Definition:
A technique used to determine how different input variables impact a specific outcome in a predictive model.
Key Components:
- Independent Variables: Factors that may influence the outcome.
- Dependent Variables: The key result being measured.
- Scenario Testing: Running multiple cases to observe outcome variations.
Use Cases/Industries:
- Project Estimation: Assessing how cost variations affect total budgets.
- Energy Pricing Models: Evaluating how fuel price fluctuations impact operating costs.
- Engineering & Design: Understanding material selection effects on durability.
Advantages:
- Identifies Critical Cost Drivers: Helps focus on factors with the most impact.
- Improves Strategic Planning: Allows for better risk mitigation.
- Enhances Resource Allocation: Optimizes cost and scheduling decisions.
Challenges:
- Requires Accurate Baseline Data: Poor inputs can lead to misleading conclusions.
- Time-Consuming: Running multiple scenarios can be complex.
- Nonlinear Dependencies: Some models may not account for real-world interdependencies.
Related Terms:
Monte Carlo Simulation, Probabilistic Modeling, Cost Forecasting
Example:
An oil and gas company uses sensitivity analysis to determine how a 10% increase in crude oil prices would impact refinery operating costs.
Visited 1 times, 1 visit(s) today
Synonyms:
What-If Analysis, Scenario Analysis, Input-Output Impact Assessment
