Assumptions and Sensitivity Analysis Notes

Analyzing Assumptions and Performing Sensitivity Analysis

15.1 Assess the situation

• 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.

15.1.1 Identify key assumptions for evaluation

• 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.

15.1.2 Identify relevant information

• 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

15.2 Analyze major issues

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

15.2.1 Verify/corroborate individual assumptions

• 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.

15.2.2 Perform sensitivity analysis

• 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.

15.2.3 Scenario analysis of multiple assumptions

• 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.

15.2.4 Discuss implications

• 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.

15.3 Conclude and advise

• 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.