Decision Tree Analysis is a diagramming and calculation technique used to evaluate multiple options in the presence of uncertainty. It helps project managers visualize different decision paths, assess risks, and calculate expected outcomes to make informed choices.

Key Aspects of Decision Tree Analysis

  • Models Uncertain Outcomes – Represents different decision paths and possible results.
  • Quantifies Risk & Rewards – Assigns probabilities and costs to each scenario.
  • Uses Expected Value Calculations – Helps identify the most beneficial choice.
  • Supports Strategic Decision-Making – Commonly applied in risk assessment and project planning.

Example Decision Tree

A company must choose between Developing a New Product or Enhancing an Existing Product. Each choice has potential success or failure scenarios, with associated costs and probabilities.

Mermaid Diagram: Decision Tree Example

graph TD;
    A["Start"] -->|New Product: Cost 50K| B["Success: Profit 200K (60%)"]
    A -->|New Product: Cost 50K| C["Failure: Loss 30K (40%)"]
    A -->|Enhance Existing: Cost 20K| D["Success: Profit 100K (70%)"]
    A -->|Enhance Existing: Cost 20K| E["Failure: Loss 10K (30%)"]

Expected Value Calculation

For the New Product decision:

For the Enhance Existing decision:

Since Developing a New Product has a higher expected value ($108K vs. $67K), it may be the preferred option.

Why Decision Tree Analysis Matters

  • Improves Risk-Based Decision-Making – Helps evaluate uncertain outcomes.
  • Provides a Structured Approach – Visualizes different decision paths.
  • Quantifies Costs & Benefits – Assigns values to risks and opportunities.
  • Enhances Project Planning & Risk Management – Supports complex project decisions.

See also: Risk Analysis, Monte Carlo Simulation, Cost-Benefit Analysis (CBA), Probability and Impact Matrix.