Sensitivity Analysis

Sensitivity Analysis in Project Risk Management

Sensitivity Analysis: A Comprehensive Guide

What is Sensitivity Analysis?

Sensitivity analysis is a quantitative risk analysis technique used in project management to determine which risks have the most potential impact on a project. It examines how changes in one variable affect the overall project outcome while keeping all other variables constant.

The technique helps project managers identify which risk factors are most significant, allowing them to focus their attention on managing those particular risks effectively. It is often represented visually using a tornado diagram, which displays the relative impact of each variable.

Why is Sensitivity Analysis Important?

Sensitivity analysis is crucial in project risk management for several reasons:

1. Prioritizes Risks: Helps identify which risks deserve the most attention based on their potential impact.

2. Improves Decision-Making: Provides data-driven insights for making informed decisions about risk responses.

3. Increases Project Stability: By focusing on high-impact variables, teams can develop more robust risk management strategies.

4. Enhances Resource Allocation: Allows for efficient use of limited resources by targeting the most critical risks.

5. Supports Stakeholder Communication: Provides clear visual representation of risk impacts that stakeholders can easily understand.

How Sensitivity Analysis Works

The process typically follows these steps:

1. Identify Key Variables: Determine which project variables will be analyzed (e.g., cost elements, schedule durations, resource availability).

2. Define Base Case: Establish the baseline projection using current estimates for all variables.

3. Vary One Variable at a Time: Adjust one variable (e.g., increasing or decreasing by a certain percentage) while keeping all others constant.

4. Calculate Impact: Measure how each variable's change affects the outcome (typically project duration, cost, or NPV).

5. Rank Variables: Order the variables based on their degree of impact on the outcome.

6. Create Tornado Diagram: Develop a visual representation showing the relative impact of each variable.

Example of Sensitivity Analysis

Consider a construction project with the following variables:
- Labor costs
- Material costs
- Equipment rental
- Subcontractor costs
- Duration

A sensitivity analysis might reveal that a 10% increase in material costs would increase the total project cost by 5%, while a 10% increase in labor costs would increase the total project cost by only 2%. This indicates that material costs are more sensitive and should receive more risk management attention.

Tornado Diagram

A tornado diagram visualizes sensitivity analysis results by showing how much each variable affects the outcome. The variables are arranged in descending order of impact, creating a tornado-like shape. The horizontal bars represent the range of possible outcomes when each variable is changed.

Limitations of Sensitivity Analysis

While valuable, sensitivity analysis has some limitations:

1. It considers variables in isolation, not accounting for correlations between variables.

2. It may not capture complex, non-linear relationships between variables.

3. It may not account for probability distributions of variables (this is where Monte Carlo analysis comes in).

Exam Tips: Answering Questions on Sensitivity Analysis

1. Understand the Core Concept: Remember that sensitivity analysis measures how changes in one variable affect the outcome while other variables remain constant.

2. Know the Visual Representation: Be familiar with tornado diagrams and how to interpret them. The variables with the longest bars have the greatest impact.

3. Differentiate from Other Techniques: Be clear on how sensitivity analysis differs from other risk analysis techniques like Monte Carlo simulation (which considers probability distributions) and decision tree analysis.

4. Focus on the Purpose: Emphasize that the main purpose is to identify which variables have the greatest impact on project objectives.

5. Application Questions: For scenario-based questions, look for clues about which variable is being tested and how it affects the outcome.

6. Calculations: Practice simple sensitivity calculations to understand how varying a parameter by a certain percentage affects the outcome.

7. PMI Terminology: Use proper PMI terminology such as "what-if analysis" (another name for sensitivity analysis) and "tornado diagram."
8. Limitations Recognition: Acknowledge the limitations in your answers, showing you understand that sensitivity analysis examines variables independently.

9. Integration with Risk Response: Connect sensitivity analysis results to appropriate risk response strategies (avoid, transfer, mitigate, accept).

10. Process Placement: Remember that sensitivity analysis is performed during the Perform Quantitative Risk Analysis process, which is part of the Risk Management Knowledge Area.

By mastering sensitivity analysis concepts and practicing their application, you'll be well-prepared to answer related questions on the PMI-RMP exam or any project management assessment.