When a single forecast is not enough
A spreadsheet forecast often uses one assumed value for a driver such as demand, duration, cost or growth. Monte Carlo analysis represents selected drivers as distributions, runs repeated trials and records the resulting variation in your output cells. This helps you ask not only “What is the forecast?” but “How wide is the range of plausible outcomes under these assumptions?”
- Estimate the range of project costs or completion dates.
- Explore how uncertain price, volume or costs affect a financial model.
- Compare downside risk across alternative plans.
- Assess how a forecast responds to multiple uncertain drivers.
Set up a simulation you can interpret
Start with a deterministic spreadsheet model whose formulas produce the outcomes you want to inspect. Then choose the uncertain inputs, assign distributions and parameters that are defensible for your data, select result cells, and run a modest number of iterations first. Review the output report before increasing the run size.
- Document why each input distribution and parameter is appropriate.
- Use a repeatable seed when you need to compare runs consistently.
- Check that the model recalculates correctly at low, typical and high input values.
- Interpret the resulting distribution as conditional on the assumptions—not as a guarantee or prediction.
How to interpret the output
Simulation output summarizes the outcomes generated by the selected model and inputs. A wider distribution signals more modeled uncertainty; percentiles help compare threshold outcomes where available in the report. Look at the assumptions and model behavior alongside any summary statistic, and avoid relying on a single average when tail risk matters.
Keep workbook simulations responsive
Workbook-mode Monte Carlo recalculates formulas repeatedly, so runtime depends on the selected iteration count and the complexity of spreadsheet formulas. Begin with a smaller sample, bound the model to the relevant cells and avoid unnecessary volatile or external calculations. Sheet Analyst includes safe limits and restores simulation inputs when the run completes or stops.
Where the model and data stay
The simulation operates on your spreadsheet model. Sheet Analyst does not send cell contents, formulas, sheet names, spreadsheet IDs or calculation results to Better Work Apps or Polar. See the privacy policy for details about the separate license-status requests.
Common questions
Does Monte Carlo simulation predict the future?
No. It shows outcomes implied by your model and probability assumptions. It does not guarantee future results, and its usefulness depends on the quality of the model and inputs.
Why can a workbook simulation take time?
Each trial may require spreadsheet formulas to recalculate. Larger models, volatile formulas and high iteration counts can increase runtime.
Can I reproduce a simulation run?
Sheet Analyst supports a repeatable seed so you can rerun a simulation with the same pseudo-random sequence while comparing model changes.