When an initiative spans teams
Multi-team Monte Carlo
Stitch together three teams' independent 85%-confidence forecasts and you don't get an 85% chance the initiative lands. You get closer to 61%. Multi-Team Monte Carlo models the whole initiative as one system instead of hoping separate forecasts add up.
One simulation for the whole initiative, not several stitched together
Running a separate forecast per team and combining the confidence levels understates the real risk: the true likelihood of every team landing together is the product of each team's confidence, not the level any one of them chose. Three teams each "85% confident" isn't an 85% confident initiative, it's closer to 61%.
An initiative that depends on more than one team
Reach for this when a program or initiative needs a single, defensible completion forecast, especially where teams depend on each other's output or compete for shared resources.
When the work sits with one team
If the work belongs to a single team, use single-team Monte Carlo, or Epic/Feature Monte Carlo if several Features are in flight. Multi-Team Monte Carlo earns its complexity once teams and dependencies enter the picture.
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See how the maths actually multiplies
Beyond Burnups walks through a real multi-team example, step by step, including how to handle dependencies between teams.