What is Sensitivity Analysis?

Sensitivity Analysis

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Sensitivity Analysis

Sensitivity analysis is a technique used to understand how different values of an independent variable impact a particular dependent variable under a given set of assumptions. It is commonly used in the business world and in finance to assess the risks associated with a particular decision and to predict the outcome of different scenarios.

Sensitivity analysis provides a way to predict the outcome of a decision if a critical factor changes. In essence, it allows decision-makers to assess “what if” scenarios.

Here’s a breakdown of what it involves:

  • Variable Selection: Identify which variables might be changed. These are often factors that are uncertain or outside the control of the decision-maker.
  • Range Determination: Define a range over which the variable will be changed. This could be based on historical data, forecasts, or other relevant information.
  • Scenario Analysis: For each variation of the independent variable, calculate the impact on the dependent variable.
  • Outcome Interpretation: Analyze the different outcomes to see how sensitive the decision is to changes in the independent variable.

Sensitivity analysis is widely used in many areas, including:

  • Financial Modeling: To see how changes in input variables (like interest rates, growth rates, etc.) can affect a model’s outcome.
  • Project Management: To understand how changes in variables (like project duration or costs) can affect the project’s outcomes.
  • Engineering: To determine how changes in design variables can impact performance.
  • Environmental Studies: To see how changes in variables (like pollution levels or rainfall) can affect environmental outcomes.

Example of Sensitivity Analysis

Let’s look at a hypothetical example of sensitivity analysis in a business context.

Scenario: DreamCoffee, a cafe chain, is considering opening a new branch in a busy city district. They’ve made some financial projections based on expected daily customers. However, they are uncertain about how many daily customers the new branch will attract. To understand the financial implications of different customer volumes, they decide to perform a sensitivity analysis.


  • Average spending per customer: $5.
  • Operating costs for the cafe (rent, wages, utilities, etc.): $500 per day.
  • DreamCoffee assumes an initial average of 150 daily customers for their base scenario.

Sensitivity Analysis:

DreamCoffee wants to understand how the daily profit changes with varying daily customer numbers. They decide to analyze a range from 100 to 200 customers, in increments of 20.

Here’s a simple breakdown:

  • 100 Customers:
    • Revenue: 100 customers x $5 = $500.
    • Profit: $500 (revenue) – $500 (costs) = $0.
  • 120 Customers:
    • Revenue: 120 customers x $5 = $600.
    • Profit: $600 – $500 = $100.
  • 140 Customers:
    • Revenue: 140 customers x $5 = $700.
    • Profit: $700 – $500 = $200.
  • 160 Customers (Base Scenario):
    • Revenue: 160 customers x $5 = $800.
    • Profit: $800 – $500 = $300.
  • 180 Customers:
    • Revenue: 180 customers x $5 = $900.
    • Profit: $900 – $500 = $400.
  • 200 Customers:
    • Revenue: 200 customers x $5 = $1,000.
    • Profit: $1,000 – $500 = $500.


By conducting this sensitivity analysis, DreamCoffee can see that for every 20 additional daily customers, their profit increases by $100. This allows them to make more informed decisions regarding advertising, potential promotions, or even reconsidering their location if attracting a minimum number of customers appears challenging.

Further, if DreamCoffee feels that achieving 140 daily customers is very feasible, they can be reasonably confident of making a daily profit of at least $200. On the other hand, if there’s significant doubt about reaching even 120 customers daily, they might reconsider the viability of this new branch.

This simple example demonstrates how sensitivity analysis can guide business decisions by illustrating the potential outcomes based on variations in key assumptions.

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