Run sensitivity analysis

Run Sensitivity Analysis

Purpose

Help teams identify which assumptions in a forecast or business case drive the most variance in the outcome — so attention, validation, and risk mitigation can be focused on what matters most.

Skill type

Conceptual skill with calculation-aware components

Use this skill when

• A forecast or business case has been built and the team wants to understand its risk profile
• Multiple assumptions exist and the team doesn't know which are most critical
• Leadership asks "what's the downside if X doesn't happen?"
• A plan needs to be stress-tested before presenting to stakeholders

Do not use this skill when

• No forecast or model exists yet (use run-forecasting-scenarios first)
• The goal is full scenario planning (use run-forecasting-scenarios)

Required inputs

• Forecast or business case with explicit assumptions
• Outcome metric to evaluate (revenue, growth, ROI, payback)

Optional inputs

• Historical variance data for key assumptions
• External benchmarks for assumption ranges
• Decision threshold (what outcome would change the decision?)

Upstream context

Works best when:

• A forecast or business case exists
• Key assumptions are documented

Downstream handoff

Output can feed:

• build-business-case (sensitivity findings → risk section)
• run-forecasting-scenarios (key variables → scenario inputs)
• plan-risk-mitigation (high-sensitivity assumptions → risks)

Instructions

1. List all key assumptions in the forecast or plan.
2. For each assumption, define a realistic range: pessimistic / base / optimistic.
3. Vary each assumption one at a time while holding others constant.
4. Measure the impact on the outcome metric for each change.
5. Rank assumptions by impact: which cause the biggest outcome variance?
6. Identify the assumption combination that produces the worst realistic case.
7. Recommend which assumptions to validate most urgently.

Output

Provide:

• Assumption inventory with ranges
• Impact on outcome metric per assumption (% change)
• Sensitivity ranking (highest to lowest impact)
• Worst realistic case analysis
• Decision threshold check (does the plan still make sense in the worst case?)
• Validation priority recommendations

Risks / caveats

• Vary one assumption at a time to isolate effects — combined changes mask individual sensitivities
• Ranges must be realistic — using extreme ranges produces useless results
• High sensitivity to one assumption means that assumption must be validated before investing