Trying to decide when to use sensitivity analysis vs scenario analysis?
Both techniques are valuable for analyzing the potential effects of uncertainty in financial models. But when does sensitivity analysis make sense, and when is scenario analysis the better choice?
Find out in this article, where we explore the main differences between the two, and when you should use one over the other.
- What is sensitivity analysis?
- How sensitivity analysis works
- What is a scenario analysis?
- How scenario analysis works
- Differences between both
- When to use sensitivity analysis vs scenario analysis
What is sensitivity analysis?
Sensitivity analysis is a powerful tool used to understand how changes in one specific variable can impact a given outcome.
Imagine you're trying to predict the revenue for next year. But there's uncertainty. That's where sensitivity analysis steps in. It allows you to change one variable at a time and observe how these changes can shift the result.
Think of it like a financial magnifying glass focusing on one aspect at a time.
Benefits of sensitivity analysis
Some benefits of sensitivity analysis include:
1. Enhanced business communication
Sensitivity analysis breaks complex model outputs into understandable insights. This means you can clearly show key stakeholders how your strategic plans can affect the broader business and budget.
2. Verifies model integrity
A financial model should mirror your business's reality. Sensitivity analysis helps cross-check this reflection. You can use it to confirm if the key drivers you based your model on genuinely have the most impact on business outcomes. This gives your models extra reassurance that they're as accurate and effective as possible.
3. Identifying risks and improving scenario analysis
By revealing how a small adjustment in one variable can impact the outcome, you get a head start on managing those risks. Not only that, but sensitivity analysis helps you focus your scenario analysis on what matters. This targeted approach saves you from creating models around different scenarios that might not be relevant to your decision-making process.
How sensitivity analysis works
Sensitivity analysis works by systematically changing one input at a time in a financial model. This helps to measure how each individual change could affect the outcome.
But what does this look like in practice?
Example 1: Portfolio investment management
Suppose you're managing a portfolio of investments, and you want to understand how changes in interest rates could affect the portfolio's value.
Using sensitivity analysis, you'd hold all other variables constant - such as the market condition, inflation rates, and specific asset performance - and adjust the interest rates to see how changes in this single variable affect the portfolio's value.
You might find that a 1% increase in interest rates leads to a 5% decrease in the portfolio value, making the portfolio highly sensitive to interest rate changes. This insight could be critical for future investment decisions, as it helps identify where potential risks and opportunities lie.
Example 2: Forecasting revenue
Let's say you're forecasting next year's revenue for a manufacturing company. Your forecast might depend on several variables, including the number of units sold, price per unit, production cost per unit, and overhead costs.
Sensitivity analysis allows you to change one variable at a time (say, the number of units sold) while keeping others constant. Doing so helps you understand how changes in that one variable impact the forecasted revenue.
What is scenario analysis?
Scenario analysis assesses multiple inputs simultaneously to model different realistic situations. With this approach, you can stress test for plausible scenarios as well as unlikely, but high-impact cases. Developing a range of possible scenarios facilitates contingency planning.
Scenario analysis is particularly useful for strategic planning, risk management, and capital budgeting. It helps visualize different paths a business could take under varying circumstances, helping it to prepare for multiple possible outcomes.
Benefits of scenario analysis
1. Improves strategic thinking
Scenario analysis creates a multifaceted perspective and allows decision-makers to anticipate both promising opportunities and potential pitfalls linked to certain policies and strategies.
2. Promotes effective resource allocation
Scenario analysis is rooted in forecasting future events and trends. This forward-thinking approach helps business leaders understand external conditions that could impact their operations. Armed with these insights, they can distribute resources more strategically, mitigating risks and harnessing opportunities more effectively.
3. Strengthens risk management
By presenting multiple possible futures, scenario analysis allows for better risk assessment and management. It provides a safety net for businesses, helping them be better prepared for unforeseen circumstances and volatile market conditions.
How scenario analysis works
Scenario analysis is a technique that assesses the combined impact of changes in all input variables simultaneously. To do this, an analyst conceives various potential events that could realistically occur down the line.
It typically involves crafting three distinctive narratives:
1. Base-case scenario: This scenario represents business-as-usual or the expected situation.
2. Worst-case scenario: The most challenging outcome that could transpire if things don't go according to plan.
3. Best-case scenario: The best possible and most desired projected outcome.
Now, let’s dive into some examples of how finance teams might employ scenario analysis:
Example 1: Revenue forecasting
Picture yourself as a CFO trying to predict the revenue for the next fiscal year. A lot depends on your forecast - budget allocations, strategic decisions, investment planning. To make an informed forecast, you need to consider several variables: unit price, unit cost, and sales volume, among others.
In a scenario analysis approach, you'd create different scenarios, altering multiple variables at once. For example:
1. The base-case scenario - You might use the current unit price, cost, and expected sales volume based on the company's performance and market trends.
2. The best-case scenario - You would use the highest feasible unit price (without significantly impacting sales volume), the lowest realistic unit cost (perhaps through supplier negotiations or process efficiencies), and a sales volume that takes into account potential market growth or successful marketing campaigns.
3. The worst-case scenario - You could consider a situation with a lower unit price (due to increased competition), a higher unit cost (due to rising raw material prices), and a reduced sales volume (perhaps because of an economic downturn or increased competition).
This analysis can give you a range of possible revenues for the next fiscal year, which can guide your strategic planning and decision-making.
Example 2: Investment
For this example, let's assume you're the head of a finance team, and your company is considering a significant investment like opening a new factory. This decision depends on a host of variables - projected sales, operational costs, and market growth, to name a few.
In scenario analysis, you'd simultaneously alter these variables across different scenarios:
1. The base-case scenario - You might base this on conservative estimates of market trends and projected sales, alongside the current operational cost structure.
2. The best-case scenario - This could assume strong market growth leading to higher than average sales, along with successful cost optimization efforts reducing operational costs.
3. The worst-case scenario - This could consider a challenging market scenario leading to lower sales, coupled with increased operational costs due to unexpected price hikes in raw materials or labor costs.
This scenario analysis allows you to view the possible outcomes for the investment holistically, providing a risk-reward assessment that could steer the final investment decision.
What is the difference between scenario analysis and sensitivity analysis?
The key difference when discussing scenario analysis vs sensitivity analysis is 'scope.' Both methods examine the potential effects of changes in the input variables on the outcomes, but the extent to which they do this differs.
Sensitivity analysis evaluates the impact of changing one variable at a time while holding all others constant. It's a great way to understand how sensitive the outcome is to changes in a single parameter.
On the other hand, scenario analysis is more holistic. It studies the combined effect of changing multiple variables at once, providing a bigger-picture view of possible outcomes under different circumstances. This helps finance teams prepare for various possible futures, offering a comprehensive perspective on risk and reward.
When to use sensitivity analysis vs scenario analysis
Both sensitivity and scenario analysis should be staples in a finance team's toolbox, especially in today's unpredictable economic climate.
But when should you use sensitivity analysis vs scenario analysis?
Below, we share examples of when to use each one:
When to use sensitivity analysis:
- Impact assessment: Sensitivity analysis is useful when you want to determine how a single variable's alteration affects the overall model output. For instance, in a cost-benefit analysis, you could modify the discount rate to evaluate its effect on the net present value.
- Significant variables: It helps identify which factors have the most substantial impact on the model results, such as identifying the most critical factor in a profit margin analysis.
- Data precision: If you're unsure about which variables need more precise data or estimates, sensitivity analysis can assist by highlighting the most influential factors.
- Model robustness: Sensitivity analysis allows you to test model robustness by observing how output variations correlate with different input values. For example, it's useful in investment appraisals to observe the effects of changes in projected cash flows.
- Model simplification: If you're looking to simplify your models, sensitivity analysis can assist by identifying variables with minimal effect on the outputs, such as insignificant cost factors in a production cost analysis.
When to use scenario analysis:
- Outcome evaluation: Use scenario analysis when you need to evaluate the model outcomes under various plausible scenarios, like anticipating revenue growth under different market conditions.
- Stress testing: Scenario analysis lets you to stress test strategies under best-case, worst-case, and moderate scenarios, such as different market share targets for a new product.
- Combinations of factors: It helps assess model performance by considering different combinations of multiple factors, like varying rates of inflation and customer demand in a sales forecast.
- Contingency plans: Scenario analysis is useful when preparing contingency plans for unlikely but high-impact scenarios, such as sudden supplier disruptions in supply chain management.
- Result swings: It can help determine which combinations of variables produce the most significant swings in results, like evaluating potential profit fluctuations due to price and cost changes.
- Market conditions: Use scenario analysis to test model suitability under different possible market conditions, like forecasting corporate earnings in varying economic climates.
- Strategy viability: It's useful for checking the viability of strategies under a variety of realistic situations, such as evaluating a company's financial resilience under different recession scenarios.
Blending sensitivity vs scenario analysis for more accurate forecasting
By blending scenario vs sensitivity analysis, you can construct a robust analytical framework. This approach helps to capture both the complexity of multiple variable changes and the depth of each variable's impact. It supports a more informed, comprehensive view of potential risks and opportunities, reinforcing the decision-making process.
For example, sensitivity analysis may reveal two critical variables. You could then define scenarios based on different value combinations for those sensitive inputs. This provides targeted scenario planning while also incorporating other factors.
Whether you're predicting future revenues, evaluating investment opportunities, or determining cost efficiencies, these twin analytical techniques will provide you with invaluable insights to make confident, data-driven decisions.
FAQs: Scenario analysis vs sensitivity analysis
What advantage does scenario analysis have over sensitivity analysis?
Scenario analysis allows evaluating the combined effect of multiple factors changing simultaneously. Sensitivity analysis only looks at one variable at a time.
What are the two types of sensitivity analysis?
One-at-a-time sensitivity analysis varies one input while holding others constant. Probabilistic sensitivity analysis assigns probability distributions to inputs and evaluates using Monte Carlo simulation.
What is the use of sensitivity and scenario analysis?
They help validate models, identify key variables, stress test strategies, and prepare contingency plans by understanding potential outcomes.
Can I use both techniques together?
Yes! Using both sensitivity and scenario analysis provides comprehensive model validation. Sensitivity analysis identifies key value drivers, while scenario analysis tests performance under various conditions.