Sensitivity Analysis

Summary

Sensitivity analysis, or what-if analysis, helps businesses assess how changes in assumptions affect forecasts. By testing variations in key assumptions—such as selling price, costs, and sales volume—companies can identify which factors most influence outcomes like profit. This technique aids in refining forecasts and guiding decision-making. Through a practical example, the video shows how a 10% change in different assumptions can drastically alter forecast profits. While useful for focusing on critical variables, sensitivity analysis has limitations, such as testing one assumption at a time and depending on the accuracy of the initial data.

Takeaways

• 😀 Sensitivity analysis is a technique used to evaluate how changes in assumptions affect business forecasts, such as cash flow, profit, and investment appraisals.
• 😀 It helps identify which assumptions in a forecast are most sensitive and crucial for accurate predictions.
• 😀 Sensitivity analysis is also known as 'what-if analysis', which tests different scenarios by altering one assumption at a time.
• 😀 An example of sensitivity analysis involves a profit forecast where assumptions about selling price, variable cost, fixed cost, and sales are tested.
• 😀 A 10% decrease in the selling price has the most significant impact on the forecasted profit, reducing it by 50%.
• 😀 A reduction in forecasted sales (10% fewer units) results in a 35% drop in profit, showing its moderate impact compared to price changes.
• 😀 Sensitivity analysis helps businesses understand the risks and uncertainties in their forecasts and make more informed decisions.
• 😀 It highlights the importance of focusing on the most sensitive assumptions to improve forecast accuracy and reliability.
• 😀 A key benefit of sensitivity analysis is that it challenges overly optimistic forecasts, which helps businesses prepare for potential setbacks.
• 😀 One drawback of sensitivity analysis is that it only tests one assumption at a time, which may not fully represent real-world complexities where multiple variables change simultaneously.
• 😀 The quality of sensitivity analysis depends on the initial assumptions made in the forecast. Poor assumptions lead to inaccurate sensitivity analysis results.

Q & A

What is sensitivity analysis?

Sensitivity analysis is a technique used to assess how changes in assumptions affect business forecasts, such as profit, cash flow, and investment outcomes. It helps identify which assumptions are most critical to the forecast's reliability.

How is sensitivity analysis similar to testing the sensitivity of teeth?

Just as you would test how sensitive your teeth are to temperature changes, sensitivity analysis tests how sensitive business forecasts are to changes in assumptions, like sales price, costs, and sales volume.

Why are assumptions important in business forecasting?

Assumptions are crucial in forecasting because they serve as the foundation for predicting future outcomes, such as revenue and profit. If these assumptions are incorrect or unrealistic, the forecasts can be misleading and lead to poor decision-making.

What are some common types of business forecasts where sensitivity analysis is used?

Common business forecasts that benefit from sensitivity analysis include cash flow forecasts, investment appraisals, and break-even analysis. These forecasts rely heavily on assumptions about future revenue, costs, and sales volume.

In the given example, what was the initial forecasted profit?

In the example provided, the initial forecasted profit was £200,000, based on assumptions of a selling price of £100 per unit, variable cost of £30 per unit, fixed costs of £500,000, and 10,000 units sold.

How does changing the assumptions by 10% affect the forecast profit?

Changing the assumptions by 10% worsens the forecasted profit. For example, lowering the selling price by 10% reduces the forecasted profit to £100,000, a 50% decrease, while reducing sales volume by 10% results in a profit of £130,000, a 35% decrease.

Which assumption was found to have the most significant impact on the forecast?

The selling price per unit was found to have the most significant impact on the forecast. A 10% decrease in the selling price led to a 50% reduction in forecast profit.

What is the primary benefit of conducting sensitivity analysis in business?

The primary benefit of sensitivity analysis is that it helps businesses identify and focus on the most important assumptions, allowing for more informed decision-making and reducing the risk of overly optimistic or unrealistic forecasts.

What is one drawback of sensitivity analysis?

One drawback of sensitivity analysis is that it typically tests one assumption at a time, which may not fully capture the complexity of real-world situations where multiple assumptions change simultaneously.

How can sensitivity analysis help businesses avoid overly optimistic forecasts?

Sensitivity analysis challenges assumptions by testing their reliability and impact on the forecast. This process helps identify potential risks and ensures that businesses are not relying on overly optimistic or unrealistic projections.