The core idea
Scenario modelling explores what follows from alternative assumptions. It differs from predicting one most likely future. A workforce scenario might vary demand, attrition, recruitment lead time or time to competence. Long-term futures work can also explore qualitatively different environments. Our short-term numerical example is an operational sensitivity exercise, not a reproduction of the government toolkit’s full foresight process.
Source and attribution [1]Using it in practice
Choose uncertainties that could materially change the decision. Build a coherent baseline and alternatives, keeping units and time periods consistent. Identify actions that are useful across several scenarios and trigger points for revisiting commitments. Avoid calling one option optimistic merely because it produces a convenient staffing budget. State where assumptions interact rather than changing each input independently without thought.
An example, not a reported case
Worked example · illustrative
An invented operation needs 3,200 coverage hours per week. At 32 usable hours per FTE it needs 100 FTE; at 30 usable hours it needs about 106.7 FTE. If demand also rises to 3,520 hours, the 30-hour scenario requires about 117.3 FTE. The calculation reveals sensitivity to both demand and usable capacity. It does not establish the correct allowance or staffing standard for a real operation.
What to watch for
Scenarios can create false confidence when every case shares an unrealistic assumption. Do not attach probabilities without a basis. A numerical range may omit structural changes such as a new service model. Keep operational, financial and legal constraints visible when comparing actions.
Name the uncertain drivers
For each driver, state why it could change and what evidence would signal movement. Separate controllable choices from external conditions. Include at least one scenario that challenges the preferred plan rather than only small variations around it.
Choose a response
Compare options by lead time, reversibility and performance across scenarios. Agree when to recruit, develop capability or redesign work. Review the assumptions when new evidence arrives instead of defending a forecast because it was approved earlier.
Take it into your next conversation
Three useful questions.
- What question can this method answer, and what can it not establish?
- Are the comparison, observation period and assumptions defensible?
- What decision follows, and how will its consequences be reviewed?
Related terms
Go to the evidence
Sources & attribution
[1] Government Office for Science: Futures Toolkit ↗
The core idea is an original summary of the cited work. Application notes, examples and sketches are our interpretations, not quotations or reproductions of the authors’ figures. Publisher records may require access to read the full original work.
Published 2026-09-20 · Reviewed 2026-09-20. Editorial approach