I once read the following definition for sensitivity analysis:

"We have already been introduced to sensitivity analysis via the geometry of a simple example. We saw that the values of the decision variables and those of the slack and surplus variables remain unchanged even though some coefficients in the objective function are varied. We also saw that varying the righthandside value for a particular constraint alters the optimal value of the objective function in a way that allows us to impute a per-unit value, or shadow price, to that constraint. These shadow prices and the shadow prices on the implicit nonnegativity constraints, called reduced costs, remain unchanged even though some of the righthand-side values are varied. Since there is always some uncertainty in the data, it is useful to know over what range and under what conditions the components of a particular solution remain unchanged. Further, the sensitivity of a solution to changes in the data gives us insight into possible technological improvements in the process being modeled. For instance, it might be that the available resources are not balanced properly and the primary issue is not to resolve the most effective allocation of these resources, but to investigate what additional resources should be acquired to eliminate possible bottlenecks. Sensitivity analysis provides an invaluable tool for addressing such issues."

Would you agree or disagree with this? Or add anything that is missing?