In LP models, the parameters are usually not exact. With sensitivity analysis, we
can ascertain the impact of this uncertainty on the quality of the optimum solution. For
example, for an estimated unit profit of a product, if sensitivity analysis reveals that the
optimum remains the same for a ±1O% change in the unit profit, we can conclude that
the solution is more robust than in the case where the indifference range is only ±1 %.