Improving the usefulness of business budgets and forecasts

Sensitivity analysis is the process of recalculating outcomes under alternative assumptions to determine the impact of variables.

The advantages of using sensitivity analysis include:

  • Testing the robustness of the results of a model or system in the presence of uncertainty.
  • Identifying cost drivers which are needed to make more informed decisions.
  • Increased understanding of the relationships between input and output variables in a system or model.
  • Uncertainty reduction – Identifying model inputs that cause significant uncertainty in the output.
  • Model simplification – Fixing model inputs that have no effect on the output.
  • Making recommendations more credible, understandable, compelling or persuasive.
  • To guide future data collections.
  • To optimise the allocation of resources.

In any business budgeting process there are always variables that are uncertain. Sales growth, volume growth, selling prices, interest rates, inflation rates, employee numbers, operating expenses and other variables may not be known with great precision. Sensitivity analysis answers the question, ‘if these deviate from expectations, what will the effect be on the business profits, balance sheet, cash-flows, and viability? Which variables are causing the largest deviations’?

A main problem with sensitivity analysis is that the variables are often interdependent (correlated), which makes examining each variable individually unrealistic, e.g. changing one factor such as selling price will most likely affect other factors such as the volume sold.

Sensitivity analysis is not perfect, but overall does improve the credibility and usefulness of business budgets and forecasts.


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