Sensitivity Analysis
- Sensitivity analysis is a technique used to determine how variations in estimated values of a parameter affect a measure of worth.
- It helps answer the question, “What if?”. So, it is also known as what-if analysis.
- also called a simulation analysis because it tests hypothetical scenarios to quantify uncertainty
- determines how sensitive a decision is to changes in parameter values.
- Typically, one parameter is varied at a time, assuming independence from other parameters (assumption)
- A probable range and increment of variation are selected for each parameter being analyzed.
- The results are often displayed graphically by plotting the parameter versus the measure of worth. A steeper slope on the graph indicates higher sensitivity.
Applications:
- Predict stock prices by analyzing variables like company earnings, shares outstanding, and debt-to-equity ratios.
- Determine how changes in interest rates affect bond prices.
- Understand how external factors influence a project or undertaking.
- Reduce uncertainty by identifying areas to plan for or be alert to.
- Identify errors in original baseline assumptions.
- Simplify models by removing insignificant factors.
- Communicate results to decision-makers.
- Help meet strategic benchmarks by understanding how conditions may affect the ability to meet targets.
- Evaluate how changes to input variables affect the profitability of a project.
Example: A company can use sensitivity analysis to determine how changes in customer traffic impact total sales. For example, a 10% increase in customer traffic might lead to a 5% increase in sales. A business that sells Christmas decorations might use sensitivity analysis to predict how much revenue will increase based on an increase in customer traffic.
- Advantages: Sensitivity analysis can help management focus on inputs to achieve specific results, communicate areas to focus on or risks to control, identify mistakes in the original benchmark, and reduce uncertainty. It tests models across various possibilities and allows flexibility in boundaries when testing variables.
Types of Sensitivity Analysis:
- Single Parameter Variation: Examining the effect of varying a single parameter on a project or when selecting between mutually exclusive alternatives.
- Multiple Parameters: Analyzing the impact of varying multiple parameters on a single project.
- Multiple Alternatives: Assessing the sensitivity of selection from multiple mutually exclusive alternatives to variations in one or more parameters.
Note:
- Breakeven Analysis is also a form of sensitivity analysis where the accept/reject decision changes depending on where the parameter’s most likely estimate lies.
- Three Estimates: Sensitivity analysis can use pessimistic, most likely, and optimistic estimates for each parameter to determine how these variations affect alternative selection.
Limitations:
- One-at-a-Time Analysis:
Fixing all parameters except the one being varied can be a limitation when several parameters significantly contribute to sensitivity. - Independence Assumption:
The assumption that parameters are independent of each other is not always valid. - Not Probabilistic:
Sensitivity analysis uses deterministic values, where each value is estimated with certainty. It does not incorporate probabilities associated with different values of the parameters, and so it differs from risk analysis. - Relies heavily on assumptions that might not be true in the future.
- Complex models may be demanding on computer systems and can become too complicated.
Bonus: Sensitivity Analysis using Spreadsheet:
