HKSM

Key Points

• Varies one input at a time to see its effect on a chosen outcome (e.g., cost, schedule, NPV).
• Helps prioritize critical assumptions and risks that drive the most change.
• Often visualized with a tornado diagram to rank variables by impact.
• Supports quantitative risk analysis, estimating, and scenario planning.
• Relies on credible ranges for inputs (e.g., optimistic, most likely, pessimistic).
• Complements but does not replace probabilistic methods like Monte Carlo.

Purpose of Analysis

To identify which uncertain inputs most influence project outcomes so that management attention, contingency, and risk responses are focused where they yield the greatest benefit.

Method Steps

• Select the outcome metric to test (e.g., total cost, finish date, benefit value).
• List candidate input variables and define realistic ranges or discrete scenarios for each.
• Hold all inputs constant at their baseline and vary one input across its range.
• Record the outcome change at each bound for that input; repeat for all inputs.
• Rank inputs by magnitude of outcome change; create a tornado diagram if useful.
• Translate findings into actions: refine estimates, add reserves, or plan risk responses.

Inputs Needed

• Baseline model or estimate linking inputs to the chosen outcome.
• List of uncertain inputs and their plausible ranges or scenarios.
• Assumptions and constraints that define fixed versus variable factors.
• Historical data, expert judgment, or market intelligence to set input ranges.
• Risk register entries that describe key drivers and potential responses.
• Tooling (e.g., spreadsheet, scheduling or cost model) to run calculations.

Outputs Produced

• Ranked list of inputs by their impact on the outcome.
• Impact ranges showing how the outcome changes at input bounds.
• Tornado diagram or similar visualization (optional but common).
• Recommendations for risk responses, contingency, and further analysis.
• Updated assumptions and documentation of input ranges tested.

Interpretation Tips

• Focus on high-impact drivers first; they offer the best leverage for control.
• Check that input ranges are realistic; exaggerated ranges distort priorities.
• Remember it is a one-factor-at-a-time view; interactions among variables are not shown.
• Use results to refine estimates and trigger targeted risk responses.
• Pair with scenario or probabilistic analysis when dependencies are important.

Example

A project cost model depends on labor rate, productivity, and material price. The team varies each input between optimistic and pessimistic bounds while holding the others at baseline. The tornado diagram shows productivity has the largest impact on total cost, followed by material price, then labor rate. The team decides to pilot process improvements (to stabilize productivity) and negotiates price locks with suppliers.

Pitfalls

• Using arbitrary ranges without data or expert validation.
• Ignoring correlations between inputs that move together in reality.
• Overlooking non-linear effects when the model only assumes linearity.
• Assuming results are precise; they are only as good as the model and inputs.
• Failing to convert insights into concrete risk responses and monitoring.

PMP Example Question

A project manager wants to identify which uncertain cost driver most affects the total project budget. Which technique should they use to vary one input at a time and compare the change in total cost?

1. Expected monetary value analysis.
2. Sensitivity analysis.
3. Cause-and-effect diagram.
4. Delphi technique.

Correct Answer: B — Sensitivity analysis

Explanation: Sensitivity analysis varies one input at a time to see its effect on the outcome and rank drivers by impact. The other options serve different purposes.