What if the central assumption is wrong?

What if the central assumption is wrong?

A plan is often most vulnerable not to a missing decimal, but to one shared belief: demand will recover, financing will remain available, a supplier will deliver, regulation will permit the model, or customers will accept the change. Scenario analysis should expose what happens when that organising assumption fails.

Start by naming the assumption in measurable terms, including horizon and range. Build a small set of coherent alternatives around the mechanisms that could invalidate it, not arbitrary percentage shocks. Trace first-order effects and then feedback: lower volume may worsen unit economics, constrain investment, weaken service and reduce demand again.

Use sensitivity analysis to locate thresholds and reverse stress testing to work backward from an unacceptable outcome. The Bank of England�s 2025 guidance emphasises both tools: sensitivity analysis reveals how key assumptions shape results, while reverse tests identify the boundary at which risk becomes material or the business model fails.

For each scenario, specify leading evidence, decision points and feasible responses. A mitigation that requires capital, supplier capacity or regulatory approval after the shock is not yet a response plan. Test whether several teams are relying on the same scarce resource and whether management has enough lead time to act.

Do not select one scenario as a disguised new forecast. Compare strategies across the range and favour actions that preserve options, reduce irreversible exposure or remain valuable in several futures. The purpose is not to predict the surprise; it is to prevent one unexamined assumption from carrying more risk than leaders consciously intended.