The key components of sensitivity analysis are identifying input variables, assessing how changes in these variables affect the output, and determining the range and impact of uncertainty on the model's results.

Sensitivity analysis involves several key components that collectively assess the impact of changes in a mathematical model. **Decision variables** are the unknowns optimized within the model. The **objective function** is the target for maximization or minimization, while **constraints** impose limitations on these variables. **Shadow prices** indicate the value of relaxing constraints, highlighting which resources are most critical. The **range of optimality** specifies the limits within which coefficients in the objective function can change without altering the optimal solution, and the **range of feasibility** defines allowable changes in constraints' right-hand sides without compromising feasibility. **Scenario analysis** examines various "what-if" conditions to evaluate the effects of input changes, while a **sensitivity report** summarizes these insights, providing valuable guidance for decision-making and resource allocation. Together, these components enable organizations to understand the robustness of their solutions and make informed strategic choices.