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Sensitivity Analysis
Definition
Sensitivity Analysis is an analytical technique used to evaluate how changes in one or more assumptions influence the outcome of a model, forecast, investment, or strategic decision. Rather than treating projections as fixed estimates, Sensitivity Analysis examines how different values for key variables affect expected results.
Organizations commonly evaluate assumptions relating to pricing, customer demand, operating costs, exchange rates, market growth, customer retention, production capacity, or implementation timelines. By adjusting one variable while holding others constant, analysts can determine which assumptions have the greatest influence on business outcomes and identify where uncertainty creates the highest level of risk.
Sensitivity Analysis is particularly valuable when decisions depend upon assumptions that cannot be verified with complete confidence. It provides decision-makers with a clearer understanding of the range of possible outcomes rather than a single projected result.
Why It Matters
Business forecasts are only as reliable as the assumptions on which they are built. Sensitivity Analysis helps organizations understand which assumptions deserve the greatest attention, improve risk awareness, strengthen investment decisions, and prepare contingency plans for situations where critical variables change unexpectedly.