These values are usually used to define the scenarios used in the sensitivity analysis.

There are several types of sensitivity analysis, including:

• One-way analysis: Varies one input variable while holding all others constant. This analysis creates a single output for each input variable that is manually varied.
• Two-way analysis: Varies two input variables simultaneously while holding the others constant. This analysis creates a two-dimensional graph that displays the results with respect to the two input variables.
• Monte Carlo simulation: A stochastic (randomized) analysis that evaluates the impact of uncertain inputs within a statistical planning model.
• Tornado diagrams: Also referred to as “bar charts,” show the sensitivity of outputs to different input variables, with the top of the chart indicating the variable that has the greatest impact on the output.

Properly Conducting a Sensitivity Analysis: Best Practices

Knowing the methodology and types of sensitivity analysis is only the first step. To conduct one properly, there are several best practices to be wary of. These include:

• First, identify the assumptions and inputs that influence the outcome of the decision.
• Second, select the appropriate type of sensitivity analysis that best fits your model and scenario, ensuring that the input ranges used in the sensitivity analysis are practical and reasonable in terms of potential outcomes.
• Third, hold constant all variables other than the ones you are testing. Otherwise, you would have no clear idea which variable is influencing your chosen outcome and thus would misinterpret your results.
• Fourth, sketch your results in the format that best suits your decision-making process, which could be a line graph, chart, or table.
• Finally, review the results carefully and determine the most appropriate course of action based on the conclusions drawn from the data output.

The key to properly conducting a sensitivity analysis is to be systematic and thorough, taking into account all the potential ramifications or influences on the model.

When to Use Sensitivity Analysis in Your Business Plan

Once you’re familiar with sensitivity analysis, the next step is to understand when it is appropriate to use it in your business plan. The best time to use sensitivity analysis is when there are high levels of uncertainty related to critical factors that may affect your business plan. By performing a sensitivity analysis, you can better assess how these factors are likely to impact the business, and therefore what measures you can take to mitigate these risks.

Sensitivity analysis can be particularly useful in forecasting potential outcomes, managing risks in the business plan, and making more logical financial models. For example, if you were planning on opening a new store, you would want to conduct sensitivity analysis to determine the impact of factors such as location, competition, and customer retention. You can then adjust the business plan as necessary to address any weaknesses that come to light.

Take the example of a restaurant chain that’s planning on opening a new location. Using sensitivity analysis, the restaurant can determine how changes in variables like customer volume, menu prices, and rent will impact profitability, providing a more accurate projection of the success of the new branch.

Predicting Outcomes: How Sensitivity Analysis Can Help Your Projects

In addition to its use in business planning, sensitivity analysis can be used to improve the outcomes of your projects. When project plans are developed, there is always some level of uncertainty regarding factors such as timing, resources, costs, and team competency. By performing sensitivity analysis during the planning process, you can better predict possible outcomes, giving you options to adjust your plans, mitigate risks, and ultimately increase your chances of achieving success.

Sensitivity analysis can help in risk assessment, resource allocation, budget constraints, and solving potential issues before they become major setbacks. For example, a construction company that’s building a new highway system can use sensitivity analysis to assess the potential costs and benefits of different routes, material choices, and project phases. By taking these factors into account, it increases the probability of a project being completed on budget, on time, and with a higher degree of success.

Sensitivity Analysis in Financial Modeling: A Comprehensive Guide

While sensitivity analysis is useful in any context where decision-making is involved, it is especially prevalent in financial modeling. Many financial models rely on complex calculations and estimations that require the use of different variables, such as exchange rates, inflation rates, and interest rates. By running different scenarios, a sensitivity analysis can provide a range of potential outcomes, helping businesses plan for various contingencies.

The most common sensitivity analyses used in financial modeling include scenario analysis and stress testing. Scenario analysis involves the development of different scenarios or variations of a particular decision or input. Stress testing, on the other hand, involves applying severe changes or shocks to different inputs simultaneously to determine the impact on the output.

When preparing final reports in financial modeling, it’s critical to do a thorough sensitivity analysis and ensure that it covers all key inputs. This must be presented in a format that’s easy to understand and intuitive for the intended audience, such as a report or a graph.

In conclusion, sensitivity analysis is a powerful tool that can help individuals make informed decisions, mitigate risks, and create more accurate projections across a wide range of applications. Whether it’s used in business planning, project management, or financial modeling, sensitivity analysis is a valuable technique that provides a deeper understanding of how changes to key inputs or assumptions can affect the outcomes of your decision-making process. It is essential to conduct and understand sensitivity analysis for anyone looking to make the best decisions possible.

So, don’t be afraid to try it on your own – you’ll never know unless you try. With the right guidance and resources, anyone can learn how to use sensitivity analysis and improve their decision-making abilities.

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From Planning to Valuation: Mastering Business Planning and Sensitivity Analysis for Your Startup

• First Online: 26 September 2023

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• Sinem Derindere Köseoğlu 2  

Part of the book series: Contributions to Finance and Accounting ((CFA))

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Starting a new business can be a daunting task, but having a solid business plan, building a good financial model, and performing sensitivity analysis can help mitigate risks and increase the chances of success. In this chapter, I discuss the importance of business planning and sensitivity analysis in startup valuation, as well as how to develop an effective business plan and build a financial model.

The chapter starts by defining the key components of a business plan, including the executive summary, market analysis, marketing strategy, financial projections, and implementation plan. The chapter also provides tips on how to write each section effectively, and how to tailor the plan to the needs of the startup and its target audience. Creating a solid business plan, building a financial model, and performing sensitivity analysis are critical for startups to succeed, but it can be challenging to apply these concepts to real-life scenarios. In this chapter, I take a deep dive into a real case study of a startup, and demonstrate how financial modeling and sensitivity analysis were used to develop a successful business plan and secure funding. The chapter also indicates how the financial model was used to create a proforma balance sheet and proforma income statement, which provide a snapshot of the company’s financial performance and position over time. By the end of this chapter, readers will have a practical understanding of how financial modeling and sensitivity analysis can be used to develop a successful business plan and drive value creation.

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On the Data tab, in the Data Tools group, click What-If Analysis , and then click Goal Seek .

Goker, O., & Derindere Köseoğlu, S. (2020). Challenges in valuation by using discounted free cash flow method. In S. D. Köseoğlu (Ed.), Valuation challenges and solutions in contemporary businesses (p. 67). IGI Global.

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Haag, A. B. (2013). Writing a successful business plan: An overview. Workplace Health and Safety, 61 (1), 19–21.

Lasher, W. (1994). The perfect business plan made simple . Doubleday Dell.

Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity analysis in practice: A guide to assessing scientific models . Wiley Pub.

Welter, C. (2021). The road to entrepreneurial success: Business plans, lean startup, or both? New England Journal of Entrepreneurship, 24 (1), 21–42.

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Köseoğlu, S.D. (2023). From Planning to Valuation: Mastering Business Planning and Sensitivity Analysis for Your Startup. In: Derindere Köseoğlu, S. (eds) A Practical Guide for Startup Valuation. Contributions to Finance and Accounting. Springer, Cham. https://doi.org/10.1007/978-3-031-35291-1_3

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Why Your Startup Pitch Needs Sensitivity Analysis

To impress risk-averse investors, you have to prove that you’re ready for whatever may come.

By Nicholas Piscani

Nicholas is a corporate strategy and FP&A expert who works with businesses to optimize their operations and execute high-priority strategic initiatives. He has helped entrepreneurs raise more than $600 million through financial modeling and pitch deck development.

Previous Role

PREVIOUSLY AT

It takes more than an all-star team, a product with a competitive advantage , and a sizable potential market to guarantee fundraising success for your startup. With thousands of proposals flooding investors’ inboxes each year, and venture capital facing an uncertain future , it’s crucial that you distinguish your startup by showing your deep, realistic understanding of the impact even small changes can have on performance. The key is to include sensitivity analysis in your pitch.

As an FP&A specialist who has supported multiple successful eight- and nine-figure fundraising efforts through financial modeling and pitch deck development , I’ve learned firsthand the nuances that separate founders who get funding from those who leave the table empty-handed. To present a convincing case, founders must show that they’re effectively balancing risk and responsibility by demonstrating that they have thoroughly evaluated the consequences of every decision.

While it’s understandable that founders want to project optimism when they’re pitching investors , ignoring challenges can damage the trust they need to build with potential funders. Venture capital firms are quickly turned off by implausibly positive financial forecast assumptions, such as overly generous market share predictions or unrealistic unit economics . What they want to see are model assumptions that have been tested and validated.

That’s especially important when capital is scarce. In 2023, raising money has been particularly tough for startups, with global funding diving to $76 billion in the first quarter, a 53% decrease from the $162 billion recorded in the same quarter of 2022, according to Crunchbase. What’s more, in the same period, every funding stage experienced a steep decline of around 44% to 54%. These figures suggest that venture capital firms are growing more risk-averse . The best response to this trend is to exhibit strong fundamentals and provide persuasive evidence of viability through sensitivity analysis.

What Is Sensitivity Analysis?

Sensitivity analysis, also called what-if analysis, measures the effects of changing inputs in a mathematical model. In a financial model, sensitivity analysis can reveal the inputs with the greatest impact on a business, and help managers develop KPIs and strategies to monitor and address changes in those areas of the business. For instance, if variables such as market size , unit cost, price, or sales volume were to change, how might that affect financial performance—and which one has the biggest impact?

In my experience, founders can sometimes confuse sensitivity analysis and scenario analysis . While both practices evaluate the impact of changes on business models, they’re not the same.

Sensitivity analysis typically focuses on the one or two most important variables in a business model—that is, the ones that will generally cause the largest degree of change, depending on the industry and how the model is built. For example, you might want to sensitize sales price per square foot in a real estate development model , customer churn rates in a subscription-based model , or product mix in a manufacturing model.

Scenario analysis, on the other hand, is used to measure how businesses perform with variations in macro factors that influence the whole organization or an entire business unit. Scenario analysis would be appropriate for evaluating the probable impact of a recession or changing industry regulations—two situations that have a significant effect on a company’s performance even if some key variables and assumptions remain the same.

Sensitivity analysis is one of the most helpful ways a founder can calm investors’ nerves, because it provides them with a view of the margin of safety associated with their investments. If an investor is using a minimum internal rate of return (IRR) as one of their investment qualification metrics, for example, sensitivity analysis can easily demonstrate how significantly the forecasted performance would need to change before the investment fell below the minimum IRR.

From an investor’s perspective, knowing that an investment can still achieve the minimum IRR over the life of the investment despite a 10% drop in sales volume, for instance, gives additional credibility to the founder, the model, and the management team. In this case, even if a prospective investor disagrees with some of the growth prospects and assumes sales will be only 95% of the forecast, they can know that the investment is still a viable one.

Now let’s take a deeper look at the many things sensitivity analysis can help you do to impress potential investors—and benefit your company.

Understand How Small Changes in Assumptions Drive Big Changes in Value

When you’re building projections over long periods, say five to 10 years, small changes in the financial model’s underlying assumptions can cause large changes in the growth of cash flows and valuation . In the first table we can see how significantly small changes in assumed unit sales growth and revenue per sale can impact profitability and cash flows for a retail company.

In Table 1, the present value (PV) of future cash flows , including the terminal value, is just under $130 million.

Table 2 illustrates the same forecast, with unit sales growth reduced by 2% and revenue per sale reduced by 1% starting in the first year. The present value of total future cash flows, including the terminal value, drops to just under $94 million, a 27.7% decrease compared with Table 1.

Again, that’s a 27.7% decrease in cash flows caused by a 2% drop in unit sales growth and a 1% drop in revenue per sale. And unit sales are not the only variable that can change. What if marketing expenses are higher? What if return rates are greater than expected? What happens if net working capital (NWC) doesn’t improve as forecasted?

Successful companies cannot just assume things will go their way: They need to know precisely what they’ll do if costs rise or sales fall unexpectedly. This is why startup companies need to assure investors they’ve stress-tested their models and developed risk management strategies for rainy days.

Identify What Is Most Significant

Sensitivity analysis enables organizations with well-constructed business and financial models to pinpoint and communicate pivotal assumptions. I can’t overemphasize what profound implications this can have for you as a startup founder—not only from a strategic perspective, but also from a fundraising perspective. Not every startup founder can confidently tell investors that they know which assumptions will have the most significant impact on cash flow, and be able to quantify the change in cash flow for every percentage point change in the relevant assumption. When you walk into a pitch meeting with this information in hand, you reassure investors that you’ve thought thoroughly and concretely about the future of the business—and their equity.

Let’s look at this in practice using our earlier retail company example. Starting with the assumptions in Table 1, the model forecasts 10% unit sales growth in Year 1. But how sensitive are cash flows to that assumption versus other assumptions? The following sensitivity analysis tables show how sensitive the present value of future cash flows is to changes in three assumptions that could have significant impacts: unit sales growth, wages paid per unit sold, and annual rent escalations.

Looking at Tables 3 to 5, it’s clear that unit sales growth is the most significant factor on cash flow, with a 1% change causing a roughly 8% change in the present value of cash flows. With this information, you can zero in on the most important drivers of the business model.

To take this analysis a step further and look at a more complicated situation, you can evaluate the potential impact of two of these factors occurring. In the current economic climate of high inflation, there’s a real risk that landlords will require higher rents as leases expire. Let’s say that you’re concerned about cash flow sensitivity to the compound effect of changes in both unit sales growth and annual rent escalations. Continuing with the retail example, we can construct the following table.

A look at Table 6 shows that a 1% increase in annual unit sales growth on the value of cash flows has about six times the impact that a 1% increase in annual rent escalations has. Performing similar analyses for all assumptions in the model will reveal how they interact.

Create Data-driven Strategies

Once you have sensitized your assumptions and identified the areas of greatest impact, you’ll have valuable data for developing strategies to monitor and optimize those parts of your business. In the example model, key drivers of unit sales growth would consist of customer acquisition costs (CAC) , repeat customer rates, return rates, and cross-sell and upsell rates. Let’s say that, after sensitizing the unit sales growth rates against these individual variables, as we did with the annual rent escalations, you determine that return rates and CAC are the primary factors. Your team can then pinpoint the specific levels of performance that would be required to attain the forecasted unit sales growth targets. These performance levels would become the key performance indicators (KPIs) that are monitored and managed by leadership.

From there, you can identify leading indicators for daily monitoring that will inform management if the KPIs are expected to come in above or below the acceptable target. For example, a leading indicator for return rate may be customer satisfaction levels or a Net Promoter Score .

A comprehensive sensitivity analysis of this kind provides the entire management team with the greatest chance of fully understanding and preparing for the opportunities and threats. Not only does this benefit your business, but it helps you formulate persuasive, data-driven answers to hard investor questions.

Prove to Investors That They Can Trust You

If there’s anything the business community has learned from the 2007-2008 financial crisis and the economic shock caused by the COVID-19 pandemic, it’s to expect the unexpected. Sensitivity analysis is a powerful tool in this environment. The ability to sensitize nearly all variables in a business model provides tremendous analytical flexibility and can illuminate potential opportunities and threats.

Lack of funding and cash flow problems can substantially impede a startup’s growth and ability to take advantage of opportunities. Startup funding has declined significantly since 2021, meaning competition for it is fierce. By integrating sensitivity analysis into your pitch and valuation projections, you can also answer potential investor concerns, validate your assumptions, and exhibit prudent risk management. In an era of heightened investor caution, this kind of foresight and preparedness can set up your pitch—and your company—for success.

Further Reading on the Toptal Blog:

• What Is a Financial Model?
• Top Pitch Deck Mistakes
• How to Build a 3-statement Model: Best Practices for Valuations and Projections
• Industry Analysis and Porter’s 5 Forces: A Deeper Look at Buyer Power
• How to Build Your Startup’s Financial Model to Grab Investor Interest
• Justifying Investments With the Capital Budgeting Process

Understanding the basics

What is the purpose of sensitivity analysis.

The purpose of sensitivity analysis is to measure the effects of changing the inputs in a mathematical model. In a financial model, sensitivity analysis can reveal the inputs with the greatest impact and help managers develop KPIs and strategies to monitor and address changes to those areas of the business.

What is an example of sensitivity analysis?

A trucking company may want to know how much a change in the average price of gas would have on the firm. It would perform a sensitivity analysis by developing a model to calculate the effect different gas prices would have on other important parts of the business, such as cash flows or profits. Those results would help the company plan for high gas prices and take advantage of low gas prices.

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What is Sensitivity Analysis? Examples & Templates

Fahad Usmani, PMP

September 25, 2022

Project managers are always involved with data analysis and decision-making. They must be conscious of the sensitivities in data and their impact on the project. To control for this, they use sensitivity analysis to determine the sensitivity of data variables in the project outcome.

In brief, sensitivity analysis examines project scenarios under different circumstances.

This article will discuss sensitivity analysis and its benefits, compare it to scenario analysis, and provide examples of how to use it appropriately.

What is Sensitivity Analysis?

Sensitivity analysis helps determine how changes in one input affect the output. Project managers find this tool useful since it allows them to weigh the benefits and risks under different conditions.

You can see which input has the most influence on the output. Based on this information, managers can then make a better informed decision.

The sensitivity analysis can be used in the following performance domains:

• Planning performance
• The project work performance
• Delivery performance

In the sensitivity analysis process, you change one input (such as cost, time, or scope) and subsequently evaluate how the output changes. You can understand how inputs affect the outcomes by repeating the process for various inputs. After that, you may make changes to plans as needed.

Selecting the right inputs to evaluate changes while performing a sensitivity analysis is crucial. For example, in accounting, you may change the interest rate or the invested amount, but in project management , you may want to change the project’s duration or the resources needed.

It is important to understand how each input influences the outcomes. For instance, you need to know how changing a project’s duration would affect the other elements (e.g., milestones , deadlines, workloads, resource cost, etc.).

How Does Sensitivity Analysis Work?

Sensitivity analysis requires input and target variables. The fields within which you want to make changes are called input variables. The fields you want to measure the consequences of changing are the target variables.

You must develop scenarios based on your adjustments and observe changes. After that, you may use results to make choices about undertakings.

Other variables must remain constant to determine how a change in one variable may affect a result.

A “what if analysis” can show the effect of changing an input variable on the target variable.

It is important not to change more than one input variable at a time while conducting a sensitivity analysis. If you do so, you cannot identify which factors affect the result when making many changes at once. If required, you can test other variables later.

To conduct a sensitivity analysis, follow these steps.

• List the input variable that can affect the project outcome.
• Change one variable while keeping the other variables intact and note the impact of this change on the outcome.
• Repeat the above steps for all other variables.
• Rank the variable according to the severity of the impact. Keep the highest impact variable at the top and the lowest at the bottom.

Approaches for Applying Sensitivity Analysis

You can use two approaches to apply sensitivity analysis. These approaches are:

1. Direct Approach

In this method, you will directly change the numbers in a model’s assumption. For example, when using the direct technique, you may replace the growth rate with alternative values to determine the resulting revenue amounts. 

For instance, the revenue calculation is as follows if your sales growth expectation is 20% annually:

(Last year’s revenue) x (1 + 20%)

2. Indirect Approach

Instead of explicitly altering the value of an assumption, the indirect technique involves inserting a % change into calculations. 

For instance, if you know that the revenue formula and your revenue growth estimate is 20% annually:

You alter the formula as follows:

(Last year’s revenue) x (1 + (20% + X)), where X is a value in the sensitivity analysis area of the model.

Benefits of Sensitivity Analysis

The following are some advantages of sensitivity analysis:

#1. It Examines Many Scenarios

This approach provides probable outcomes in the event of change. Management can easily comprehend the effects and make contingency plans . It will predict the result based on the effect, which may occur when variables change.

#2. Enhanced Managerial Judgment

Sensitivity analysis provides a wide range of potential outcomes that might occur due to changes in a variable. The business will be in a much better position to make decisions after considering all available information.

#3. Effective Resource Management

Sensitivity analysis can aid in ensuring resource distribution is optimal. The business must keep a secure space. Moreover, it should enhance its resources in areas where it lags considerably behind its rivals.

#4. Highlights Areas for Improvement

Sensitivity analysis aids decision-makers in determining where they can make modifications.

#5. Provides a Higher Level of Credibility

By putting financial models to the test against a wide range of potential outcomes, sensitivity analysis increases their trustworthiness.

Sensitivity Analysis Disadvantages

• Since variables often depend on each another, it is impossible to analyze them separately. For instance, a change in selling price will result in a change in sales volume.
• The analysis is based on historical data and experiences, which might not be relevant in the future.
• Determining the highest and the lowest value depends on the decision maker’s interpretation and risk preferences. A wrong choice influences the analysis accuracy.
• It is neither a method of risk measurement nor risk mitigation. It does not result in a more transparent decision-making process. The information must be correctly interpreted.
• All factors in real life are liable to change. A simulation is a good option if you want to evaluate several variables simultaneously.
• Sensitivity analysis only reveals the consequences of changing a variable. It does not indicate the likelihood that those changes will occur.
• Sensitivity analysis will reveal various effects on the result, but it does not identify the optimal option. It just gives information on potential consequences.

Sensitivity Analysis in Different Industries

Sensitivity analysis is used in many industries. Some examples are as follows:

• Chemistry: Sensitivity analysis is used by scientists like chemists to determine measurement positions.
• Social Sciences: Econometric models may be developed using sensitivity analysis to forecast economic patterns in the future.
• Business: Sensitivity analysis is tool companies use to plan future data flow, allocate resources, and pinpoint critical assumptions.
• Meta-Analysis: Sensitivity analysis determines if constraints lead to sensitive outcomes, such as decisions that a team leader must make quickly.
• Engineering: Engineers test their designs and models through sensitivity analysis.
• Environmental: Models for assessing the effects of water purification or the global climate may be developed using sensitivity analysis.

Examples of Sensitivity Analysis

Consider the following two examples of sensitivity analysis :

Tom is the head of the sales department of ABC corporation that sells air coolers. He knew that the sales would increase during the summer season. This year Tom wants to discover the rise in sales with increased customer traffic.

The cost of one air cooler is 700 USD. Last year during May, June, and July, the ABC company sold 200 air coolers, bringing in 140,000 USD.

After conducting a sensitivity analysis, Tom confirms that a 10% increase in customer visits during the summer months will result in a 10% increase in sales. This data helps Tom predict how much profit can be made from prioritizing additional customer visits. 

Therefore, if the percentage of customer visits rises by 25% or 50%, he can expect a big boost in sales.

John is a sales executive who wants to understand customer growth in the new resort company he has joined. 

From last year’s data, he determines that when the customer base increases by 20%, sales increase by 10%. He uses sensitivity analysis and understands that if the increase in customers in the resort is 50%, total sales should increase by 25%.

Sensitivity Analysis Templates

You may forecast sales income using this sensitivity analysis table template based on input factors like traffic increase, unit pricing, and sales volume changes. In this template, we will use the indirect sensitivity analysis approach.

Sales volume after a change in the variable = (Previous Sales volume) x (1 + (20% + X)), where X is a value in the sensitivity analysis area of the model.

The above Sensitivity analysis template can be accessed here .

Use the following template to display the impact of changes on business plans.

The above template is available here .

Sensitivity Analysis Techniques

The three popular sensitivity techniques are:

• Tornado Diagram
• Spider Diagram
• Monte Carlo Simulation

Sensitivity Analysis Vs Scenario Analysis

Sensitivity and scenario analysis are different techniques, although they serve the same purpose (i.e., assessing the risks or impact of changes).

In sensitivity analysis, you change one variable while keeping other variables intact and study the impact of the change on a specific outcome.

In scenario analysis, you can change the complete input scenarios and then alter all variables to align with the new scenario and study the impact of this new scenario on the outcome. Scenario analysis assesses the impact of changing all variables at the same time.

Sensitivity analysis is a good method to identify different outcomes by changing an input variable. You can use this analysis to find risks and opportunities and communicate them to the relevant stakeholders.

Stakeholders can see the prioritized options and then make decisions based on an objective interpretation of the data.

I am Mohammad Fahad Usmani, B.E. PMP, PMI-RMP. I have been blogging on project management topics since 2011. To date, thousands of professionals have passed the PMP exam using my resources.

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• What is Sensitivity Analysis and How Can It Help Your Organization?

What if you had the power to see every possible outcome of your business decisions and confidently make decisions about your organization’s financial future?

Building an agile organization capable of withstanding and adjusting to ever-changing market conditions means being able to identify, with a high degree of certainty, how your organization could be impacted by unexpected events.

This is where sensitivity analysis comes into play. It helps organizations identify critical internal and external drivers that impact their choices, as well as how sensitive their financial models are to each of those variables.

Let’s go over what sensitivity analysis is used for, how it’s conducted, and how you can use it to steer your organization towards better decision-making.

What is sensitivity analysis?

All financial models are built with certain assumptions. These assumptions often take the form of input and output variables (also called independent and dependent variables ). An analyst performing sensitivity analysis examines different combinations of these variables, their interrelationships, and how they impact business decisions and outcomes.

Also known as “what-if” analyses and “stress tests,” sensitivity analysis is often performed as a type of risk analysis and is very important in risk management and contingency planning. In a stress test, the sensitivity of a particular financial model to a set of independent variables is determined by running the model through extreme events (such as COVID-19).

When push comes to shove, can the model survive all possible worst-case scenarios?

The main goal of sensitivity analysis is to help organizations uncover potentially overlooked financial vulnerabilities. Sensitivity analysis is especially useful for complex “black box” scenarios that are very difficult to analyze using conventional methods.

Sensitivity analysis is also a reliable way to uncover the hidden levers that have the greatest impact on business decisions. Analysts adjust independent variables using one-at-a-time (OAT) analysis to uncover how each independent variable impacts the dependent variables.

What is Model Sensitivity?

The degree to which a dependent variable is affected by a change in an independent variable is called its sensitivity. The degree to which a financial model is susceptible to changes in independent variables is called model sensitivity.

Here are some examples of how a financial model may be sensitive to changes in specific inputs:

• What happens to operating profits if you raise the price of your best-selling product by $1.00 per unit?
• How will your bottom line be impacted if the construction of a new facility takes 3 months longer than expected?
• What happens to cash flow if your customer foot traffic drops by 25 percent?

In unprecedented or difficult times, answering critical operational questions like these as accurately as possible makes it easier for decision-makers to confidently reach a consensus on the best decisions for the organization.

Sensitivity Analysis vs. Scenario Analysis

It’s easy to mistake sensitivity analysis for scenario analysis and vice-versa. After all, both are used by analysts in risk management and contingency planning and share several similarities. 

The major difference between the two types of analysis is the outcome of each analysis: scenario analysis reveals which scenarios are most optimal or most detrimental, while sensitivity analysis reveals how sensitive different scenarios are to changes in specific input variables.

For example, let’s say that a university wants to find out whether it makes more sense to build a new science center or dormitory during a partial capacity semester (due to COVID-19). Let’s assume that the cost of the science center is twice the cost of the new dormitory.

How will the university’s endowment determine which project makes more fiscal sense?

Using sensitivity analysis, analysts might determine that both projects, which require financing, are very sensitive to a longer timeline for a vaccine because of the lost room-and-board costs. But the new dormitory construction is much less disruptive to in-person and virtual science classes because no students or teachers will have to be relocated.

Using sensitivity analysis alone, the university might decide that building the new dormitory first may make more sense. But using scenario analysis, the university might review many other scenarios, including one in which neither project commences. They may actually decide that the best course of action, while COVID-19 continues unabated, is to simply do nothing at all.

Sensitivity and Risk Analysis

While no one can accurately predict the future, that doesn’t mean you shouldn’t try. Predicting points of failure and vulnerabilities ahead of time helps your organization stay solvent with plenty of cash equivalents on hand.

Unsurprisingly, sensitivity analysis and stress tests, in general, are very commonly used on Wall Street by financial organizations, such as banks, funds, and portfolio managers.

The most well-known example of sensitivity analysis is the Comprehensive Capital Analysis and Review (CCAR) , conducted annually by the Federal Reserve Bank. The CCAR is used to assess whether the largest bank holding companies in the U.S. can weather worst-case market scenarios, such as sudden market crashes, high default rates for mortgages, and political upheavals.

That’s not to say that sensitivity analysis is only useful for risk management. Far from it. In fact, once sensitivity analysis is properly incorporated into your organization’s risk management strategy, not only can you dodge bullets, but you can also be poised and ready to take advantage of opportunities in times of crisis.

As an example, in the most recent COVID-19-driven market crash, some hedge fund managers (like Bill Ackman) made fortunes as the markets were nosediving by diligently conducting scenario and sensitivity analysis. As Warren Buffett is so fond of saying , “Be fearful when others are greedy, and greedy when others are fearful.”

How to do Sensitivity Analysis

Every sensitivity analysis can be simplified into three major steps:

• Establish a base case

In sensitivity analysis and scenario planning, the three most common scenarios are called:

• The best case , or the most optimistic scenario with the highest potential upside
• The worst case , or the most pessimistic scenario with the highest potential downside
• The base case , or the most conservative scenario with an outcome squarely between the best-case and worst-case scenarios (e.g., the outcome in which nothing amazing or disastrous occurs).

Once a realistic base case scenario is identified, analysts must decide which independent and dependent variables are most relevant to the outcome.

• Determine your variable inputs

Input variables may include things like cost of goods sold, debt financing, employee wages, customer foot traffic, etc. Output variables may include cash flows, internal rate of return (IRR), net present value (NPV), net profits, etc.

For example, net present value, which accounts for the time value of money, is often used to determine whether projects will be profitable. NPV takes into consideration initial capital, the acceptable rate of return, and the return on investment from cash flows.  

• Test the variables

Once the inputs and outputs are determined, analysts perform sensitivity analysis on the assumed independent variables one by one to rigorously test how sensitive their base case is to even the smallest changes.

It’s important to use the base case as a frame of reference for the OAT analysis because it serves as a control. Without a realistic base case scenario, there is no way to reliably determine how the best-case and worst-case scenarios might be impacted.

After all, in the real world, multiple-input variables are likely to change all at once or one after the other, often in extreme and unpredictable ways.

You can test your input and output variables by following this simple four-step process:

• Determine which independent variable you will change first and which dependent variables you will observe.
• Record the output of your base case scenario after changing the first independent variable. Record the percentage change of both the input and output variables.
• Repeat steps 1 and 2 until you have tested all of your independent variables one at a time and recorded how they impact the dependent variables.
• Using the appropriate sensitivity analysis formula, calculate how sensitive your base case scenario is to changes in each of your independent variables.

Key Sensitivity Analysis Formulas

Here’s the main formula that analysts use to calculate the sensitivity of output variables to changes in input variables: 

Sensitivity = Percentage change in output / Percentage change in input

If it’s possible to determine the exact mathematical relationship between each independent variable and dependent variable, outputs can also be written as a function:

“x” is the independent variable (input), and “y” is the dependent variable (output)

The exact function formula will vary depending on the specific relationship between variables, which will be unique to the scenario you’re analyzing.

In order to determine whether a project or investment is worthwhile, analysts may look at net present value (NPV) as the output (dependent variable) in their sensitivity analysis . NPV helps you see whether a project is profitable using the following formula:

NPV = ( Cash flows / (1 + discount rate) t ) – initial investment

“t” is an incremental unit of time (such as # of years)

If the result of the NPV calculation is positive, the investment will yield the desired returns. If it’s negative, it won’t.

Real-world sensitivity analysis example

Let’s look at a real-world example of how sensitivity analysis might help a retailer decide where to focus their efforts in 2021. In 2019, they sold 225,000 units of their flagship product, Product A, at $49 per unit, which resulted in $11,025,000 in revenue. Some of their sales came from website visitors, while others came from in-store purchases.

The company’s financial analyst was tasked with determining the sensitivity of sales to website traffic vs. foot traffic. The analyst organized previous sales history in the following manner:

At a glance, the analyst realizes that store traffic had actually decreased in 2018 before recovering slightly in 2019. But it was still below 2017 levels. Meanwhile, website traffic increased significantly in both years. He reasonably concluded that the YoY sales and revenue increases were both solidly due to growth in website traffic.

Then he looked at the numbers for 2020:

Due to the COVID-19 pandemic shutdowns, store traffic plummeted to the lowest levels in the retailer’s history. Website traffic also fell, but not by nearly as much. Overall, revenue fell by 46.2% compared to 2019.

Clearly, website sales were less sensitive to the shutdowns than store sales, which makes perfect sense. More importantly, website overhead and associated expenses were significantly lower across the board:

Without looking at the cost of certain fixed overhead expenses, which remained the same across both websites and stores (such as employees and vendors), the analyst was able to determine that although website costs rose faster relative to store costs, website expenses were still much lower as a percentage of website sales and revenue.

The analyst was also able to determine that despite ramping up advertising costs in 2020, website sales were still down, just like store sales. He reasonably determined that there was another variable the retailer’s customers had been particularly sensitive to in 2020: price.

The analyst’s final recommendation to the retailer? Continue lowering store costs (or close stores entirely), continue to increase online advertising budgets, and drop the price of Product A by 20% for six months, then by 10% for the next six months.

Will his recommendations work in 2021? Only time will tell. But the analyst can confidently state his case—and the retailer can more confidently make a strategic decision—after reviewing the results of this sensitivity analysis.

A Smarter Way to Perform Sensitivity Analysis

The most popular tool by far for conducting sensitivity analysis and building financial models remains Excel . Unfortunately, spreadsheets leave a lot to be desired. They require a lot of repetitive, manual entry, leaving little room for error.

There’s also no easy way to create a multidimensional analysis in a two-dimensional spreadsheet. Analysts often have to go back to the drawing board and build new spreadsheets from scratch every time stakeholders come up with a new set of questions that need to be answered.

This is why sophisticated organizations should consider using financial modeling tools built specifically for sensitivity analysis. Synario , for example, features patented Multiverse Modeling™ software , which allows users to run unlimited scenarios in a single model.

Not only does this help analysts uncover key drivers faster, but it also lets them stress test their models across countless scenarios in a fraction of the time it would take in spreadsheets. 

Synario’s intuitive interface and vast library of prebuilt algorithms allow modelers to conduct advanced what-if analyses at the macro- and micro-scenario level without the need for hours of tedious and repetitive manual entry.

When it’s time for analysts to meet with decision-makers and present their findings in the board room, Synario’s Presentation Mode makes it easy to visualize the impacts of any executive decision—and to make changes that are automatically reflected mid-presentation.

Join hundreds of public and private organizations around the world and invest in your company’s financial future with Synario’s patented Multiverse Modeling™ solution.

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• Fundamental Analysis

How Can I Apply Sensitivity Analysis to My Investment Decisions?

Charlene Rhinehart is a CPA , CFE, chair of an Illinois CPA Society committee, and has a degree in accounting and finance from DePaul University.

Market participants can use sensitivity analysis to estimate the effects of different variables on investment returns. This form of analysis is designed for project management and profitability forecasts, but you could use it for any type of uncertain projection. The practical benefit of using sensitivity analysis for your investment decisions would be to assess risks and potential errors.

Perhaps the most common investment application of sensitivity analysis involves adjusting the discount rate or other streams of cash flows. This allows you to re-evaluate risks based on specific adjustments.

Taken one step further, sensitivity analysis offers an insight into how your investment strategy is structured. You can use it to compare investment models by demonstrating how profitability depends on underlying model data or other assumptions.

Sensitivity analysis does not produce any specific prescriptions or generate any trading signals. It is left up to the individual investor or project manager to decide how best to utilize the generated results.

Key Takeaways

• Sensitivity analysis is a financial model that examines how specific variables are impacted in response to changes in other variables, called input variables.
• Sensitivity analysis is used to predict the results of a decision in response to a certain variety of variables.
• Sensitivity analysis in financial markets can be used to make predictions as to the direction of the stock price of publicly-traded companies.
• It can also be used more broadly by market participants to assess risk and determine the likelihood of errors when making investing decisions.

Review of Sensitivity Analysis

Sensitivity analysis is a calculation procedure that predicts the effects of changes on input data. Investment decisions are wracked with uncertainty and risk. Most investment models have explicit and implicit assumptions about the behaviors of models and the reliability and consistency of input data.

If these underlying assumptions and data prove incorrect, the model loses its effectiveness. By applying sensitivity analysis, you can examine input values, such as costs of capital , income and the value of investments.

The fundamental purpose of sensitivity analysis is twofold: insight into the impact of critical model-based parameters and the sensitivity of model-produced profitability on those parameters.

The Method of Sensitivity Analysis

To perform sensitivity analysis for your investment models, first, identify a set of criteria by which to evaluate the investments' success. These criteria must be quantitative. Normally, this can be set as the rate of return (ROR) .

Next, define a set of input values that are important to the model. In other words, find out which independent variables are most important in generating ROR. These can include discount rates, asset prices or your personal income.

Next, determine the range over which these values can move. Longer-term investments have larger ranges than shorter-term investments.

Identify the minimum and maximum values that your input variables (and other criteria as necessary) can take while the investment model remains profitable (generating a positive ROR).

Lastly, analyze and interpret the results of moving factors. This process can be simple or complex based on the types of input variables and their effect on ROR.

Disadvantages of Sensitivity Analysis

Investments are complex and multifarious. Investment evaluations might depend on asset prices, exercise or strike prices, rates of return, risk-free rates of return, dividend yields, accounting ratios , and countless other factors.

Sensitivity analysis only generates results based on movements for critical independent variables. Any variables not singled out – for which there are many for any given investment decision – are assumed to be constant.

Independent variables seldom move independently. Independent variables and nonmeasured variables tend to change at the same time.

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How to Do Sensitivity Analysis in Excel

Sensitivity analysis is a powerful tool in financial modeling that allows you to assess the impact of different variables on the outcome of a business decision. By exploring various scenarios and analyzing the results, you can gain a deeper understanding of the potential risks and opportunities associated with a particular course of action.

Table of Contents

Understanding the Importance of Sensitivity Analysis in Financial Modeling

Before we dive into the specifics of how to conduct sensitivity analysis in Excel, it’s important to understand why this technique is so essential in financial modeling. In a complex business environment where multiple variables interact to determine the outcome of a decision, sensitivity analysis can help you identify the key drivers of success or failure and evaluate how different factors may impact the bottom line.

For example, imagine you are considering launching a new product line. You may want to assess the impact of different pricing strategies, marketing campaigns, and production costs on the profitability of the venture. By conducting sensitivity analysis, you can test different scenarios and identify the optimal strategy for achieving your goals.

Another benefit of sensitivity analysis is that it can help you identify potential risks and uncertainties associated with a particular decision. By testing different scenarios and assessing the impact of various factors, you can gain a better understanding of the potential outcomes and make more informed decisions.

Furthermore, sensitivity analysis can be a useful tool for communicating the potential impact of a decision to stakeholders. By presenting different scenarios and highlighting the key drivers of success or failure, you can help others understand the risks and opportunities associated with a particular decision.

Step-by-Step Guide to Conducting Sensitivity Analysis in Excel

Now that we’ve established the importance of sensitivity analysis, let’s walk through the steps for conducting this technique in Excel:

• Identify the variables that may impact the outcome of your decision. These could include factors such as price, volume, production costs, marketing spend, and so on.
• Create a spreadsheet in Excel with columns for each variable and rows for each scenario. Enter your assumptions for the variables in the appropriate cells.
• Use Excel’s data tables function to analyze the results of your scenarios. This will allow you to see how changing one variable impacts the outcome of your decision.
• Analyze the results and identify the scenarios that offer the greatest potential for success. Use this information to inform your decision-making process.

It’s important to note that sensitivity analysis is not a one-time process. As your business or project evolves, it’s important to revisit your assumptions and update your analysis accordingly. This will ensure that you are making informed decisions based on the most up-to-date information available.

Common Uses of Sensitivity Analysis in Business Decision Making

Sensitivity analysis is a versatile technique that can be applied to a wide range of business decisions. Some common use cases include:

• Evaluating potential investments or capital projects
• Assessing the impact of market or economic factors on business performance
• Determining optimal pricing strategies
• Forecasting sales and revenue
• Optimizing supply chain management

In addition to the above use cases, sensitivity analysis can also be used to evaluate the potential risks associated with a business decision. By analyzing how changes in key variables can impact the outcome of a decision, businesses can identify potential risks and develop contingency plans to mitigate them. This can be particularly useful in industries that are highly regulated or subject to significant market volatility.

Creating Scenarios and Variables for Sensitivity Analysis in Excel

When creating scenarios and variables for sensitivity analysis in Excel, it’s important to be thorough and methodical. Consider all the factors that may impact your decision and create a spreadsheet that allows you to test different scenarios and variables.

For example, if you’re evaluating a potential investment, you may want to consider variables such as initial cost, expected return, risk level, and time horizon. By testing different scenarios and assumptions for these variables, you can gain a better understanding of the risks and rewards associated with the investment.

Another important factor to consider when creating scenarios and variables for sensitivity analysis in Excel is the level of detail. It’s important to break down each variable into smaller components to get a more accurate analysis. For instance, if you’re analyzing the sales of a product, you may want to break down the sales by region, customer type, and product category.

Additionally, it’s important to keep in mind that sensitivity analysis is not a one-time process. As the market and business conditions change, you may need to update your scenarios and variables to reflect the new reality. Therefore, it’s important to keep your spreadsheet flexible and adaptable to changes.

Tips for Interpreting and Analyzing Sensitivity Analysis Results

Interpreting and analyzing sensitivity analysis results can be a complex task, but there are some tips and best practices that can make the process easier:

• Focus on the key drivers of success or failure. Identify the variables that have the greatest impact on the outcome of your decision and prioritize these factors in your analysis.
• Be conservative in your assumptions. When testing different scenarios, it’s important to be realistic and avoid overly optimistic or pessimistic assumptions.
• Consider the interplay between different variables. Some variables may have a greater impact when combined with others, so it’s important to analyze the results in a holistic manner.
• Use graphs and visual aids to communicate your findings. Charts and graphs can help stakeholders better understand the results of your sensitivity analysis.

Advanced Techniques for Conducting Monte Carlo Simulation in Excel

Monte Carlo simulation is a more advanced technique for conducting sensitivity analysis in Excel. This method involves generating a large number of scenarios with random values for each variable, allowing you to analyze a wide range of potential outcomes.

To conduct Monte Carlo simulation in Excel, you will need to use a specialized add-in or create your own custom function. This technique requires a deep understanding of statistics and probability theory, so it’s not recommended for beginners.

Practical Examples of Sensitivity Analysis in Real-World Business Situations

Let’s take a look at a few practical examples of how sensitivity analysis can be applied in real-world business situations:

• A manufacturing company is considering expanding its production capacity. By analyzing different scenarios for production costs, sales volume, and pricing, the company can determine the optimal strategy for growth.
• A retail business is evaluating a new store location. By conducting sensitivity analysis on factors such as foot traffic, rent costs, and sales projections, the business can make an informed decision on whether or not to pursue the opportunity.
• A software startup is looking to raise funding. By analyzing different scenarios for revenue growth, customer acquisition costs, and churn rate, the company can create a compelling business plan and pitch to potential investors.

Best Practices for Presenting Sensitivity Analysis Results to Stakeholders

When presenting sensitivity analysis results to stakeholders, it’s important to focus on the key takeaways and make the information as clear and concise as possible. Some best practices to keep in mind include:

• Provide context for the results. Explain the assumptions and methodology behind the analysis and provide an overview of the decision being evaluated.
• Focus on the most important findings. Instead of overwhelming stakeholders with a lot of data, highlight the key drivers of success or failure and explain how they can impact the decision.
• Use visual aids to enhance understanding. Charts, graphs, and other visual aids can be highly effective in communicating complex concepts and helping stakeholders understand the results of the analysis.
• Be prepared to answer questions and address concerns. Stakeholders may have questions or objections to the results of the analysis, so it’s important to be prepared to respond and provide additional information.

Common Mistakes to Avoid When Conducting Sensitivity Analysis in Excel

Even experienced financial modelers can make mistakes when conducting sensitivity analysis in Excel. Some common pitfalls to avoid include:

• Overlooking important variables. It’s important to consider all the variables that may impact your decision, even if they seem minor or insignificant.
• Making unrealistic assumptions. Be sure to base your assumptions on realistic data and avoid overly optimistic or pessimistic scenarios.
• Not considering uncertainties or risk. Sensitivity analysis can help you identify potential risks, so be sure to incorporate this information into your decision-making process.
• Being too reliant on the results. Remember that sensitivity analysis is just one tool among many for making business decisions, and it’s important to consider other factors as well.

Integrating Sensitivity Analysis into Your Financial Planning and Forecasting Processes

Sensitivity analysis can be a valuable addition to your financial planning and forecasting processes. By incorporating this technique into your regular workflow, you can gain a deeper understanding of the risks and opportunities associated with different business decisions.

To effectively integrate sensitivity analysis into your processes, consider the following tips:

• Ensure that your assumptions are based on accurate and up-to-date data.
• Use templates or standard models to streamline the analysis process.
• Document your assumptions and methodologies for future reference.
• Regularly review and update your models to reflect changes in your business environment.

Top Tools and Add-Ins for Streamlining Your Sensitivity Analysis Workflow

There are many tools and add-ins available that can help you streamline your sensitivity analysis workflow in Excel. Some top picks include:

• Data Tables
• Crystal Ball

Understanding the Limitations and Assumptions of Sensitivity Analysis

Like any technique, sensitivity analysis has its limitations and caveats. It’s important to understand these factors when interpreting the results of your analysis:

• Sensitivity analysis is based on assumptions and estimates, which can introduce a margin of error.
• It may not account for more complex interactions between variables that cannot be easily modeled.
• It cannot predict with certainty what will happen in the future, but rather shows the potential outcomes of different scenarios.
• The results are only as good as the quality of the data and assumptions used in the analysis.

Future Trends and Innovations in the Field of Sensitivity Analysis and Financial Modeling

The field of sensitivity analysis and financial modeling is constantly evolving, with new techniques and tools being developed all the time. Some emerging trends and innovations to watch for include:

• More advanced statistical modeling techniques, such as machine learning and artificial intelligence.
• Growing interest in environmental, social, and governance (ESG) factors when evaluating business decisions.
• Increasing use of scenario analysis to evaluate the potential impact of different future events or trends.
• Integration of sensitivity analysis into other financial modeling tools, such as portfolio optimization and risk management.

Sensitivity analysis is a critical tool for financial modelers and decision-makers alike. By understanding how to conduct this technique in Excel, you can gain a deeper understanding of the risks and opportunities associated with different business decisions and make more informed choices.

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Components of a Sensitivity Analysis

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Three Components of Murray's Sensitivity Analysis:

1. THE HEADING Notice the "Heading" of the Sensitivity Analysis depicts the name of the company (Scholarship Information Services), the type of statement to follow (Sensitivity Analysis) and the date of the analysis (for the year ending December 31, 200X).

SCHOLARSHIP INFORMATION SERVICES SENSITIVITY ANALYSIS FOR YEAR ENDING DECEMBER 31, 200X

Although the Heading may seem simple, it's extremely important and should never be omitted from any financial statement or analysis.

2. SALES PERCENTAGE FACTORS Sales percentage factors indicate the sales percentage increases and decreases, in which the analysis is based upon. As you can see, Scholarship Information Service's Sensitivity Analysis uses the following sales percentage factors.

Therefore, Murray's Sensitivity Analysis will show how his 200X "original" Forecasted Net Income will change if his "original" 200X sales forecast decreases by 15% & 10%, and if his "original" 200X sales forecast increases by 10%. PLEASE NOTE: you may choose any Sales Percentage Factor for your forecasted sensitivity analysis.

The third column, entitled "200X forecasted figures" represents Murray's 200X original Forecasted Income Statement. Furthermore, the sales percentage factors will be applied to the values appearing under this column.

3. THE BODY The final component of the sensitivity analysis is The Body. The Body consists of four main areas - A) Sales, B) Variable Costs, C) Fixed Costs and D) Net Income. Lets look at each component separately.

A) SALES: Sales (in dollars or in units) are the driving force behind a sensitivity analysis. The reason is simple - expenses & costs either increase, decrease or remain the same when sales levels are changed. And when sales, expenses, and/or costs increase or decrease, from their original values, a company's net income (bottom line) will change.

Recall from Budget 2 entitled "Developing Your Sales Budget", Murray estimates that he will sell 4,000 units at $26.00 each during the 200X business year. As a result, he projects his sales (in dollars) for 200X at $104,000 (4,000 units x $26.00 = $104,000). This value is depicted under the column entitled "200X Forecasted Figures" (see below).

If Murray actually sells 10% fewer products in 200X than originally forecasted, then his total unit sales would be 3,600 units ( 4,000 units - 10% = 3,600 unit sales) and his total dollar sales would be $93,600 (3,600 units x $26.00 = $93,600). Or if Murray actually sells 15% fewer products in 200X than originally forecasted, then his total unit sales would be 3,400 units ( 4,000 units - 15% = 3,400 unit sales) and his total dollar sales would be $88,400 (3,400 units x $26.00 = $88,400).

On the other end of the continuum, if Murray actually sells 10% more diskettes in 200X than originally forecasted, then his total unit sales would be 4,400 diskettes (4,000 units + 10% = 4,400 unit sales) and his total dollar sales would be $114,400 (4,400 units x $26.00 = $114,400).

B) VARIABLE COSTS: Variable Costs are costs or expenses that DO fluctuate with the production or the sale of one "additional" unit. In other words, if sales levels are increased by 10%, then variable cost items will also increase by 10%. Similarly, if sales levels decrease by 10%, variable cost items will also decrease by 10%.

Cost of Goods Sold is ALWAYS considered a Variable Cost. Why? If a company sells more products than originally forecasted, then it will be required to purchase, buy and use more finished products or direct materials. On the other hand, if a company sells fewer products than originally forecasted, then it will be required to purchase, buy and use fewer finished products or direct materials.

As indicated below, the only Variable Cost of Scholarship Information Services is its Cost of Goods Sold (COGS).

Recall from previous discussions, Murray expects to sell 4,000 units during his 200X business year. In addition, the cost to purchase and ship each diskette is estimated at $3.00. Thus, it will cost the company $12,000 to purchase the 4,000 diskettes it plans to sell during the 200X business year ( 4,000 units x $3.00 each = $12,000). This value is presented under the column entitled "200X Original Forecasted Figures".

If, however, Murray actually sells 10% fewer products in 200X than originally forecasted, then his total unit sales would be 3,600 units ( 4,000 units - 10% = 3,600 unit sales). As a result, the firm's Cost of Goods Sold would be $10,800 (3,600 units x 3.00 = $10,800). This value is presented under the column entitled "10% Decrease In Sales". Similarly, if the company actually sells 15% fewer products in 200X than originally forecasted, then its total unit sales would be 3,400 units ( 4,000 units - 15% = 3,400 unit sales). As a result, the firm's Cost of Goods Sold would be (3,400 units x $3.00 = $10,200). This value is presented under the column entitled "15% Decrease In Sales".

On the other end of the continuum, if Murray actually sells 10% more products in 200X than originally forecasted, then his total unit sales would be 4,400 units (4,000 units + 10% = 4,400 unit sales). As a result, his Cost of Goods Sold would be $13,200 (4,400 units x $3.00 = $13,200). This value is presented under the column entitled "10% Increase In Sales". In summary, your Cost of Goods Sold will ALWAYS be considered a Variable Cost and therefore, will ALWAYS fluctuate with sales increases and decreases.

C) FIXED COSTS In the above discussion, we defined a variable cost as a cost or an expense that fluctuates with the production or sale of one "additional" unit. A Fixed Cost, however, is cost or an expense that does NOT fluctuate with the production or sale of one "additional" unit. In other words, fixed costs are costs that do NOT increase when a company produces or sells one additional unit. Nor does a fixed cost decrease when a company sells one less unit. Think of a fixed cost as a cost or an expense that remains relatively fixed or constant over a one year business period.

Examples of Fixed Costs include; advertising, office rent, leasing expense, telephone expense, wages, utilities expenses, bank charges, interest expenses, deprecation, salaries, etc... Usually, but not in all cases, ALL OPERATING EXPENSES ARE GENERALLY CONSIDERED FIXED COSTS. In our example, for instance, Murray has assumed that all his forecasted operating expenses are Fixed Costs. Below illustrates Murray's Operating Expenses and hence, his Fixed Costs for his 200X forecasted business year. The chart also depicts his Forecasted Fixed Costs at various sales increases and decreases.

Notice, the Fixed Cost do not change from the 200X original forecasted figures when sales are increased or decreased. Moreover these Costs are relatively constant or fixed over the 200X business year ($84,171).

D. NET INCOME BEFORE TAXES Net Income Before Taxes is calculated by subtracting Variable Costs and Fixed Costs from Sales.

In 200X, Murray is forecasting a Net Income Before Tax of $7,829 (assuming he sells 4,000 diskettes for $26.00 each and that all costs are accurate). If however, in 200X he sells 10% fewer diskettes than originally forecasted or 3,600 units, Murray's Net Income will be negative $1,371 (better known as a net loss). If in 200X, Murray sells 15% fewer products than originally forecasted or 3,200 units, his Net Loss will be $5,971. On the other hand, if in 200X, the company sells 10% more units than originally forecasted or 4,400 units, its Net Income Before Tax will be $17,029.

Most firms, when developing a sensitivity analysis, apply an income tax rate to their Forecasted Net Incomes Before Tax. This provides a more accurate look at a company's net income(s) on a after tax basis. For example, assuming Murray estimates that his combined federal and state/provincial income tax rate will be 30%, he would calculate the Net Income After Tax as follows:

In essence, a Sensitivity Analysis is simply a series of forecasted income statements at various sales levels. It estimates the forecasted earnings of a company, assuming the original forecasted sales are off by 10%, 20% 30% or any other percentage. By doing so, an entrepreneur, as well as an investor, can better assess the earnings capability of the business.

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Home > Financial Projections > Sensitivity Analysis vs Scenario Analysis

Sensitivity Analysis vs Scenario Analysis

Financial projections show a single outcome based on a set of assumptions and inputs. Uncertainty in the various assumptions and inputs creates risk, and will determine how the investor interprets the projections. Sensitivity analysis is carried out in order to assess risk.

With sensitivity analysis only one input is changed at a time in order to assess the impact of that input on the financial projection. By changing each input seperately it is possible to assess the significance of each variable on the business

Scenario Analysis and Sensitivity Analysis in a Business Plan

The difference between sensitivity analysis and scenario analysis is that sensitivity analysis changes only one input at a time in order to assess the sensitivity of the financial projection to that variable. With scenario analysis, all inputs changes are made at the same time with the purpose of assessing the effect on the business plan of a complete change in circumstances.

The three main scenarios are usually referred to as the best case, base case and worst case scenarios and the procedure for carrying out the analysis using the financial projections template is as follows:

Step 1 – Develop the Base Case Scenario

Step 2 – develop the best case scenario.

Make a copy of the base case scenario financial projections template developed in step 1, and amend the inputs to show what will happen if your positive expectations are met, and you can seize all the opportunities available to the business.

For example, in the base case scenario, you might estimate that revenue will increase by 5% each year, in the best case scenario, you might want to show what will happen if revenue increases by 10% each year. When carrying out sensitivity analysis, it is important to remember that the projections still have to be feasible and achievable, they are not simply hypothetical what ifs.

Step 3 – Develop the Worst Case Scenario

Again, make a copy of the base case scenario financial projections template developed in step 1, and change the inputs to reflect what will happen if your negative expectations are met, if all the problems anticipated do happen, and projections develop worse than expected.

For example, in the base case scenario, you might have anticipated opening an export market in year three, show what will happen if that market does not develop or is delayed until a later year.

Investors will look at the sensitivity analysis and in particular the worst case scenario, to see how vulnerable the business is to assumption and input changes in order to assess the risks involved in the business.

When presenting the best case, worst case, and base case scenarios a brief description should be provided to show how the major assumptions and inputs have been changed between scenarios. In addition, for the base case scenario a detailed description should be given, and for the best and worse case scenarios, a summary of the key financial information should be provided.

About the Author

Chartered accountant Michael Brown is the founder and CEO of Plan Projections. He has worked as an accountant and consultant for more than 25 years and has built financial models for all types of industries. He has been the CFO or controller of both small and medium sized companies and has run small businesses of his own. He has been a manager and an auditor with Deloitte, a big 4 accountancy firm, and holds a degree from Loughborough University.

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3.3 Perform Break-Even Sensitivity Analysis for a Single Product Under Changing Business Situations

Finding the break-even point or the sales necessary to meet a desired profit is very useful to a business, but cost-volume-profit analysis also can be used to conduct a sensitivity analysis , which shows what will happen if the sales price, units sold, variable cost per unit, or fixed costs change. Companies use this type of analysis to consider possible scenarios that assist them in planning.

Link to Learning

Watch this video that shows what happens if one or more of the variables in a break-even analysis is changed to learn more.

The Effects on Break-Even under Changing Business Conditions

Circumstances often change within a company, within an industry, or even within the economy that impact the decision-making of an organization. Sometimes, these effects are sudden and unexpected, for example, if a hurricane destroyed the factory of a company’s major supplier; other times, they occur more slowly, such as when union negotiations affect your labor costs. In either of these situations, costs to the company will be affected. Using CVP analysis, the company can predict how these changes will affect profits.

Changing a Single Variable

To demonstrate the effects of changing any one of these variables, consider Back Door Café, a small coffee shop that roasts its own beans to make espresso drinks and gourmet coffee. They also sell a variety of baked goods and T-shirts with their logo on them. They track their costs carefully and use CVP analysis to make sure that their sales cover their fixed costs and provide a reasonable level of profit for the owners.

Change in Sales Price

The owner of Back Door has one of her employees conduct a survey of the other coffee shops in the area and finds that they are charging $0.75 more for espresso drinks. As a result, the owner wants to determine what would happen to operating income if she increased her price by just $0.50 and sales remained constant, so she performs the following analysis:

The only variable that has changed is the $0.50 increase in the price of their espresso drinks, but the net operating income will increase by $750. Another way to think of this increase in income is that, if the sales price increases by $0.50 per expresso drink and the estimated sales are 1,500 units, then this will result in an increase in overall contribution margin of $750. Moreover, since all of the fixed costs were met by the lower sales price, all of this $750 goes to profit. Again, this is assuming the higher sales price does not decrease the number of units sold. Since the other coffee shops will still be priced higher than Back Door, the owner believes that there will not be a decrease in sales volume.

When making this adjustment to their sales price, Back Door Café is engaging in target pricing , a process in which a company uses market analysis and production information to determine the maximum price customers are willing to pay for a good or service in addition to the markup percentage. If the good can be produced at a cost that allows both the desired profit percentage as well as deliver the good at a price acceptable to the customer, then the company should proceed with the product; otherwise, the company will not achieve its desired profit goals.

Change in Variable Cost

In March, the owner of Back Door receives a letter from her cups supplier informing her that there is a $0.05 price increase due to higher material prices. Assume that the example uses the original $3.75 per unit sales price. The owner wants to know what would happen to net operating income if she absorbs the cost increase, so she performs the following analysis:

She is surprised to see that just a $0.05 increase in variable costs (cups) will reduce her net income by $75. The owner may decide that she is fine with the lower income, but if she wants to maintain her income, she will need to find a new cup supplier, reduce other costs, or pass the price increase on to her customers. Because the increase in the cost of the cups was a variable cost, the impact on net income can be seen by taking the increase in cost per unit, $0.05, and multiplying that by the units expected to be sold, 1,500, to see the impact on the contribution margin, which in this case would be a decrease of $75. This also means a decrease in net income of $75.

Change in Fixed Cost

Back Door Café’s lease is coming up for renewal. The owner calls the landlord to indicate that she wants to renew her lease for another 5 years. The landlord is happy to hear she will continue renting from him but informs her that the rent will increase $225 per month. She is not certain that she can afford an additional $225 per month and tells him she needs to look at her numbers and will call him back. She pulls out her CVP spreadsheet and adjusts her monthly fixed costs upwards by $225. Assume that the example uses the original $3.75 per unit sales price. The results of her analysis of the impact of the rent increase on her annual net income are:

Because the rent increase is a change in a fixed cost, the contribution margin per unit remains the same. However, the break-even point in both units and dollars increase because more units of contribution are needed to cover the $225 monthly increase in fixed costs. If the owner of the Back Door agrees to the increase in rent for the new lease, she will likely look for ways to increase the contribution margin per unit to offset this increase in fixed costs.

In each of the prior examples, only one variable was changed—sales volume, variable costs, or fixed costs. There are some generalizations that can be made regarding how a change in any one of these variables affects the break-even point. These generalizations are summarized in Table 3.1 .

Watch this video that walks through, step by step, how to calculate break even in units and dollars and at a desired profit or sales level to learn more.

Changing Multiple Variables

We have analyzed situations in which one variable changes, but often, more than one change will occur at a time. For example, a company may need to lower its selling price to compete, but they may also be able to lower certain variable costs by switching suppliers.

Suppose Back Door Café has the opportunity to purchase a new espresso machine that will reduce the amount of coffee beans required for an espresso drink by putting the beans under higher pressure. The new machine will cost $15,000, but it will decrease the variable cost per cup by $0.05. The owner wants to see what the effect will be on the net operating income and break-even point if she purchases the new machine. She has arranged financing for the new machine and the monthly payment will increase her fixed costs by $400 per month. When she conducts this analysis, she gets the following results:

Looking at the “what-if” analysis, we see that the contribution margin per unit increases because of the $0.05 reduction in variable cost per unit. As a result, she has a higher total contribution margin available to cover fixed expenses. This is good, because the monthly payment on the espresso machine represents an increased fixed cost. Even though the contribution margin ratio increases, it is not enough to totally offset the increase in fixed costs, and her monthly break-even point has risen from $4,125.00 to $4,687.50. If the new break-even point in units is a realistic number (within the relevant range), then she would decide to purchase the new machine because, once it has been paid for, her break-even point will fall and her net income will rise. Performing this analysis is an effective way for managers and business owners to look into the future, so to speak, and see what impact business decisions will have on their financial position.

Let’s look at another option the owner of the Back Door Café has to consider when making the decision about this new machine. What would happen if she purchased the new machine to realize the variable cost savings and also raised her price by just $0.20? She feels confident that such a small price increase will go virtually unnoticed by her customers but may help her offset the increase in fixed costs. She runs the analysis as follows:

The analysis shows the expected result: an increase in the per-unit contribution margin, a decrease in the break-even point, and an increase in the net operating income. She has changed three variables in her costs—sales price, variable cost, and fixed cost. In fact, the small price increase almost gets her back to the net operating income she realized before the purchase of the new expresso machine.

By now, you should begin to understand why CVP analysis is such a powerful tool. The owner of Back Door Café can run an unlimited number of these what-if scenarios until she meets the financial goals for her company. There are very few tools in managerial accounting as powerful and meaningful as a cost-volume-profit analysis.

Concepts In Practice

Value menus.

In January 2018, McDonald’s brought back its $1 value menu. After discontinuing its popular Dollar Menu six years previously, the new version has a list of items priced not only at $1, but at $2 and $3 as well. How can McDonald’s afford to offer menu items at this discounted price? Volume! Although the margin on each unit is very small, the food chain hopes to make up the difference in quantity. They also hope that consumers will add higher priced (and higher margin) items to their orders. 1 The strategy is not without its risks, however, as rising food or labor costs could put franchisees in a position where the value pricing does not cover their product costs. Rivals Taco Bell and Dunkin’ Donuts have aggressively marketed their value menus, making it almost impossible for McDonald’s to ignore the growing trend among consumers for “value pricing.” Watch this video to see what McDonald’s is offering consumers.

• 1 Zlati Meyer. “McDonald’s Hope Customers Buck Up Thursday to New Dollar Menu.” USA Today. January 3, 2018. https://www.usatoday.com/story/money/2018/01/03/mcdonalds-hopes-customers-buck-up-thursday-new-dollar-menu/996350001/

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