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Sensitivity Analysis vs. Scenario Analysis: What You Need To Know

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The main difference between sensitivity analysis and scenario analysis is that a sensitivity analysis assesses the result of changing one variable at a time, while a scenario analysis examines the result of changing all possible variables at the same time.

Both methods you can use to evaluate the level of risk involved in a variety of situations.

All business decisions are based on a set of assumptions that always come with a certain level of risk. While you may never have the power to see the definitive outcome of every business decision prior to making it, knowing how to utilize effective analytical and forecasting tools is crucial to assess the scale of those risks against the potential benefits.

But when do you use sensitivity analysis vs. scenario analysis? Let's dig a little deeper.

Scenario Analysis: What Is It And When To Use It?

Be it in life or business, nothing is certain, but uncertainty. Scenario analysis provides a structured way to guide strategic decision making by exploring the different possible impacts of a range of events.

A Scenario analysis doesn't aim to predict a single outcome to any of these sample scenarios. Rather, it explores a range of potential outcomes from best- to worst-case scenarios. This allows you to better prepare for a variety of scenarios and to make nimble decisions when they happen in order to still meet specific business objectives.

Scenario analysis allows for a more proactive assessment of potential risk, ultimately driving better decision making as a result. It focuses less on definitive outcomes and more so on predicting several possible outcomes that are all valid, though uncertain. In business cases, it can be used to improve systems thinking and provide clarity on how to best allocate resources.

However, scenario analysis is not without its downsides. It is a fairly time-consuming process that often requires a specific set of skills and expertise to effectively complete. That being said, there are planning platforms available in order to help consider all possible scenarios presented by this method of analysis.

Sensitivity Analysis: What Is It And When To Use It?

Also called "what-if" analysis, sensitivity analysis differs from scenario analysis in that it determines how a change in one variable affects the possible outcome. 

Sensitivity analysis is beneficial specifically for fact checking and providing an in-depth forecast as it deals with the likelihood of success or failure for variable scenarios. In a business setting, you can utilize this forecasting method to predict the return on investment of a specific project.

Something to keep in mind when using this method is that the use of historical data can potentially lead to unreliable results. These types of inaccuracies can occur since past outcomes do not necessarily mean similar future outcomes will take place as well.

Example of Scenario Analysis vs Sensitivity Anlysis

Scenario Analysis Example  

Scenario analysis involves evaluating a range of plausible future outcomes by varying multiple inputs simultaneously. In this context, imagine a coffee shop owner trying to forecast the next year's revenue. The owner identifies three distinct scenarios:

A: Optimistic Scenario:  

  • The local economy thrives.
  • A university opens nearby, increasing foot traffic.
  • No significant competition emerges.

B: Base Case Scenario: 

  • The local economy remains stable.
  • Student numbers remain unchanged.
  • A small competing coffee shop opens two blocks away.

C: Pessimistic Scenario:

  • The local economy enters a recession.
  • A large coffee chain opens next door.
  • Unexpected roadworks divert customer traffic.

For each of these scenarios, the coffee shop owner might forecast different revenue figures, accounting for the combined effects of economic conditions, competition, and foot traffic.

Now let's compare this to a sensitivity analysis.  

Sensitivity Analysis Example:

Sensitivity analysis, on the other hand, involves altering one input at a time to see how it impacts a specific outcome. Using the same coffee shop example:

A: Impact of Competition:

The owner adjusts only for the presence or absence of a competing coffee shop, keeping all other variables constant, to observe its specific effect on projected revenue.

B: Impact of Local Economy:

By solely varying the economic conditions from thriving to recessionary, while keeping other factors constant, the owner measures how sensitive the revenue is to economic fluctuations.

C: Effect of Foot Traffic

The owner considers only changes in foot traffic, perhaps due to the university's presence or roadworks, to ascertain how it alone could shift the revenue.

For each input change in a sensitivity analysis, the owner gets a clearer picture of which factors have the most significant influence on the coffee shop's revenue.

Sensitivity Analysis vs Scenario Analysis  

In summary, while scenario analysis looks at combinations of multiple inputs to project different futures, sensitivity analysis tweaks individual inputs to measure their specific influence on an outcome. Both offer valuable insights and, when used together, provide a comprehensive understanding of potential risks and rewards. 

Combining Analysis Methods for More Accurate Forecasting

Sensitivity analysis vs. scenario analysis: Should you use them together or independently? You can reap a lot of benefits from using the two together. By doing so, you can gain a more comprehensive view of possible outcomes and better prepare for them. 

For example, a post-secondary school is considering developing a new student learning center on campus. They decide to run a financial forecasting model to determine the impact of their investment. First, they do a scenario analysis to find a base-, best- and worst-case scenario. 

From there, conducting a sensitivity analysis would supply more nuanced information regarding one of these possible scenarios. In this case, the school may be looking at how a potential 5% increase or decrease in revenue would affect their bottom line if they move forward with this investment. Which costs are flexible or inflexible to this change in revenue? Rent is inflexible, but will the ability to control the salaries of the center's staff, the overhead costs, etc. be enough to keep their revenue positive? Conducting a sensitivity analysis can help the school's leaders gain a clearer picture of what the optimal budget would be for a project of this size.

Standalone analysis--either sensitivity or scenario on its own--would not provide the full picture. Ultimately, using both scenario planning methods in conjunction with one another will help you forecast more accurately and diminish the risk potential.

Strategically Position Your Business Through Analysis and Complete Planning

The future is full of uncertainties, but that doesn't mean you can't be prepared--and with Vena's help, unknowns are manageable when backed by predictive modeling.

With our scenario planning and analysis software, you're able to mitigate risk with agile scenario modeling. We harness the full power of live data in a secure, centralized database so that you're able to capture a precise picture of your organization's financial health, enabling you to make business decisions much more confidently. 

Additionally, with Vena you can:

  • Reduce data inaccuracies due to human error
  • Increase efficiency through automatic data loading
  • Generate more reliable predictive analytics
  • Scale your processes while staying within Excel

Ready to get started? Contact us today to request a demo.

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About the Author

Tom Seegmiller, Vice President, FP&A, Vena

As Vice President, FP&A at Vena, Tom Seegmiller is responsible for strategic finance, including business partnering, budgeting and forecasting, with a focus on optimizing enterprise value. Tom is instrumental in the formulation of the financial narrative for the executive leadership team, investors and board members. Tom has always had a focus on driving enhanced business decisions through leveraging financial and operational data. He is an experienced finance executive, having most recently led the finance team at Miovision Technologies. Prior to that, he was in senior FP&A leadership roles at OpenText. Tom enjoys golfing, skiing, exercising and traveling in his spare time, but most importantly, he loves spending time with his wife and daughter.

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