Advantages and Disadvantages of Sensitive Analysis

Meaning of Sensitivity Analysis

Sensitivity analysis is a management tool that helps in determining how different values of an independent variable can affect a particular dependent variable. It can be useful in wide range of subjects apart from finance, such as engineering, geography, biology, etc.

An example of sensitivity analysis can be something like – “What would happen to the price of a bond (dependent variable) if inflation (independent variable) increases by 0.5%?” As it tries to answer many what-if questions, it is also called as “what-if analysis”

There are many advantages and disadvantages to sensitivity analysis as follows:

Advantages of Sensitivity Analysis

Following are the advantages of this analysis:

In-depth Analysis

When sensitivity analysis is done, each independent & dependent variable is studied in-depth. Their movements are studied and how independent variable affects dependent variable is also studied. In the pursuit of understanding the relationship between the variables, the cause and effect reaction between the two establishes. Such in-depth analysis will bring more accurate future forecast.

Strengthen “weak spots”

As sensitivity analysis studies each variable independently, it can identify critical variables that may act as a weakness. For example – In this analysis, we find out that the bond prices are extremely volatile to changes in inflation, we can take measures to reduce the impact, say by hedging. Thus we can say – weak spot is identified & strengthened.

Decision Making

Sensitivity analysis results in data backed forecast. When all the variables are considered and all the outcomes are analyzed, it becomes easy for the management to make decisions about investments within the business & decisions about investing in the markets. Thus it is an extremely helpful tool for future planning.

Quality Check

Through sensitivity analysis, the management can know which variables have a high impact on success or failure of a project. For example, in a sensitivity analysis of Company A, the management discovered that packaging of their product affects the sales by up to 20%. So management can concentrate to provide best quality packaging in order to optimize sales.

Proper Allocation of Resources

As discussed, sensitivity analysis can identify strong & weak areas, & measure their impact on final objective. This helps the management in directing resources to variables that most require these resources. Following the above example of Company A, if management knows that packaging has a major effect on sales, it will direct more resources to come up with optimum packaging for its product.

Advantages and Disadvantages of Sensitivity Analysis

Disadvantages of Sensitivity Analysis

Following are the disadvantages of this analysis:

Based on Assumptions

Sensitivity analysis is based on historical data & management assumptions. If these assumptions itself are wrong, the whole analysis will be wrong and the future forecast will not be accurate. For example, if management assumes that raw material prices will increase in future and will affect the final price of the product, the company will purchase additional raw material at present prices. But if instead of rising, raw material prices fall, then the company will be at a disadvantage in the market.

Not Relative in Nature

Sensitivity analysis considers each variable individually and tries to determine the outcome. In the real world, all variables are related to each other. For example, both inflation as well as market interest rates, affect bond prices. Sensitivity analysis will consider how much a change in inflation will affect bond price and how a change in market interest rate will affect bond price, but it won’t consider how a change in inflation will affect market interest rate or vice versa. This is actually incomplete analysis. Thus, we can say this analysis gives depth to the forecast but doesn’t consider its breadth.

Finally, we can conclude that even though sensitivity analysis is a very good analytic tool, it would be better to use it with other management tools to get optimum forecasts.

Reference –

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Last updated on : June 1st, 2018

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