Monte Carlo simulation

What Monte Carlo simulation means

Monte Carlo simulation is a concept used in software delivery planning to describe a condition, relationship, estimate, or decision about engineering work. The concept becomes actionable when it has a trigger, a response, and a review point. The term is clearer when the team defines what enters the measure or decision, what counts as completion, and which planning period is in scope. That discipline prevents a familiar phrase from becoming a different metric in every meeting.

A concrete example

For example, a team can review three recent decisions, identify where the concept helped, and remove it from the workflow if it adds no useful signal. The record should include the starting condition, the intended result, and the tradeoff that makes the choice possible. When the outcome differs from the plan, the team can inspect the assumption instead of assigning a simplistic explanation to the people doing the work.

Limits and interpretation

If a plan changes materially, keep the old assumption visible and explain why the new interpretation is different. Use this concept as an input to a decision or learning loop, not as a detached score. Keep the relevant work population visible, and revisit the interpretation when the system changes. A useful planning artifact explains what someone should do next and what evidence would change that action.

How Weave fits

Weave can help teams inspect delivery evidence related to monte carlo simulation, including completed work, waiting, review, and rework patterns when those records are connected. That context can inform a planning discussion, but Weave does not make the decision, infer missing organizational context, or guarantee a date, capacity level, or outcome.