sensitivity analysis for 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.

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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.

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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?

What-If Analysis

Sensitivity analysis example, download the free template, sensitivity analysis vs. scenario analysis, advantages of financial sensitivity analysis, best practices in sensitivity analysis, video explanation of sensitivity analysis, related articles and guides.

A guide to sensitivity analysis

Sensitivity Analysis is a tool used in financial modeling to analyze how the different values of a set of independent variables affect a specific dependent variable under certain specific conditions. In general, sensitivity analysis is used in a wide range of fields, ranging from biology and geography to economics and engineering.

It is especially useful in the study and analysis of a “Black Box Process” where the output is an opaque function of several inputs. An opaque function or process is one which, for some reason, can’t be studied and analyzed. For example, climate models in geography are usually very complex. As a result, the exact relationship between the inputs and outputs is not well understood.

Image from CFI’s  Scenario & Sensitivity Analysis in Excel Course 

A Financial Sensitivity Analysis, also known as a What-If analysis or a What-If simulation exercise, is most commonly used by financial analysts to predict the outcome of a specific action when performed under certain conditions.

Financial Sensitivity Analysis is done within defined boundaries that are determined by the set of independent (input) variables.

For example, sensitivity analysis can be used to study the effect of a change in interest rates on bond prices if the interest rates increased by 1%.  The “What-If” question would be: “ What would happen to the price of a bond If interest rates went up by 1%?”. This question can be answered with sensitivity analysis.

The analysis is performed in Excel, under the Data section of the ribbon and the “What-If Analysis” button, which contains both “Goal Seek” and “Data Table”. These functions are both taught step-by-step in our free Excel Crash Course .

John is in charge of sales for HOLIDAY CO, a business that sells Christmas decorations at a shopping mall. John knows that the holiday season is approaching and that the mall will be crowded. He wants to find out whether an increase in customer traffic at the mall will raise the total sales revenue of HOLIDAY CO and, if so, then by how much.

The average price of a packet of Christmas decorations is $20. During the previous year’s holiday season, HOLIDAY CO sold 500 packs of Christmas decorations, resulting in total sales of $10,000.

After carrying out a Financial Sensitivity Analysis, John determines that a 10% increase in customer traffic at the mall results in a 7% increase in the number of sales.

Using this information, John can predict how much money company XYZ will generate if customer traffic increases by 20%, 40%, or 100%. Based on John’s Financial Sensitivity Analysis, such increases in traffic will result in an increase in revenue of 14%, 28%, and 70%, respectively.

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Sensitivity Analysis Table

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It is important not to confuse Financial Sensitivity Analysis with Financial Scenario Analysis. Although similar to some degree, the two have some key differences.

Sensitivity Analysis is used to understand the effect of a set of independent variables on some dependent variable under certain specific conditions. For example, a financial analyst wants to find out the effect of a company’s net working capital on its profit margin. The analysis will involve all the variables that have an impact on the company’s profit margin, such as the cost of goods sold , workers’ wages, managers’ wages, etc. The analysis will isolate each of these fixed and variable costs and record all the possible outcomes.

Scenario Analysis , on the other hand, requires the financial analyst to examine a specific scenario in detail. Scenario Analysis is usually done to analyze situations involving major economic shocks, such as a global market shift or a major change in the nature of the business.

After specifying the details of the scenario, the analyst would then have to specify all of the relevant variables, so that they align with the scenario. The result is a very comprehensive picture of the future (a discrete scenario). The analyst would know the full range of outcomes, given all the extremes, and would have an understanding of what the various outcomes would be, given a specific set of variables defined by a specific real-life scenario.

There are many important reasons to perform sensitivity analysis:

• Sensitivity analysis adds credibility to any type of financial model by testing the model across a wide set of possibilities.
• Financial Sensitivity Analysis allows the analyst to be flexible with the boundaries within which to test the sensitivity of the dependent variables to the independent variables. For example, the model to study the effect of a 5-point change in interest rates on bond prices would be different from the financial model that would be used to study the effect of a 20-point change in interest rates on bond prices.
• Sensitivity analysis helps one make informed choices. Decision-makers use the model to understand how responsive the output is to changes in certain variables. Thus, the analyst can be helpful in deriving tangible conclusions and be instrumental in making optimal decisions.

#1 Layout in Excel

Layout, structure, and planning are all important for good sensitivity analysis in Excel.  If a model is not well organized, then both the creator and the users of the model will be confused and the analysis will be prone to error.

The most important points to keep in mind for layout in Excel include:

• Place all assumptions in one area of the model
• Format all assumptions/inputs in a unique font color so that they are easy to identify
• Think carefully about what to test – only the most important assumptions
• Understand the relationship (correlation) between dependent and independent variables (linear? – nonlinear?)
• Create charts and graphs that enable users to easily visualize the data
• Create a separate area for the analysis by using grouping (see example below)

#2 Direct versus indirect methods

The direct method involves substituting different numbers into an assumption in a model.

For example, if the revenue growth assumption in a model is 10% year-over-year ( YoY ), then the revenue formula is = (last year revenue) x (1 + 10%). In the direct approach, we substitute different numbers to replace the growth rate – for example, 0%, 5%, 15%, and 20%  – and see what the resulting revenue dollars are.

The indirect method (as shown below) inserts a percent change into formulas in the model, instead of directly changing the value of an assumption.

Using the same example as above, if the revenue growth assumption in a model is 10% year-over-year ( YoY ), then the revenue formula is = (last year revenue) x (1 + 10%).  Instead of changing 10% to some other number, we can change the formula to be = (last year revenue) x (1 + (10% + X)), where X is a value contained down in the sensitivity analysis area of the model.

Learn how to do this step-by-step – launch our sensitivity analysis course now!

#3 Tables, charts, and graphs

Sensitivity analysis can be challenging to comprehend even by the most informed and technically savvy finance professionals, so it’s important to be able to express the results in a manner that’s easy to comprehend and follow.

Data tables are a great way of showing the impact on a dependent variable by the changing of up to two independent variables. Below is an example of a data table that clearly shows the impact of changes in revenue growth and EV/EBITDA multiple on a company’s share price.

Tornado Charts can be a great way of showing the impact of changes to many variables at once. They are called Tornado Charts because they are sorted, from the most impactful to least impactful, in a way that shapes the chart like a tornado cone. To learn how to build these charts, launch our sensitivity analysis in Excel course now!

Watch this short video to quickly understand the main concepts covered in this guide, including the Direct and Indirect methods.

Thank you for reading this guide to sensitivity analysis. To learn more about financial modeling, these free CFI resources will be helpful:

• Scenario Analysis
• Analysis of Financial Statements
• DCF Modeling Guide
• Financial Modeling Best Practices
• See all financial modeling resources

Analyst Certification FMVA® Program

Below is a break down of subject weightings in the FMVA® financial analyst program. As you can see there is a heavy focus on financial modeling, finance, Excel, business valuation, budgeting/forecasting, PowerPoint presentations, accounting and business strategy.

A well rounded financial analyst possesses all of the above skills!

Additional Questions & Answers

CFI is the global institution behind the financial modeling and valuation analyst  FMVA® Designation . CFI is on a mission to enable anyone to be a great financial analyst and have a great career path. In order to help you advance your career, CFI has compiled many resources to assist you along the path.

In order to become a great financial analyst, here are some more  questions and answers  for you to discover:

• What is Financial Modeling?
• How Do You Build a DCF Model?
• How Do You Value a Business?
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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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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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Sensitivity analysis: What is it?

A Guide to Mastering Sensitivity Analysis in Financial Modeling

Before deciding to pursue his  MBA , Andy previously spent two years at Credit Suisse in Investment Banking, primarily working on  M&A  and  IPO  transactions. Prior to joining Credit Suisse, Andy was a Business Analyst Intern for  Capital One  and worked as an associate for Cambridge Realty Capital Companies.

Andy graduated from University of Chicago with a Bachelor of Arts in Economics and Statistics and is currently an  MBA  candidate at The University of Chicago Booth School of Business with a concentration in Analytical Finance.

Josh has extensive experience private equity, business development, and investment banking. Josh started his career working as an investment banking  analyst for Barclays  before transitioning to a private equity role Neuberger Berman. Currently, Josh is an Associate in the Strategic Finance Group of Accordion Partners, a  management consulting  firm which advises on, executes, and implements value creation initiatives and 100 day plans for Private Equity-backed companies and their financial sponsors.

Josh graduated Magna Cum Laude from the University of Maryland, College Park with a Bachelor of Science in Finance and is currently an  MBA  candidate at Duke University Fuqua School of Business with a concentration in Corporate Strategy.

• What Is Sensitivity analysis?
• Advantages Of Sensitivity Analysis
• Disadvantages Of Sensitivity Analysis
• Sensitivity: How Is It Measured?
• Example Of Sensitivity Analysis
• Sensitivity Analysis In Decision-Making And Decision Trees
• Sensitivity Analysis: Approaches
• Sensitivity Analysis Vs. Scenario Analysis
• Sensitivity Analysis – Model Template

What is Sensitivity analysis?

​Sensitivity analysis is a technique that helps us analyze how a change in an independent input variable affects the dependent target variable under a defined set of assumptions. It is also known as what-if analysis or simulation analysis.

It is widely used in several fields requiring analysis, from biology and engineering to finance and economics.

Every model has an overall level of uncertainty arising due to various assumptions, which may or may not hold up in the future. Sensitivity analyses help us identify and account for uncertainty arising due to the use of these assumptions.

It is also helpful in studying black-box models, which cannot be otherwise studied or analyzed due to high levels of complexity.

The video below demonstrates the practical application of such analysis using the "What-If Analysis" tool in Excel. We see how the tool works by testing for different values for gross margin and sales growth rate and how the changes can further impact the share price.

It also illustrates how, while using the What-If Analysis, Excel performs thousands of calculations and, thus, its high computational cost.

Key Takeaways

• Sensitivity analysis helps identify how changes in independent variables impact the dependent target variable under given assumptions.
• It is a valuable tool for managing uncertainty and understanding cause-and-effect relationships in models.
• Sensitivity analysis aids in decision-making by providing a holistic view and identifying key variables that affect outcomes.
• The analysis can be time-consuming and complex for models with many inputs, and it may overlook multicollinearity between variables.
• Variance-based sensitivity analysis is a probabilistic approach that breaks down output variance into contributions from different inputs, allowing for a global view of sensitivity.

Advantages of sensitivity analysis

This analysis results in a range of values based on assumptions and a range of input values, the primary application of which is to analyze how sensitive the dependent variable is to changes in the independent variables.

It is a useful tool for many good reasons:

• In-depth analysis: What-if analysis studies the relationship between the independent and the dependent variables. It also studies the sensitivity of the dependent variable to changes in independent variables. In doing so, the cause-and-effect relationship is established, which helps to improve the conclusions drawn from the model.
• Managing uncertainty: Models are rife with uncertainty. Simulation analysis aids in identifying, attributing, and analyzing the effects of uncertainties from different sources in a model.
• Importance of variables: Users can determine the importance of different independent variables depending on how sensitive the output is to them. They can then focus on the key variables to work towards the best possible outcome.
• More reliable outcomes: Studying inputs and their effects on the outcome thoroughly can help in making predictions that are more consistent and reliable.
• Decision-making: Simulation analysis helps make predictions backed by data as a wide range of possible inputs and outcomes are studied. This provides decision-makers with a holistic picture, thus helping better decision-making.
• Improving models: A thorough analysis of the inputs can help identify and fix the model's shortcomings to make it more reliable. It lays out the limitations and the scope of a model, thereby helping fix errors. It also helps verify whether the underlying assumptions behind the model are sound.
• Adds credibility: Testing models with wide input ranges adds credibility to models and predictions.

Disadvantages of sensitivity analysis

Like all analytical methods, sensitivity analysis is not without its drawbacks. 

Some of the disadvantages are: 

• Too many inputs: Some models can be extremely complex, making analysis of the relationships between each input and the output infeasible. Screening may reduce the dimensionality (by reducing the number of inputs) before running a what-if analysis.
• Multicollinearity: This analysis ignores the interrelationship between the independent variables. For instance, outcome m may depend on variables x and y , which may be correlated. However, these variables will be examined separately under simulation analysis, and their correlation will be ignored.
• Historical data: It uses historical data to establish relationships. However, that leaves plenty of room for errors, as it may not be a good way to make estimates for the future. In addition, there can be new external factors that did not impact the relationship in the past but may impact it going forward.
• Assumptions: We must make assumptions since it is based on historical data. The assumptions may not be rationally sound at times, so they must be verified before being used in the model.
• Requires time: Running the simulations requires a lot of time and computing resources. Accelerating the model and reducing the number of runs can reduce the total time. This can be achieved through metamodels, also known as emulators .

Sensitivity: How is it measured?

We have understood that the simulation analysis determines the sensitivity of the output to changes in inputs, but how do we do that?

Below are the steps to measure sensitivities:

• Determine the base case output.
• Change an input variable by a certain percentage.
• Determine the percentage change in the value of the output.
• Compare the change in output to the change in the input. This is done by dividing the percentage change in the output by the percentage change in the input.

For example, let's consider a 10-year annuity with annual payments of $100 and a discount rate of 8%.

• In the base case scenario, the NPV of the annuity is $671. 
• Now, the number of years is reduced by 10% from 10 years to 9 years.
• The new NPV would be $624.69. The percentage change in NPV is 6.9% (($671 - $624.69) / $671).
• The measure of sensitivity would be calculated as 6.9% / 10% = 0.69 or 69%.

This process is repeated to obtain the measure of sensitivity for each input while keeping all the other inputs the same. A higher measure of sensitivity for an input implies that the output is more sensitive to changes in that input.

Example of sensitivity analysis

Abe operates a fast-food franchise on a busy street. He would like to understand the impact of footfall on sales of a particular item. He observes that total revenue from that item depends on the price and volume sold.

The item's price is $2, and he sells 100,000 units on average every year, which means that annual sales from that item amount to $200,000. He further concludes that a 15% increase in footfall increases the sales volume by 10%. 

Based on his observations, he makes a three-statement model . He then incorporates what-if analysis in his financial model to answer some what-if questions. For example, what affects total sales if footfall reduces by 20%? What happens to the total sales figure if the price increases by 10%?

The model can answer such questions for him. Abe can tell that a 10%, 50%, or 100% surge in footfall could translate into a hike in sales by 6.67%, 33.33%, or 66.67%, respectively. 

The measure of sensitivity is calculated as the percentage change in the output divided by the percentage change in the independent variable, i.e., 0.67 (= 10% / 15%). The analysis shows that revenues are quite sensitive to fluctuations in footfall.

Based on this information, Abe may decide to reduce the prices to boost revenues further during surging footfall or increase the prices, which might reduce overall revenues but boost margins.

Sensitivity analysis in decision-making and decision trees

Decision-makers need a comprehensive view of all information before making any significant decision. For that, they often rely on what-if analysis. It is imperative to know how deviations from planned inputs can impact the outcome. Reruns of the analyses are crucial in obtaining all the information necessary to make informed decisions. 

It helps in understanding not only the uncertainties that are inherent in a model but also its scope and limitations. It also points out the pros and cons of a model. Besides, uncertainty is an inherent part of decision-making.

Therefore, pinpointing the degrees of uncertainty from various sources is vital to making informed decisions. One of the common ways to factor for it is to use probability-weighted expected values in place of uncertain inputs before running a simulation analysis.

A decision tree is a tool that allows users to represent the decision options and their respective probability-weighted outcomes visually and explicitly. It is a useful tool to derive strategies to achieve given objectives. The outcomes having a certain probability of occurrence are known as chance nodes.

To get to the chance nodes, users need to make some decisions. These nodes are called choice nodes or decision nodes.

Decision trees can sometimes be very complex. However, they make the decision process very clear by clarifying the series of decisions necessary to reach the desired outcome.

Once the decision tree analysis is complete, users can implement scenario analysis . Using scenario analysis in a decision tree shows how dependent the strategy is upon probability factors. This way, users can assess the quality of a decision tree analysis.

Sensitivity analysis: Approaches

There are multiple approaches to using what-if analysis. Each approach has its pros and cons. However, they primarily differ in the calculation of the measures of sensitivity.

The most common approaches are below. 

One-at-a-time (OAT)

It is the simplest and most used approach. It involves changing the value of one input factor at a time and observing its effect on the output. The process involves the following steps:

• Change one independent variable while keeping all the other variables at their base values.
• Readjust the value of that independent variable back to its base value and repeat the process with other independent variables.

This process is repeated until the effect of the changes in all independent variables has been observed. Sensitivity is then determined by examining the changes in the output by using linear regression or partial derivatives . This is a simple approach as by changing one variable at a time, users can unmistakably track the impact of those changes.

However, what makes this approach so simple is also its biggest pitfall. It does not explore the impacts of simultaneous changes in inputs. It also fails to detect multicollinearity between the independent variables. This makes it unfit for nonlinear and multifactor models.

Regression analysis

Regression analysis is another simple approach to what-if analysis. It has a few variations (linear, nonlinear, and multiple linear) which may be used depending on the use case. The regression coefficients are directly used as the measures of sensitivity.

Simple linear and multiple linear regression analyses are the most used variations. In contrast, nonlinear regression is used for complex data where the independent and dependent variables demonstrate a nonlinear relationship.

Since multiple regression tests for the relationship between a dependent variable and multiple independent variables, users must ensure the absence of multicollinearity, i.e., independent inputs should have zero to very low correlations among them.

If they are highly correlated, the effect of the independent variables on the dependent variable will be difficult to assess.

Although this approach can evaluate different types of relationships between the inputs and the output, it is ideally used when the model is linear. The regression must be linear; otherwise, it can be problematic to understand what the standardized coefficients are implying.

The Capital-Asset Pricing Model ( CAPM ) and the subsequent asset pricing models based on the CAPM (like the Fama-French model) are built on regression analysis. While the CAPM employs linear regression analysis, the Fama-French model employs multiple linear regression.

Another common use of regression analysis in finance is in forecasting financial statements . Multiple regression analysis might be more suited to determine the impact that changes in model drivers will have on revenue and expenses.

Variance-based sensitivity analysis

It is often called the Sobol indices or the Sobol method. It works in a probabilistic framework and breaks down the variance of the output variable of the model into parts which are then attributed to independent inputs or sets of independent inputs.

A variance-based analysis is useful for a variety of reasons.

• It gauges sensitivity through the entire input space; thus, it is a global method.
• It can work with nonlinear model responses.
• It can also quantify the effects of interactions in non-additive models.

Let's assume a model with two inputs (x and y) and one output (m). Using variance-based analysis, a user might discover that 40% of the total variance in m results from x, 50% results from y, and the remaining 10% results from interactions between x and y.

The percentages are directly used as the sensitivity measures. Other approaches to simulation analysis include:

• Derivative-based Local Methods
• Variogram Analysis of Response Surfaces (VARS)
• Scatter Plots

Sensitivity analysis vs. Scenario analysis

Scenario analysis helps users assess various outcomes under different scenarios, as the name suggests, ranging from the best-case scenario to the worst-case. It produces a range of outcomes by altering more than one independent variable at the same time to analyze the overall situation.

Therefore, the purpose is to study the range of outcome scenarios. It is done to find out what possible outcome scenarios look like, say, for a farmer, if the tax regime and the weather conditions change for the worse at the same time.

What-if (sensitivity) analysis also produces a variety of outcomes, so what's the difference?

Unlike scenario analysis, what-if analysis tweaks one input at a time to observe its effects on the outcome. Here, the purpose is to study the effects of each of the input variables on the target variable.

For example, a What-if analysis would be conducted to analyze separately what impact the weather conditions and the tax regime have on the farmer's earnings.

Even though both analyses work similarly by altering inputs, they have different purposes and workings. Using both tools can provide users with a broader view of future outcomes.

Please refer to this webpage for a detailed breakdown of the differences between the two.

Sensitivity analysis – Model template

To help our readers understand this topic better, our finance experts have created a template for this kind of analysis. We believe that a more hands-on approach will help you better understand the topic.

Download WSO's free financial modeling templates below! This template allows you to build your table to demonstrate the effect of various variable changes on the outcome.

It is plug-and-play, and you can enter your numbers or formulas to auto-populate output numbers. It also includes other tabs for other elements of a financial model. 

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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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Sensitivity Analysis

Written by True Tamplin, BSc, CEPF®

Reviewed by subject matter experts.

Updated on September 04, 2023

Fact Checked

Why Trust Finance Strategists?

Table of Contents

Sensitivity analysis: definition.

Sensitivity analysis is a powerful financial modeling technique that allows decision-makers to assess the impact of changes in key input variables on the outcome of financial models.

By analyzing the relationships between independent and dependent variables, sensitivity analysis enables organizations to identify potential risks and opportunities, improving the quality of their financial decision-making.

Sensitivity analysis involves examining what happens to a budget when changes are made in the assumptions on which it is based.

It is also known as what-if analysis , and it can be carried out using a spreadsheet or manual calculations.

Manual calculations are easier if they focus only on the parts of the budget that are subject to change.

Importance of Sensitivity Analysis

Sensitivity analysis has various essential applications in today's businesses.

For example, sensitivity analysis can be applied to determine how sensitive the cash budget is to possible changes in the initial assumptions.

If a change in one assumption generates only a small cash difference, there isn't any reason to be too concerned.

However, a change in one assumption can sometimes lead to dramatic shifts in the company's cash position.

Sensitivity analysis, therefore, is useful to determine which assumptions are critical and which have less impact.

The technique investigates the impact that changes would have on the budget, ensuring managers are aware of how the situation could vary from our expected position.

Sensitivity analysis is sometimes called "what-if analysis." This is an excellent name because it perfectly sums up what it focuses on.

The technique simply shows the analyst what will happen to the budget if changes happen.

The procedure for sensitivity analysis involves "trying out" various alterations from the original assumptions to assess the impact.

This can be achieved by changing one category of receipts or payments (e.g., what if the purchase price increases by 5%?).

It can also be achieved by investigating the effect of more than one change in combination (e.g., what if the purchase price increases by 5% and we have to pay off a loan in one month, not two?).

You may be asked to perform sensitivity analysis in a given task, so we'll examine the numerical techniques shortly.

Before we do that, it's worth examining an important practical tool for carrying out sensitivity analysis: the computer spreadsheet.

Using a Spreadsheet for Sensitivity Analysis

The format of a cash budget is easy to reproduce on a computer spreadsheet. Formulas are used to help us avoid having to carry out all the arithmetic.

Once you've opened a spreadsheet, it's a simple matter of inputting data or adding new rows/columns (e.g., for additional receipts or payments).

When the spreadsheet for a cash budget is configured correctly, the total amounts for receipts and payments will automatically adjust when changes are made, together with the bank/cash balances at the bottom of the budget.

An example of a cash budget layout is shown below. There are two illustrations in this example:

• A normal view, showing the figures (upper illustration)
• An alternate view showing the formulas used (lower illustration)

Although the practical use of spreadsheets is difficult to incorporate into AAT simulations, the knowledge and understanding requirements of the unit include computer modeling.

Performing Sensitivity Analysis Manually

The types of changes to data that are applied in sensitivity analysis generally fall into one of three categories. These categories are discussed below.

1. Changes in Underlying Volumes

Changes in underlying volumes are changes in the sales units or the production or purchase units. To some extent, it could apply to overheads or fixed assets (e.g., hiring or buying additional equipment not included in the original budget).

2. Changes in Prices

We will initially deal with straightforward price changes. In the next section, we will examine the impact of inflation and how to deal with it.

3. Timing Changes

We also need to assess the impact of situations in which the receipts and payments are the same as the original cash budget but they occur at different times.

Examples include allowing longer (or shorter) credit terms on sales or paying for purchases or other outgoings at a different time than was originally planned.

There are two approaches used to change the cash budget data:

• Redraft the whole cash budget: While this is simple when working with spreadsheets, it would be very time-consuming manually, especially if there were several options to consider.
• Calculate the impact on monthly cash movements by examining the changes proposed: This approach requires the application of some logic to the problem but it is quicker than redrafting the whole budget

The second technique is often required in AAT simulations. This article will now examine in more detail how this is carried out.

The key is to consider each month separately and perform the calculations for that month:

• Any change in receipts, and
• Any change in payments, that together result in
• A change in cash movement

The revised closing cash balance for that month can then be calculated and carried forward to the next month. The exercise can be carried out in the form of a table.

Sensitivity Analysis Examples

We will use a straightforward example to demonstrate the process used to perform sensitivity analysis.

The following cash budget is based on all sales made on two month's credit. March sales are estimated at $8,000.

Suppose that we want to determine the impact of changing the terms of sale to one month's credit, with effect from January sales. For simplicity, we will assume that all our customers comply with the revised terms.

By following the procedure outlined above, we would get the following results:

In the above example, two months' sales receipts would arise in February, increasing the receipts for that month by $6,000.

The changes in receipts for March and April result from receiving different month's sales than originally planned.

The overall result is a new bank balance at the end of April of $5,000 instead of the original overdrawn figure of $3,000.

Note that using this technique, it is only necessary to examine those lines in the cash budget that are subject to change.

Here, there was no impact on payments, and so there was no need to revisit those figures.

Key Components of Sensitivity Analysis in Finance

Variables and parameters.

The foundation of sensitivity analysis lies in understanding the relationships between variables and parameters in financial models.

Independent Variables

Independent variables are input variables that can change, affecting the outcome of a financial model. Examples include interest rates , inflation rates, and growth rates.

Dependent Variables

Dependent variables are the output variables that are influenced by the independent variables. Examples include net present value (NPV), internal rate of return (IRR) , and stock prices.

Assumptions and Scenarios

Assumptions and scenarios form the basis for sensitivity analysis, allowing analysts to test the effects of different input variables on financial models.

Baseline Scenario

This represents the most likely set of assumptions, often based on historical data and current market conditions.

Alternative Scenarios

These are variations of the baseline scenario, incorporating changes in key assumptions to assess their impact on dependent variables.

Financial Models

Several financial models can be used in sensitivity analysis, including:

Discounted Cash Flow (DCF) Model

DCF estimates the value of an investment based on its expected future cash flows, discounted to their present value.

Capital Asset Pricing Model (CAPM)

CAPM calculates the expected return of an asset based on its risk relative to the overall market.

Black-Scholes Option Pricing Model

This model determines the fair price of an option by considering factors such as the stock price, strike price , and time to expiration.

Sensitivity Analysis Applications in Finance

Investment analysis.

Sensitivity analysis plays a crucial role in evaluating investments, including the valuation of stocks and bonds , and capital budgeting decisions.

Valuation of Stocks and Bonds

By adjusting key variables such as interest rates, growth rates, and dividend payout ratios , analysts can assess the impact of these changes on the valuation of stocks and bonds, providing insights into potential risks and rewards.

Capital Budgeting Decisions

Sensitivity analysis helps in evaluating the viability of investment projects by analyzing the impact of changes in variables like costs, revenues , and discount rates on the project's net present value (NPV) and internal rate of return (IRR).

Risk Management

Risk management is another area where sensitivity analysis can be invaluable, as it helps organizations identify, assess, and mitigate various risks, including credit risk, market risk, and operational risk.

Credit Risk Assessment

Sensitivity analysis can be used to assess the potential impact of changes in variables such as interest rates, borrower credit scores , and economic conditions on loan portfolios.

Market Risk Assessment

By analyzing the impact of changes in market variables like exchange rates , interest rates, and asset prices, sensitivity analysis can help organizations understand their exposure to market risk and develop appropriate hedging strategies .

Operational Risk Assessment

Sensitivity analysis can assist in identifying and quantifying the potential impact of changes in operational variables, such as production costs , labor rates, and regulatory compliance costs, on an organization's financial performance.

Financial Forecasting

Sensitivity analysis can be used to improve the accuracy and reliability of financial forecasts, including revenue and earnings forecasts, cash flow projections, and budgeting and financial planning.

Revenue and Earnings Forecasts

By adjusting key variables such as sales growth rates, pricing, and customer retention, sensitivity analysis can help organizations develop more accurate and robust revenue and earnings forecasts.

Cash Flow Projections

Sensitivity analysis enables organizations to assess the impact of changes in variables like working capital requirements, capital expenditures , and financing costs on their cash flow projections.

Budgeting and Financial Planning

By incorporating sensitivity analysis into their budgeting and financial planning processes, organizations can identify potential risks and opportunities, allowing for more informed decision-making and resource allocation.

Limitations and Challenges of Sensitivity Analysis in Finance

Sensitivity analysis has its limitations and challenges, which must be considered when interpreting and applying its results.

Subjectivity of Assumptions and Scenarios

The quality of sensitivity analysis depends on the assumptions and scenarios used, which are often subjective and can be influenced by the biases of the analysts involved.

Overemphasis on Specific Variables

Focusing too heavily on certain variables can lead to an overemphasis on their importance, potentially resulting in a misallocation of resources or incorrect decision-making.

Incomplete or Inaccurate Data

Sensitivity analysis relies on the availability and accuracy of data. Incomplete or inaccurate data can lead to misleading results and flawed decision-making.

Simplification of Complex Relationships

Financial models often simplify complex relationships between variables, which can result in a distorted view of reality. Sensitivity analysis may not fully capture these complexities, leading to an oversimplification of the relationships between input and output variables.

Importance of Sensitivity Analysis in Financial Decision-Making

Sensitivity analysis is a vital tool for financial decision-making, as it enables organizations to assess the potential impact of changes in key input variables on financial models.

By understanding the risks and opportunities associated with various scenarios, decision-makers can make more informed choices and better allocate resources.

Best Practices for Conducting Sensitivity Analysis

To ensure the effectiveness of sensitivity analysis, organizations should adopt best practices, such as using a diverse range of scenarios and assumptions, incorporating a variety of sensitivity analysis techniques, and presenting results in a clear and accessible format.

Continuous Improvement and Adaptation to New Information and Circumstances

Sensitivity analysis should not be viewed as a one-time exercise but rather as an ongoing process of continuous improvement and adaptation to new information and circumstances.

By regularly updating models and assumptions, organizations can ensure that their sensitivity analysis remains relevant and useful in guiding their financial decision-making.

Sensitivity Analysis FAQs

What is sensitivity analysis, and how is it used in financial modeling.

Sensitivity analysis is a technique used in financial modeling to evaluate how changes in a specific input variable affect the output. It helps to identify the variables that have the most significant impact on the model's results.

What are the benefits of sensitivity analysis in risk management?

Sensitivity analysis enables risk managers to identify the most critical risk factors and evaluate how changes in these factors affect the overall risk exposure of a portfolio.

How does sensitivity analysis differ from scenario analysis?

While scenario analysis considers the impact of multiple variables in a given scenario, sensitivity analysis focuses on the impact of changes in a single variable, allowing for a more detailed analysis of its impact on the model's results.

How can sensitivity analysis help in decision-making?

Sensitivity analysis helps decision-makers to evaluate different scenarios and identify the critical factors that affect the outcome. This information can be used to make more informed decisions and develop robust strategies.

What are some common tools used for sensitivity analysis?

Some common tools used for sensitivity analysis include tornado diagrams, spider plots, and Monte Carlo simulation. These tools help to visualize the sensitivity of the model to changes in input variables and enable more informed decision-making.

About the Author

True Tamplin, BSc, CEPF®

True Tamplin is a published author, public speaker, CEO of UpDigital, and founder of Finance Strategists.

True is a Certified Educator in Personal Finance (CEPF®), author of The Handy Financial Ratios Guide , a member of the Society for Advancing Business Editing and Writing, contributes to his financial education site, Finance Strategists, and has spoken to various financial communities such as the CFA Institute, as well as university students like his Alma mater, Biola University , where he received a bachelor of science in business and data analytics.

To learn more about True, visit his personal website or view his author profiles on Amazon , Nasdaq and Forbes .

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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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Sensitivity Analysis: Evaluating Financial Risks and Opportunities

✅ All InspiredEconomist articles and guides have been fact-checked and reviewed for accuracy. Please refer to our editorial policy for additional information.

Sensitivity Analysis Definition

Sensitivity analysis is a financial modeling tool used to understand how the variability in the output of a mathematical model or system can be influenced by different input variables. It allows financial analysts to predict the potential impact of specific changes and assess risk, making it an integral part of planning for variable business conditions.

Purpose of Sensitivity Analysis

In the realm of business, sensitivity analysis emerges as a vital tool utilized in financial management and planning. Its fundamental purpose spins around the concept of risk management, significantly adding to the decision-making process.

Understanding Potential Outcomes

To begin with, sensitivity analysis assists businesses in understanding the potential outcomes based on various scenarios. It is imperative, since it helps in forecasting the financial outcome by altering one variable at a time, while others remain constant. By doing this, companies can determine the potential 'what if' scenarios and their consequences on the overall financial position of the organization.

Aid in Risk Management

From a risk management perspective, sensitivity analysis supports businesses in identifying the risks associated with changes in specific variables of a financial model. Organizations can effortlessly examine how changes in these variables can impact their business and, thus, develop potential strategies to ameliorate or manage this risk effectively. With the inputs from sensitivity analysis, businesses can highlight major risk points and process underlining their business operations.

Assist in Strategic Decision Making

Decision making is another crucial area where sensitivity analysis plays a paramount role. Companies, small or big, make numerous strategic decisions every day, which are often predicated on uncertain factors or variables. With sensitivity analysis, these companies can evaluate and compare different scenarios, forming a base for the strategic decisions to be made.

To sum up, sensitivity analysis is an instrumental tool in organizations' financial planning and management. It helps in predicting potential outcomes, identifying risks, and aiding in strategic decision making, all of which together assists in securing smoother business operations and fostering sustainable growth.

Components of Sensitivity Analysis

The first component of sensitivity analysis is the identification and assessment of variables. These are factors that could potentially impact the outcome of a project or investment. In the context of sensitivity analysis, these variables will be adjusted to forecast the impact they might have on the final outcome. Variables in an economic context might include interest rates, inflation, customer demand, and operational costs among others.

Base Case Scenario

The base case scenario — often called the expected case — is another crucial component. This represents the most likely outcome or circumstance under normal conditions without any modifications. It serves as the reference point against which all other scenarios in the sensitivity analysis will be compared.

Range of Outcomes

Sensitivity analysis also entails creating a range of outcomes based on the modification of the variables. This range is created by adjusting the variables one at a time, although combinations of variables can also be altered. Each unique adjustment to the variables creates a new scenario within the range of outcomes. The main purpose of this range is to understand how drastically outcomes can change based on different possible scenarios.

Robustness of the Model

The robustness of the model being used is another important facet in sensitivity analysis. This refers to the stability of the model when the input variables are manipulated. If a model's results vary too widely with minimal changes, it may not be robust enough for reliable use, indicating that it needs refinement.

Decision Rules

Sensitivity analysis also includes establishing decision rules. These are the criteria that will be used to judge the outcomes of the sensitivity analysis. It could be a goal or limit that if exceeded would tip the decision toward or away from a certain option. For example, it might be a rule that if the potential return on investment falls below a certain threshold, then the project is deemed not viable.

Uncertainties

Lastly, sensitivity analysis also involves acknowledging and understanding uncertainties. These are factors that are entirely unpredictable or beyond control, such as market volatility or regulatory changes. Including uncertainties in sensitivity analysis provides a more realistic range of potential outcomes.

Executing these components thoroughly provides a solid, comprehensive sensitivity analysis that can enable decision-makers to forecast different results based on changing circumstances and make informed choices.

Running a Sensitivity Analysis

Running a sensitivity analysis involves several meticulous steps. The process involves identifying critical variables, defining a range for these variables, and analyzing the outcomes.

Identifying Critical Variables

The first step in a sensitivity analysis is identifying the critical variables. These variables are the key inputs that have the potential to impact your analysis or model. The goal throughout this process is to isolate these inputs to understand their influence on the output.

To identify these critical variables, consider factors such as industry research, historical trends, future projections, and expert opinion. Remember, the goal is to pinpoint the variables that have the most significant impact on your model.

Defining a Range for the Identified Variables

Once you've identified your key inputs, the next step is to assign a range to each. This range indicates the potential spread or fluctuations an input might experience. This could be based on historical data, potential future events or market changes, or statistical modeling.

Each variable is assigned a 'low' and a 'high' value, thereby creating a range. It is essential to be realistic while defining these ranges. Inputting overly optimistic or pessimistic values may not yield valuable results.

Analyzing the Outcomes

After defining a range for each identified variable, the analysis phase starts. The process involves altering one variable at a time from its low to high range values while keeping others at their base levels. This way it’s easier to understand how changes in a single variable influence the result.

It is typically recommended to run several scenarios, changing one variable at a time. This way, you can understand how susceptible your model is to changes in each specific variable. It also allows you to identify any nonlinear dependencies.

By the end of this process, you should be able to understand the variables to which your model is most sensitive. Also, examining the outcomes under different scenarios provides a comprehensive understanding of the potential risks and opportunities.

To summarize, a sensitivity analysis includes identification of critical variables, defining a realistic range for these variables based on existing data or future projections, and analyzing the impact of these variables when they are strained under different scenarios. This step-by-step approach would help in determining potential inconsistencies, risks, and vulnerabilities of the analysis or model.

Understanding Outputs of Sensitivity Analysis

To fully understand the outputs of sensitivity analysis, it is important to remember that the primary goal of sensitivity analysis is to help identify the variables that have a significant impact on a particular outcome or set of outcomes. Once these key inputs are isolated, businesses can focus their efforts and resources on effectively managing these variables.

Interpreting Outputs of Sensitivity Analysis

The findings of a sensitivity analysis are normally presented in graphs and tables displaying how variations in the input variables affect the outcomes. In these graphical representations, the Y-axis typically represents the outcome of interest (such as net profit or loss), and the X-axis indicates the variable under consideration.

When looking at such tables or graphs, the steepness of the slope indicates the sensitivity of the model to changes in the corresponding variable. A steeper slope means the outcome is highly sensitive to changes in that particular variable. Conversely, a flatter slope suggests the output is less affected by changes in this variable.

Implications of Sensitivity Analysis

The implications of sensitivity analysis for financial planning, strategy, and decision making are multi-faceted. An understanding of the outputs from sensitivity analysis helps businesses identify risks and uncertainties in their financial model. This can inform strategic planning by highlighting which variables carry the greatest potential for impact, both positive and negative, on performance.

For example, if sensitivity analysis reveals that a company's profit is significantly influenced by the price of a raw material, it might decide to negotiate longer-term contracts to mitigate the cost variation. If it shows that customer demand is highly sensitive to price changes, the business might consider using pricing strategies that can help maintain stable demand.

In decision-making, the outputs of sensitivity analysis can help leadership weigh the costs and benefits of different options. Understanding the sensitivity of key outcomes to changes in underlying variables can provide valuable insights into which decisions might lead to the most attractive results, given the uncertainties inherent in business operations.

In summary, interpreting the outputs of sensitivity analysis equips businesses with the capability to understand essential drivers, manage risks, and align strategies with the realities of their operating environment. This consequently strengthens the robustness and resilience of their decision-making processes.

Practical Applications of Sensitivity Analysis

Sensitivity analysis plays a fundamental role in a wide array of fiscal decisions, from investment appraisals to budgeting to cost optimization.

Investment Appraisals

In investment appraisals, sensitivity analysis is used to measure how different values of an independent variable impact a particular dependent variable under a given set of assumptions. This approach is often used by business analysts to evaluate the uncertainty present in forecasting models, providing critical insights that aid in the investment decision-making process. By illustrating how variables can sway the net present value or internal rate of return, sensitivity analysis helps the decision makers analyze the potential risk and return of an investment under different scenarios.

Budgeting and Forecasting

In the realm of budgeting and forecasting, sensitivity analysis is also essential. Businesses often have to cope with uncertainty and make predictions based on a variety of factors, some of which may be prone to significant fluctuations. Sensitivity analysis allows them to quantify the potential impact of such changes, aiding in both the formation of solid contingency plans and the identification of crucial budget streams. Using sensitivity analysis, businesses can assess the potential impact of changes, such as variations in sales volumes, cost of goods sold, or overhead costs, on their budget.

Pricing Strategies

Sensitivity analysis also aids in shaping pricing strategies. As pricing is a crucial determinant of profitability, understanding how alterations in price points impact the company’s financials is vital. Sensitivity analysis can be used to ascertain how fluctuations in price levels affect the bottom line and thus can aid in the formulation of dynamic pricing strategies that optimize revenue and profitability.

###Cost Optimization

In cost optimization efforts, sensitivity analysis clarifies the effects of various factors on the cost structure of a project or business overall. By systematically adjusting variables, businesses can observe how shifts in parameters such as material cost, labor hours, or energy consumption alter the cost outcome. Armed with this knowledge, businesses are better equipped to manage costs and allocate resources more efficiently.

It's clear that sensitivity analysis is a crucial tool across a variety of business domains, providing clarity and direction in situations involving financial uncertainty.

Limitations of Sensitivity Analysis

Uncertainty in variable selection.

One of the most significant limitations of sensitivity analysis is its dependency on the selection of input variables. Notably, a lack of understanding of what factors are most relevant can lead to either ignoring crucial data or including irrelevant ones, both of which can significantly swing the results.

Inability to Account for Interdependent Variables

Sensitivity analysis is often conducted by changing one variable at a time while keeping others constant. This falls short when dealing with interdependent variables, where the change in one simultaneously affects the others. Such interconnected scenarios can distort the sensitivity analysis because real-world changes are rarely limited to one isolated variable.

Over-simplification of Complex Economic Realities

Sensitivity analysis can provide an over-simplified snapshot of economic realities. For example, it might assume linear relationships between variables, which may not always hold true. Additionally, it often doesn't account for external factors such as changes in policy, market competition, or socio-economic trends, which can significantly influence the forecasted outcomes.

Under-representation of Scenario Variation

While sensitivity analysis tests the response to changes in variables, the chosen range of variation can limit its effectiveness. If the applied changes are either too small or too large, the results may not provide an accurate reflection of reality, leading to misguided decisions.

Issues with Multiple Outputs

In cases where there are multiple output variables, sensitivity analysis may not provide clear information on which input variables are the most influential across all outputs.

Dependence on Model Accuracy

Finally, the accuracy of sensitivity analysis is wholly dependent on the accuracy of the underlying model. A model built on false assumptions or bad data will provide skewed results, irrespective of how correctly or thoroughly the sensitivity analysis is conducted.

Sensitivity Analysis in Corporate Sustainability Reporting (CSR)

Sensitivity analysis is a crucial tool in Corporate Sustainability Reporting (CSR) and contributes significantly in predictive analysis related to environmental, social, and governance (ESG) risks.

Use of Sensitivity Analysis in CSR

One of the overarching goals of CSR is to ensure that a company operates in a manner that's socially, economically, and environmentally responsible. Sensitivity analysis provides a solid method for gauging potential compliance and sustainability risks.

In terms of environmental risks, for instance, sensitivity analysis can help evaluate how susceptible a business might be to changes in environmental regulations, legislation or disasters. By simulating various scenarios within the analysis, companies can forecast potential impacts, engage in strategic planning, and initiate damage control measures.

Impact on Social and Governance Risks

Similarly, sensitivity analysis aids in quantifying social risks such as labor unrest, poor community relationships, changes in public sentiment, and shifts in customer behavior. It's instrumental in determining the sensitivity of a corporation's performance to these changes.

From a governance standpoint, sensitivity analysis is also immensely useful. It can help a company anticipate potential implications of a range of issues from legislative changes and fines to risks associated with unethical practices or poor management decisions.

Key Role in Risk Mitigation

Therefore, sensitivity analysis forms a cornerstone of risk mitigation in CSR and reinforces a company’s commitment to long-term sustainability. By gaining a comprehensive understanding of ESG risks, corporations can build resilience, enhance their reputation, and potentially avoid significant financial damage.

It’s important to note, however, that the effectiveness of sensitivity analysis depends on the quality and accuracy of data used in these predictive models. Regularly updating the models with the most current data is crucial for making accurate predictions and developing suitable response strategies.

Use of Sensitivity Analysis in Various Industries

Sensitivity analysis serves as an invaluable tool in several burgeoning and high-stakes industries such as pharmaceuticals, alternative energy, and tech start-ups.

Pharmaceuticals

The pharmaceutical industry makes use of sensitivity analysis in the realm of drug discovery, development, and marketing. As the development process of a new drug is econometrically expensive and time-consuming, firms broadly apply sensitivity analysis to determine the factors that could impact costs, profitability, and success rates at different stages.

In the discovery phase, sensitivity analysis can help in identifying the influential parameters during drug formulations. Should a particular variable cause a substantial difference, then a further detailed study might be conducted to better mitigate the risks.

During the marketing phase, sensitivity analysis is used to model patient behavior and market trends. This strategic use helps pharmaceutical companies isolate influential factors in drug sales, allowing them to better plan their marketing and sales strategies.

Alternative Energy

Sensitivity analysis sees substantial use within the alternative energy sector. With several factors influencing the cost and efficiency of renewable energy, sensitivity analysis helps identify which variables play a more dominant role.

Variables such as geographical location, technology employed, capital cost, and policy incentives all play a role in the profitability of renewable energy projects. Sensitivity analysis provides key insights into how alterations of these variables may impact the overall profitability, thus guiding decision-making objectives and strategies.

For instance, solar power companies often use sensitivity analysis to evaluate the potential return on investment under different scenarios – involving factors like installation costs, energy prices, governmental policies and subsidies.

Tech Start-Ups

For tech start-ups operating in highly uncertain and quickly evolving markets, applying sensitivity analysis is a common practice. As tech start-ups hinge on fast growth and a hyper-competitive environment, it is critical for them to understand which parameters hold the highest impact on their success.

Through sensitivity analysis, tech start-ups can study their financial forecasts under different scenarios – like changes in customer acquisition costs, deck growth rates, or unit economics. The insights derived help mitigate risks, showcase potential vulnerabilities, and aid in more informed decision making for strategies related to scaling or fundraising.

In conclusion, regardless of industry, sensitivity analysis aids in honing our understanding of complex systems, illuminating the most significant factors driving outcomes. This, in turn, enhances strategic planning and decision-making across diverse fields.

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Sensitivity analysis aims to eliminate uncertainty about the future by modeling financial risks and decisions. Also called what-if analysis, this type of analysis examines how changes in inputs affect outputs. The process helps with long-term decision-making. 

Sensitivity analysis is a vital part of any risk management strategy. When used correctly, it can unveil risks, identify lucrative opportunities, and enhance future planning. By illuminating the best path forward, sensitivity analysis serves as a valuable strategic tool. 

Almost every field utilizes sensitivity analysis, including geography, engineering, education, and finance. Organizations and businesses can use sensitivity analysis in a variety of ways, from analyzing how customer traffic impacts sales to seeing how capital investments impact revenues. Even individual investors can use sensitivity analysis to make better price predictions.

No matter how or why you use sensitivity analysis, it’s crucial that you do so the right way. Here, we’ll discuss how sensitivity analysis should work, as well as all the sensitivity and risk analysis techniques you should know.

Sensitivity and risk analysis: your organization’s GPS

Sensitivity analysis functions like a GPS: it maps the best way forward while helping you avoid major pitfalls, like traffic, bad weather, or worse. 

If you hope to future-proof your organization, you’ll need to incorporate sensitivity analysis into your risk management strategy. This type of analysis helps you identify the best direction for your organization by looking at potential real-world situations. Events in the real world are multi-dimensional, so your risk analysis should be, too. 

Advanced sensitivity analysis can perform limitless “ what-if” analyses without ever changing the underlying model. This lets analysts quickly test the impact of independent variables on dependent variables and uncover key drivers that have the greatest impact on the business.

By enabling you to rapidly stress-test different decisions, sensitivity analysis gives you a broad view of the relationship between independent and dependent variables, as well as their financial consequences.

This holistic, unified view makes it easier for analysts and stakeholders to work together, understand all possible outcomes, and make effective, well-informed decisions.

Risk management and sensitivity analysis steps

As an analytical framework for handling uncertainty, risk analysis not only aims to decrease the likelihood that your organization takes on bad projects, but also to increase the likelihood that you discover worthwhile ones. 

Given the benefits of sensitivity analysis to risk management, all organizations should know how to meticulously perform this type of analysis. Below, we’ve outlined a step-by-step process for conducting sensitivity analysis as part of your risk management.

1. Establish a base case

In simple terms, financial models help decision-makers determine what the three most common scenarios look like:

• The best case , or one extreme
• The worst case , or the other extreme
• The base case , or the most likely outcome

The base case is the outcome most people expect to happen. Establishing a base case is the first—and, perhaps, most important—step in performing sensitivity and risk analysis.

Analysts use historical data as well as predictable assumptions (such as growth trends) to establish a base case. For instance, if your company saw 10% revenue growth in the past year, your base case for the following year may have revenue projections that are 10% higher.

It’s crucial that you identify and acknowledge the most expected outcome of each decision. This often means the most conservative outcome, one in which nothing disastrous or amazing occurs.

With this type of control scenario accounted for, you can use sensitivity analysis to rigorously test how even small changes in variables and assumptions could impact your business. 

2. Determine your variables

The next step is to determine the input and output variables that are important to your organization. What particular variables you test will ultimately depend on your project, company, and/or industry. 

For instance, if you run a retail business and are considering whether or not to expand a store, your financial analysts may perform sensitivity analysis to analyze the following input variables: 

• Cost of goods sold
• Construction costs
• Financing costs
• Employee wages
• Manager wages
• Customer traffic
• Cost of utilities

As for output variables, an analyst may look at outputs like internal rate of return (IRR) , net present value (NPV) , discounted payback period, net profits, and share price. It all depends on which output you’d like to sensitize. 

For example, net present value is the output of choice for most analysts when it comes to determining whether a particular project will be profitable, according to the Harvard Business Review .

That’s because NPV analysis—a method of calculating return on investment—accounts for the time value of money. It takes into account the initial investment, the acceptable rate of return, and the stream of cash flows from the investment. 

3. Test the variables

In order to successfully test these variables, you need to build out a financial model that does the following: 

• Organizes all assumptions in one place.
• Easily helps viewers identify inputs and outputs.
• Utilizes charts and graphs for data visualization.
• Identifies linear/nonlinear relationships between independent and dependent variables.
• Makes testing variables as easy as changing numbers in a spreadsheet cell.

Most analysts use spreadsheets to build these models, using ‘what-if’ analysis to determine the impact of each variable on every outcome.

Returning to the example of the retail store expansion, you may be able to answer questions such as: 

• What if foot traffic increases by 10%?
• What if labor costs increase by 5%?
• What if the cost of goods sold decreased by 5%?
• What if the construction loan had a 1% higher interest rate?

By calculating how changes in these independent input variables affect your business outputs, you can determine just how important each variable is to your financial model and projection.

With each unique variable, analysts examine how changes in those variables affect the company’s outputs, such as profit margins and IRR. By exploring a wide set of variables, stakeholders can better visualize future outcomes for their decisions. This simplifies and streamlines decision-making around capital budgeting and business strategy. 

Let’s use net present value as the output for the retail store expansion, just as an example. In order to determine whether the expansion will yield the desired returns, analysts may use the following formula for NPV:

• NPV = (Cash Flow / (1 + Required Return))) t – Initial Investment

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

For instance, lower-than-expected foot traffic could lead to less cash flow, potentially making the project unprofitable. A lower interest rate on the construction loan, on the other hand, may improve the rate of return, increasing the project’s profitability.

Best practices and techniques for sensitivity and risk analysis

It’s vital that your organization use the best sensitivity and risk analysis techniques possible. Otherwise, you won’t get a clear overview of all the future possibilities. That means missing out on identifying key risks and capitalizing on profitable opportunities. 

When performing sensitivity and risk analysis, you’ll want to pay attention to the following techniques and best practices:

Utilize data tables and tornado charts

Your team must go through three steps when performing sensitivity analysis:

• Communicate

This last step is vital: if you don’t communicate your findings, all the hard work of modeling and analyzing the sensitivity of outputs could be wasted.

You need a way to effectively communicate just how important each input is to the business. The best way to do that is through data visualization using tables, charts, and graphs. 

Specifically, you should use: 

• Data tables that list the impact of each variable, organized by highest to least impact
• Tornado charts that sort the variables from most to least impactful 

Understand direct versus indirect methods

Direct analysis methods account for cash flow by adding up operating activities (cash receipts and payments). Conversely, indirect methods account for cash flow by reconciling from net income.

Understanding how these two accounting practices differ is key to proper analysis as they can directly affect the data in your financial models in unexpected ways. 

For example, if you use the direct method, poor tracking of cash inflows can lead to inaccurate data in your sensitivity and risk analysis. While most organizations find the indirect method easier to employ, it’s hard to gain accuracy in real time as adjustments are being made. 

The method you choose depends on your personal preference, as well as the nature of your business or organization. Just be sure you maintain strong records—accurate data is vital to effective sensitivity and risk analysis. 

Use a capable financial model

As mentioned above, most analysts use spreadsheets when performing sensitivity and risk analysis. Unfortunately, this isn’t the best sensitivity and risk analysis technique.

That’s because spreadsheets aren’t really designed to run financial models. In fact, they have a whole lot of drawbacks. Spreadsheets tend to be:

• Too manual : Even advanced spreadsheet users can waste nine hours per week on repetitive manual entry and adjustments. 
• Two-dimensional : Testing one variable at a time is slow and inefficient. You have no way of getting “the big picture” without multiple spreadsheets and models, none of which are easy to update.
• Static and undynamic : Things change daily, but spreadsheets don’t change at all unless you make them change. This isn’t ideal for any fast-moving business.
• Vulnerable to human error : Almost 90% of spreadsheets contain an error . And any single error could compromise the integrity of an entire analysis, rendering it useless.

What you need is a more capable financial modeling solution —one that can update in real-time and test multiple variables at once without needing to change the underlying model. This way, your sensitivity analysis can broaden your view of the future and provide deepened insight into risk exposure. 

A better sensitivity analysis tool for risk management

When you utilize the best sensitivity and risk analysis techniques, you gain greater insight into risk exposure and can more effectively identify new opportunities. This helps your organization find the best path forward. 

But the reality is that you can’t do that with just a spreadsheet. You need agile, intelligent software made specifically for financial modeling. It should let you rapidly stress-test multiple scenarios and accelerate and improve your decision-making. 

Synario’s financial modeling software offers: 

• A simple toggle feature for changing inputs without touching the underlying model
• Patented layering technology that enables testings of multiple variables at once
• Automated object orientation and financial statements to limit errors and increase efficiency
• An out-of-the-box solution that can be quickly customized to run limitless ‘what-if’ analyses

All these features make your sensitivity and risk analysis more efficient and more accurate, meaning you can enjoy greater clarity when it comes to making important decisions for your organization.

Isn’t it time you put yourself in a better position to succeed?

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The Use of Sensitivity Analysis and Scenario Analysis in Your Business Strategy Explained 

• April 10, 2023
• Financial Analysis

Rene Levermann

Business strategy defined  .

Business Strategy is the framework that defines the company’s goals and the direction and ways to achieve these goals. It is an essential component of any organization before offering any product or service. It connects where the company is now to where it wants to go.   

Strategy is important because the future cannot be predicted. Without perfect foresight, businesses must be prepared to deal with the unforeseen circumstances that make up the business environment. A business strategy details the steps needed to achieve the business goals. It helps evaluates how to maximize the company’s strengths and address its weaknesses. It makes it easier to measure how well the organization meets its objectives.     

Businesses increasingly compete globally, highlighting the importance of a solid business strategy to remain viable and profitable. A business strategy must be realistic, adaptable, flexible, and responsive. Likewise, a strategy must adapt to any change or unforeseen incident a business may encounter.    

Sensitivity Analysis Defined  

The technique of determining how sensitive a company’s financial performance is to changes in essential variables is known as sensitivity analysis. Businesses can test numerous scenarios to see how changes in sales, costs, prices, or interest rates affect their profitability. This study can help businesses build risk-mitigation strategies and uncover growth opportunities.  

Scenario Analysis Defined  

Scenario analysis is the approach for evaluating likely outcomes by creating hypothetical situations. This technique is beneficial for investigating external issues that could affect the firm, such as economic developments, industry trends, or consumer behavior. Businesses can discover potential risks and opportunities by analyzing events and developing response strategies.  

The Advantages of Including Sensitivity and Scenario Analysis in Business Strategy  

Businesses can use sensitivity and scenario analysis to fine-tune their strategy and make better decisions. Incorporating them into the strategy process of a firm can also enable it to be competitive and adapt to changing market conditions.   

• Sensitivity and scenario analysis can assist firms in identifying risks and developing strategies to minimize them. Businesses can understand how changes in essential variables affect their financial outcomes and discover areas where they may be weak by testing different scenarios. To illustrate, a company may perform a sensitivity analysis to see how changes in raw material prices may affect its profitability. If the study finds that a significant increase in material costs could significantly impact the company’s bottom line, they may consider implementing a risk-mitigation strategy. Renegotiating contracts with suppliers, finding ways to streamline processes to decrease expenses, or raising pricing to retain profitability are all possibilities.  
• Businesses benefit from sensitivity and scenario research in identifying growth prospects. Businesses can optimize their operations and enhance profitability by testing multiple scenarios and identifying areas where they might boost revenue or cut costs.  For example, a firm may do a scenario analysis to assess the potential impact of introducing a new product. Suppose the analysis indicates the opportunity has vast growth potential. In that case, the corporation may adopt a profit-generating plan, such as investing in R&D or increasing sales and marketing efforts.
• Businesses may foresee and respond to market developments using sensitivity and scenario analysis, allowing them to adjust their strategy as needed.  A business, for example, may do a scenario analysis to examine the potential impact of economic change, such as a recession or inflation. Assume the analysis reveals that the company is vulnerable to these developments. In that case, it may create a risk-mitigation strategy, such as cost-cutting or product diversification.  
• Sensitivity and scenario analysis can help businesses understand how alternative situations affect financial outcomes. This information enables businesses to make more informed and strategic decisions per their goals and objectives.  A company, for example, may do a sensitivity analysis to determine the potential impact of a planned price increase. Assume the analysis indicates that the price increase will considerably reduce sales. In that case, the company may boost earnings by other measures, such as cost reduction or greater sales volume.  

  Factors to Consider in Preparing Sensitivity and Scenario Analysis  

•   When undertaking sensitivity and scenario analysis, it is crucial to use realistic and data-driven assumptions. Businesses should guide their assumptions with historical data and industry benchmarks to ensure they are based on factual information.  
• Companies should consider conducting frequent sensitivity and scenario analysis to ensure their plans stay relevant and practical.
• Thoroughness is another essential factor to consider in conducting sensitivity and scenario analysis. This necessitates the examination of several conditions in terms of revenue, cost, cash flow, and profitability. Businesses can ensure that they capture the full range of potential outcomes and establish a robust and adaptive strategy using a comprehensive approach.
• The sensitivity and scenario analysis results must be fully explained, presenting the analysis clearly and concisely, stressing significant conclusions and insights, and making suitable recommendations. The overall goals and objectives must be conveyed to the stakeholders, such as investors and employees, to make them agree and support these strategic decisions.   

Sensitivity and scenario analysis are valuable tools for companies wanting to strengthen their strategic planning processes. They help organizations be competitive and promptly react to changing market conditions, ensuring long-term success and sustainability.    

Using Financial Models for Sensitivity and Scenario Analysis   

Sensitivity and scenario analysis necessitate using a financial model incorporating key variables and assumptions that influence a company’s financial results.  

Spreadsheet tools such as Microsoft Excel are frequently used to create financial models. Models can range in complexity from simple models that record basic financial statements to more comprehensive models that incorporate precise operational and financial data.  

It is critical to ensure that a financial model for sensitivity and scenario analysis is flexible and adaptable. The financial model should be constructed to capture various situations and changes in critical aspects such as revenues, costs, and pricing. In addition, the model should be built with a thorough understanding of the business and industry and essential benchmarks and KPIs to ensure that the assumptions are practical and data-driven.  

At Levermann Consulting, we provide financial modeling services and can help businesses who want to do sensitivity and scenario analysis. We can assist the company in developing a customized financial model that captures the significant variables and assumptions influencing its financial results. This financial model is a powerful tool that enables us to make more accurate forecasts, conduct sensitivity analyses, and create scenarios for various strategies and business decisions.   

We will provide direction and assistance throughout the sensitivity and scenario analysis process in addition to financial modeling services. We could assist with identifying acceptable scenarios to test, analyzing data, and developing realistic recommendations for improvement projects.   

A company’s long-term performance and sustainability must incorporate sensitivity and scenario analysis into its strategic planning process. Levermann Consulting is dedicated to assisting organizations in developing comprehensive and adaptive financial models to aid decision-making and encourage growth and profitability.  

Plan Projections

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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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How to Do Risk & Sensitivity Analysis in a Feasibility Study

Are you conducting feasibility study on a business project and need help doing the analysis? If YES, here is a complete guide on how to do risk and sensitivity analysis.

The rate at which businesses fail can be traced to lack of business training. Most people jump into a new business once they notice that the business is trending and highly profitable. They fail to carry out their due diligence to know if the business is worth investing in. Part of what you need to do aside undergoing business training before starting your business is to conduct risks and sensitivity analysis for your business.

What is Risk and Sensitivity Analysis?

Conducting risks and sensitivity analysis involves accessing all the risks and sensitive areas that is associated with the type of business you want to start. As a matter of fact, there is no business that is not prone to risks and it is your responsibility as an entrepreneur to access the risks involved in running the type of business you are about to start and also to know whether you have what it takes to shoulder the risks.

Likewise, sensitivity analysis will help you put measure in place that will help you predict the outcome of all the business decisions you make. Simply put, sensitivity analysis will help you increase your understanding of the existing relationship between input and output variables in your business. It is a known fact that succeeding or failing in business is all about the decisions you make.

Although conducting risks and sensitivity analysis will not help you to totally eliminate all the uncertainties of making business decision, but you are sure that it will help you minimize the uncertainty to the barest minimum. Now here the steps you would need to follow to be able to effectively conduct risks and sensitive analysis for your business;

How to Do Risk & Sensitivity Analysis in a Feasibility Study

1. Study and Research

To start with, if you have never conducted a risk and sensitivity analysis for a business before, then you have a lot of work to do. What is expected of you is to spend enough time studying and researching on the subject – risk and sensitivity analysis.

There are materials online and in libraries that will help you achieve your aim of conducting a risk and sensitivity analysis for your business. The truth is that conducting risks and sensitivity analysis is usually done by experts because of the technicality involved, but you can do it yourself if you spend time to study existing models.

2. Collect and Analyze Data and Graphs

Part of what you need to do when studying and researching on risks and sensitivity analysis is to collect and analyze data and graphs that are related to your business. Once you are able to properly analyze data and interpret graphs, it will give you an edge when conducting your risks and sensitivity analysis.

You will be able to pin point areas where you would need to concentrate on. As a matter of fact, you can not effectively conduct risks and sensitivity analysis without the skills to properly analyze graphs and interpret data.

3. Develop a Theoretical Framework for Using Risks and Sensitivity Analysis for Decision Making

Once you are able to analyze the required data and graphs, what is expected of you to do is to develop a theoretical framework for using risks and sensitivity analysis for decision making. The essence of conducting risks and sensitivity analysis for your business is to put structures in place that will help you mitigate risks and uncertainty in your business and in turn maximize profits. You can work with experts to help you create a model that is suitable for your business.

4. Run the Model a Number of Times before Adopting It

It is one thing to develop a theoretical framework that will guide you in decision making in your business, it is entirely another thing for the model to work in real life situation. It is important to run the model you developed for risks and sensitivity analysis for your business a number of times before adopting it in your business.

What is the use of having a fantastic theoretical framework on paper without it working in real life situation? The best time to adjust your theoretical framework is during the process of test running it; once you discover any drawback, then you should go back to the drawing board and restructure or adjust your framework.

5. Review the Document Generated From Your Risks and Sensitivity Analysis

Once you are done with all the steps listed above, you would have succeeded in completing your risks and sensitivity analysis for your business and also you will be able to produce a comprehensive document in that regard. The process will not be complete if you do not review the document generated from your risks and sensitivity analysis. You can engage the services of a professional to help you review the document.

Over and above, if you are successful in conducting your own risks and sensitivity analysis for your business, you would have succeeded in finding the possible outcomes of most of the business decisions that you will make in your business and you will be well guided.

As a matter of fact, some folks consider risks and sensitivity analysis as a systematic common sense technique adopted by many business owners to minimize making wrong decisions that will cost the company.

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Creating a Sensitivity Analysis & Forecast

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Creating Your Forecasted Sensitivity Analysis

After completing your Financial Budgets (step 1) , your First Year Forecasted Cash Flow Statement (step 2), your First Year Forecasted Income Statement (step 3), your First Year Forecasted Balance Sheet (step 4) your First Year Forecasted Ratios (step 5), and your First Year Forecasted Break-even Point (step 6), the next step is to develop your Forecasted Sensitivity Analysis (remember to create your forecasted financial statements and analysis one year at a time).

Recall from previous discussions, a Sensitivity Analysis is a "what-if"tool that examines the effect on a company's Net Income (bottom line) when forecasted sales levels are increased or decreased. For example, a sensitivity analysis can answer the following questions:

• "WHAT" would be my forecasted net income, "IF" my sales forecast is 30%, 20%, or 10% too high ?
• "WHAT" would be my forecasted net income, "IF" my sales forecast is 20% or 10% too low ?

As you might suspect, an original Forecasted Income Statement is needed to create a Forecasted Sensitivity Analysis. In other words, before you can create a 200Z Forecasted Sensitivity Analysis, for example, you MUST prepare a 200Z Forecasted Income Statement (IE the 200Z Forecasted Income Statement becomes the foundation for the 200Z Sensitivity Analysis).

Many business plan writers generally prepare only one Sensitivity Analysis. That is, a sensitivity analysis for their FIRST forecasted year of operation. Therefore, in our example, Murray would prepare a Forecasted Sensitivity Analysis for 200X only (IE his first forecasted business year). Below illustrates Murray's 200X Sensitivity Analysis. (Please Note: Murray can develop a Forecasted Analysis for 200Y if he chooses; however, he elects not too).

As you can see, the sensitivity analysis consist of three main components; namely, 1) The Heading, 2) Sales Percentage Factors, and 3) The Body.  Below briefly explains each component; beginning with "The Heading".  For a complete examination of these three components as they relate to Murray Wilson's company, please click HERE .

Below summaries the Forecasted Financial Statements and/or Budgets that need to be completed before you can develop your Forecasted Sensitivity Analysis.

ADDITIONAL EXAMPLE ON THE SENSITIVITY ANALYSIS

J&B Incorporated

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Sensitivity Analysis: Unraveling the Key Factors Influencing Decision-Making

• By: Magistral Consulting
• June 22, 2023

Introduction

Sensitivity Analysis establishes the impact of various independent variable values on a specific dependent variable under a specific set of assumptions. In other words, sensitivity analyses look at how different types of uncertainty in a mathematical model affect the overall level of uncertainty. This method is applied within defined parameters that are dependent on one or more input variables.

In the realm of business and the study of economics, sensitivity analysis is applied. It is sometimes referred to as a “what-if analysis,” and financial analysts and economists frequently employ it. Finance sensitivity analysis aids in the understanding of potential risks, uncertainties, and trade-offs related to financial decisions. It facilitates risk management, permits informed decision-making, and improves comprehension of the spectrum of potential outcomes in various financial circumstances.

Share price predictions for publicly traded firms can be aided by sensitivity analysis. A few elements that affect stock prices are the company’s earnings, the number of shares in circulation, the ratio of debt to equity (D/E), and the number of opponents in the market. Examining future stock prices can be enhanced by altering the underlying hypotheses or introducing new factors. Using this model, it is possible to determine how changing interest rates affect bond prices. It enables the use of actual historical data for forecasting. Carefully examining all the elements and potential outcomes can help one make crucial decisions about investment, businesses, and the economy.

Applications of Sensitivity Analysis

Sensitivity analysis is a popular tool in finance for determining how changes in input variables or assumptions would affect risk management, investment choices, and other financial applications. Finance sensitivity analysis offers insightful information about potential risks, uncertainties, and trade-offs related to financial models, investment choices, and risk management tactics. It helps with decision-making, risk quantification, portfolio optimization, and improving comprehension of the effects of numerous factors on financial results.

The following are some crucial financial uses of sensitivity analysis:

Pricing and Estimation

Sensitivity analysis is essential for evaluating complicated derivatives, options, bonds, and other financial instruments. Analysts can determine how sensitive the value of an instrument is to various inputs, such as underlying asset prices, interest rates, volatility, or dividend yields. It aids in assessing the impact of changes in market conditions on the pricing of financial instruments as well as recognizing the main drivers of value.

Risk Mitigation

It is useful to comprehend how different market conditions affect portfolio returns, value-at-risk (VaR), or other risk assessments. Sensitivity analysis is used to facilitate stress testing, scenario analysis, and assessing the resilience of financial institutions or portfolios to volatile market conditions.

Asset Distribution and Portfolio Management

Asset allocation and portfolio optimization are accomplished through sensitivity analysis. Analysts can determine the best allocation techniques by evaluating how responsive portfolio returns, risk measures or other performance indicators are to changes in asset weights, correlations, or other portfolio parameters. It helps in assessing the possible effects of asset class returns, monetary considerations, or market conditions on portfolio performance and serves as a roadmap for portfolio modifications.

Making Decisions and Budget Allocation

Financial statement sensitivity to changes in revenue growth rates, cost structures, or interest rates can be assessed by analysts for financial statements like income statements, balance sheets, or cash flow statements. Sensitivity analysis supports decision-making by shedding light on the potential effects of various scenarios on financial performance.

Assessing Investments and Capital Planning

Analysts can determine the sensitivity of investment indicators such as net present value (NPV), internal rate of return (IRR), or payback duration by adjusting important parameters like cash flows, discount rates, or project timelines. This research aids in understanding the range of probable outcomes for various investment situations as well as the most important elements affecting investment profitability.

Benefits of Sensitivity Analysis

Sensitivity analysis in finance offers several benefits that contribute to better decision-making, risk management, and understanding of financial outcomes. It in finance aids in improved risk management, more informed decision-making, and a deeper comprehension of the range of possible outcomes. It aids in quantifying uncertainty, identifying crucial elements, and enhancing stakeholder communication, ultimately resulting in more solid and trustworthy financial strategies and plans. Here are some key benefits of sensitivity analysis in finance:

Risk Assessment of Sensitivity Analysis

Sensitivity analysis is a tool for evaluating and controlling risks related to financial models, portfolios, or investment choices. Analysts can detect and quantify potential risks by examining how sensitive financial outcomes are to changes in important variables. This knowledge improves the ability to adapt to various market conditions and enables the implementation of suitable risk mitigation techniques.

Measurement of Uncertainty

The uncertainty connected to financial models, projections, or investment decisions can be quantified with the aid of sensitivity analysis. Analysts can determine the range of possible outcomes and the likelihood of various scenarios by evaluating the sensitivity of financial outcomes to changes in factors.

Identifying Crucial Factors of Sensitivity Analysis

Sensitivity analysis aids in locating the most important factors or hypotheses that have a major impact on financial outcomes. Analysts can identify the factors that impact the outcomes most by changing the inputs and analyzing how those changes affect the outputs. Decision-makers can focus their attention and resources more effectively and strategically by using this knowledge to identify the most important aspects.

Stress Testing

Scenario analysis and stress testing, which are essential for evaluating the robustness of financial models, portfolios, or institutions, are made easier by sensitivity analysis. Analysts can track how financial outcomes react to difficult circumstances by modeling various scenarios and stress variables. This analysis aids in locating weak points, estimating the impact that extreme events might have, and creating backup plans or risk-reduction tactics.

Better Communication

Sensitivity analysis shows the connections between input factors and financial results simply and visually. Stakeholders and decision-makers can better understand the significance and influence of many variables with the aid of visual tools like tornado diagrams and sensitivity charts. This promotes dialogue, enhances stakeholder understanding, and increases the transparency of financial decision-making processes.

Magistral’s Services on Sensitivity Analysis

Financial models have a long history of being trusted tools for determining the boundaries of trade. Due to a recent surge of acquisitions where investors are willing to pay big premiums for rapid growth or a high-impact technology, traditional financial models have undergone qualitative changes. The following is ensured by Magistral’s sensitivity analysis:

-Analyzing the financial model’s unclear input values.

-Predicting potential outcomes and planning for unanticipated risks.

-Aiding the execution of risk assessment techniques.

-Establishing co-relationships between the model’s multiple inputs and output.

-Execution of well-informed judgments.

About Magistral Consulting

Magistral Consulting  has helped multiple funds and companies in outsourcing operations activities. It has service offerings for  Private Equity, Venture Capital, Family Offices ,  Investment Banks ,  Asset Managers, Hedge Funds, Financial Consultants,   Real Estate, REITs, RE funds ,  Corporates, and Portfolio companies . Its functional expertise is around  Deal origination ,  Deal Execution, Due Diligence,   Financial Modelling ,  Portfolio Management , and  Equity Research .

For setting up an appointment with a Magistral representative: visit www.magistralconsulting.com/contact

About the Author

The article is authored by the Marketing Department of Magistral Consulting. For any business inquiries, you can reach out to   [email protected]

Hasib sharif

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