Why perform sensitivity analysis

He reasonably concluded that the YoY sales and revenue increases were both solidly due to growth in website traffic. Website traffic also fell, but not by nearly as much.

Overall, revenue fell by Clearly, website sales were less sensitive to the shutdowns than store sales, which makes perfect sense. More importantly, website overhead and associated expenses were significantly lower across the board:. Without looking at the cost of certain fixed overhead expenses, which remained the same across both websites and stores such as employees and vendors , the analyst was able to determine that although website costs rose faster relative to store costs, website expenses were still much lower as a percentage of website sales and revenue.

The analyst was also able to determine that despite ramping up advertising costs in , website sales were still down, just like store sales. Will his recommendations work in ? Only time will tell. But the analyst can confidently state his case—and the retailer can more confidently make a strategic decision—after reviewing the results of this sensitivity analysis. The most popular tool by far for conducting sensitivity analysis and building financial models remains Excel.

Unfortunately, spreadsheets leave a lot to be desired. They require a lot of repetitive, manual entry, leaving little room for error.

Analysts often have to go back to the drawing board and build new spreadsheets from scratch every time stakeholders come up with a new set of questions that need to be answered. This is why sophisticated organizations should consider using financial modeling tools built specifically for sensitivity analysis. Not only does this help analysts uncover key drivers faster, but it also lets them stress test their models across countless scenarios in a fraction of the time it would take in spreadsheets.

Skip to content. What is sensitivity analysis? When push comes to shove, can the model survive all possible worst-case scenarios? What is Model Sensitivity? How will your bottom line be impacted if the construction of a new facility takes 3 months longer than expected? What happens to cash flow if your customer foot traffic drops by 25 percent? Sensitivity Analysis vs. How to do Sensitivity Analysis Every sensitivity analysis can be simplified into three major steps: Establish a base case In sensitivity analysis and scenario planning, the three most common scenarios are called: The best case , or the most optimistic scenario with the highest potential upside The worst case , or the most pessimistic scenario with the highest potential downside The base case , or the most conservative scenario with an outcome squarely between the best-case and worst-case scenarios e.

Determine your variable inputs Input variables may include things like cost of goods sold, debt financing, employee wages, customer foot traffic, etc. Test the variables Once the inputs and outputs are determined, analysts perform sensitivity analysis on the assumed independent variables one by one to rigorously test how sensitive their base case is to even the smallest changes.

You can test your input and output variables by following this simple four-step process: Determine which independent variable you will change first and which dependent variables you will observe. Record the output of your base case scenario after changing the first independent variable.

Record the percentage change of both the input and output variables. Repeat steps 1 and 2 until you have tested all of your independent variables one at a time and recorded how they impact the dependent variables.

Using the appropriate sensitivity analysis formula, calculate how sensitive your base case scenario is to changes in each of your independent variables. A Smarter Way to Perform Sensitivity Analysis The most popular tool by far for conducting sensitivity analysis and building financial models remains Excel. Better Analysis with Synario. Join Our Newsletter. Allow Cookies? We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits.

Sensitivity analysis determines how different values of an independent variable affect a particular dependent variable under a given set of assumptions. In other words, sensitivity analyses study how various sources of uncertainty in a mathematical model contribute to the model's overall uncertainty.

This technique is used within specific boundaries that depend on one or more input variables. Sensitivity analysis is used in the business world and in the field of economics. It is commonly used by financial analysts and economists and is also known as a what-if analysis. Sensitivity analysis is a financial model that determines how target variables are affected based on changes in other variables known as input variables.

This model is also referred to as what-if or simulation analysis. It is a way to predict the outcome of a decision given a certain range of variables. By creating a given set of variables, an analyst can determine how changes in one variable affect the outcome. Both the target and input—or independent and dependent—variables are fully analyzed when sensitivity analysis is conducted. The person doing the analysis looks at how the variables move as well as how the target is affected by the input variable.

Sensitivity analysis can be used to help make predictions about the share prices of public companies. The analysis can be refined about future stock prices by making different assumptions or adding different variables. This model can also be used to determine the effect that changes in interest rates have on bond prices. In this case, the interest rates are the independent variable, while bond prices are the dependent variable.

Investors can also use sensitivity analysis to determine the effects different variables have on their investment returns. Sensitivity analysis allows for forecasting using historical, true data. By studying all the variables and the possible outcomes, important decisions can be made about businesses, the economy, and making investments.

Assume Sue is a sales manager who wants to understand the impact of customer traffic on total sales. She determines that sales are a function of price and transaction volume. This allows her to build a financial model and sensitivity analysis around this equation based on what-if statements.

The sensitivity analysis demonstrates that sales are highly sensitive to changes in customer traffic. In finance, a sensitivity analysis is created to understand the impact a range of variables has on a given outcome. It is important to note that a sensitivity analysis is not the same as a scenario analysis.

The sensitivity analysis is based on the variables that affect valuation, which a financial model can depict using the variables' price and EPS. The sensitivity analysis isolates these variables and then records the range of possible outcomes. On the other hand, for a scenario analysis, the analyst determines a certain scenario such as a stock market crash or change in industry regulation.

He then changes the variables within the model to align with that scenario. Put together, the analyst has a comprehensive picture. He now knows the full range of outcomes, given all extremes, and has an understanding of what the outcomes would be, given a specific set of variables defined by real-life scenarios. A Experimental design: It includes combination of parameters that are to be varied.

This includes a check on which and how many parameters need to vary at a given point in time, assigning values maximum and minimum levels before the experiment, study the correlations: positive or negative and accordingly assign values for the combination. B What tfo vary: The different parameters that can be chosen to vary in the model could be: a the number of activities b the objective in relation to the risk assumed and the profits expected c technical parameters d number of constraints and its limits.

C What to observe: a the value of the objective as per the strategy b value of the decision variables c value of the objective function between two strategies adopted.

This process of testing sensitivity for another input say cash flows growth rate while keeping the rest of inputs constant is repeated until the sensitivity figure for each of the inputs is obtained. The conclusion would be that the higher the sensitivity figure, the more sensitive the output is to any change in that input and vice versa. Local sensitivity analysis is derivative based numerical or analytical.

The term local indicates that the derivatives are taken at a single point. This method is apt for simple cost functions, but not feasible for complex models, like models with discontinuities do not always have derivatives. Mathematically, the sensitivity of the cost function with respect to certain parameters is equal to the partial derivative of the cost function with respect to those parameters. Local sensitivity analysis is a one-at-a-time OAT technique that analyzes the impact of one parameter on the cost function at a time, keeping the other parameters fixed.

Global sensitivity analysis is the second approach to sensitivity analysis, often implemented using Monte Carlo techniques. This approach uses a global set of samples to explore the design space.

One of the key applications of Sensitivity analysis is in the utilization of models by managers and decision-makers. All the content needed for the decision model can be fully utilized only through the repeated application of sensitivity analysis. It helps decision analysts to understand the uncertainties, pros and cons with the limitations and scope of a decision model.