Assumptions and Sensitivity Analysis Notes

Analyzing Assumptions and Performing Sensitivity Analysis

Analyzing assumptions is crucial when preparing future results based on a set of assumptions.
Situations include assessing product line expansion profitability or predicting cash flow for the next five years.
Evaluating assumptions is important for:
- Gaining greater confidence in the results of an analysis.
- Assessing the risk of significant variance between actual and estimated results.

Identify assumptions that significantly affect analysis results.
Consider which key assumptions are subject to fluctuation and should be investigated further.
Information reliability depends on its source:
- Third-party data and stable historical data are more reliable.
- Unsupported personal opinions or rapidly changing economic circumstances are less reliable.
Key assumptions that can fluctuate often pertain to:
- Market uncertainties (demand, price).
- Product mix variation.
- Input cost fluctuation, especially for commodities.
Example: Industry data for projected sales is more reliable than a sales manager's projection. Projections for existing products are more reliable than those for new products.

Essential part of assessing the situation.
Questions to identify relevant information:
- What information can verify the assumption (e.g., industry data for sales projections, supplier estimates for part costs)?
- If the assumption depends on uncertain future events, what is the range of possible future values (e.g., historical fluctuations in raw material costs, percentage error in sales estimates)?
- Were multiple independent estimates obtained, revealing a range of values?
In case scenarios:
- Review information for clues about the expected analysis.
- Determine if a sensitivity analysis is appropriate.
- State assumptions and qualify that results may be impacted by these assumptions

Apply analysis methods once relevant information is identified and key assumptions are determined.

Test the reasonableness of key assumptions using corroborating information.
The goal is to test if key assumptions are reasonable, similar to evidence in an audit.

Assess the risk that actual results will vary significantly from estimates by varying key variables.
Sensitivity analysis helps understand how outcomes change as assumptions in a projection change.
Start with expected results and vary key assumptions to accommodate different outcomes.
Analyze “worst-case” and “best-case” scenarios by recalculating results with the most negative and positive values for assumptions.
Hint: In case scenarios, center sensitivity analysis on key inputs like the price of oil, if the case focuses on its variability.
Hint: The range is often provided. For example, sales are expected to increase by 2% to 5% over three years. Initial projections are based on the most likely figure, and sensitivity analysis examines the high and low points.

Sensitivity analyses can be performed for individual assumptions or multiple assumptions simultaneously.
Perform worst-case and best-case scenario analysis by varying all assumptions in the same way.
Worst-case: Use the lowest value for sales, the highest value for costs, and so on.
Best-case: Use the highest value for sales and the lowest value for costs.
Hint: Management may specify the purpose of the sensitivity analysis, such as knowing the worst- and best-case scenarios.
Alternatively, management might want to understand the potential impact of variances in isolation, focusing on each assumption independently.
Effective sensitivity analysis recognizes how the provided range affects assumptions and communicates these effects to management.

Interpret findings and explain what was learned.
Highlight the significant impacts of potential variances in key assumptions for decision-makers.
Example: A sensitivity analysis might indicate that the company would be in a loss position at the low range of the sales projection.
Include the results of assumption evaluations in the summary of strengths and weaknesses for each viable alternative.
An alternative highly sensitive to changes in a key assumption might be viewed as a risky option.

State a conclusion about the evaluation of assumptions and advise management on next steps.
Example: A significant risk to the profitability of a new product is the cost of a key input. Recommend securing a long-term contract to stabilize results and reduce risk.
Provide conclusions/recommendations about the overall analysis of alternatives and potential impact of key assumptions.
Base overall conclusion on the analysis and the most likely outcome while acknowledging assumptions and possible outcomes.
Communicate the results obtained by varying the assumptions and the potential impact these variances have on the organization so decision-makers can make informed decisions.