Sensitivity Analysis examines how optimal solutions and objective values change when problem parameters vary. In linear programming, it determines ranges for: (1) objective function coefficients maintaining optimal basis; (2) right-hand-side values (resource availability) maintaining feasibility. Shadow prices (dual variables) indicate marginal value of resources. Reduced costs show opportunity cost of non-basic variables. For nonlinear programs, sensitivity uses KKT multipliers and parametric programming. Applications: determining critical parameters, conducting what-if analysis, evaluating robustness, and guiding data collection efforts. Sensitivity analysis provides managerial insights beyond optimal solutions, informing decisions about resource allocation, pricing, and risk management.