Simulation model
A simulation model represents selected features of a real system and runs those rules under specified inputs or scenarios. It can help a decision-maker explore variability, constraints and consequences without changing the actual system. Monte Carlo simulation repeatedly samples from specified probability distributions to estimate a range of outcomes; other simulations can follow different rules. Results depend on model structure, input assumptions and validation, so they are estimates rather than guarantees. Explain uncertainty, check dependencies and test how conclusions change under alternative assumptions.
Worked example
A retailer simulates demand and supplier delays to compare stock policies, then checks predicted stock-outs against actual records before relying on the model.
Common mistake
Treating simulation output as an objective prediction while ignoring the distributions, relationships and assumptions supplied to the model.
Where it appears in your exams
| Qualification | Exam | Relationship | Period |
|---|---|---|---|
| ACCA | FM | Direct | September 2026–June 2027 |