methodatlas
Delivery

Monte Carlo Forecasting

Moves scope, sequence, and delivery flow toward a concrete result through "formulate the forecast question", "choose historical flow data", and "update the forecast regularly".

Core question
With what probability will the desired scope be finished by the target date (or how much scope can we complete by then), and which assumptions drive the distribution?
HighWorkshop + async30-90 min Setup, danach laufend
Open runsheetPlan sessionStart comparison
Purpose

Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication.

How it works

The team follows the steps "formulate the forecast question", "choose historical flow data", "run simulations", "interpret percentiles", and "update the forecast regularly". Each step is made visible. At the end, Forecast Percentiles, Throughput Dataset, and Risk Communication are available so decisions, tests, or actions can continue directly.

Visual orientation

Method sketch for a quick mental model.

Monte Carlo Forecasting · probabilistische LieferungHistorische Flow-Daten simulieren und Liefertermine oder Scope als Wahrscheinlichkeitsverteilung mit Perzentilen kommunizieren
Monte Carlo ForecastingDas Visual zeigt Forecast-Frage, historische Flow-Daten, Simulationen und Perzentile als probabilistische Lieferprognose.Lieferprognosen als Wahrscheinlichkeitsverteilung kommunizierenHistorische Durchsatz- oder Cycle-Time-Daten werden wiederholt gezogen, um viele plausible Fertigstellungstermine zu simulieren.Forecast-FrageWann ist Scope X mit welcher Sicherheitfertig?Historische Flow-DatenDurchsatz oder Cycle Time als Stichprobeviele plausible Zukunftsverläufe50% Wahrscheinlichkeitbis 12. Jun70% Wahrscheinlichkeitbis 18. Jun85% Wahrscheinlichkeitbis 26. JunSimulationen aus Daten ziehennicht ein Datum, sondern Wahrscheinlichkeitenregelmäßig mit neuen Daten aktualisieren

Flow

  1. 1Formulate the forecast question
  2. 2Choose historical flow data
  3. 3Run simulations
  4. 4Interpret percentiles
  5. 5Update the forecast regularly

The runsheet guides execution with 5 phases, timeboxes, 5 pitfalls, and clear stop criteria.

Open runsheet

Ideal for

  • Release forecasting
  • Kanban flow
  • Delivery date communication

Not good for

  • Teams without history
  • Very heterogeneous items
  • One-off large projects without comparison data

Deep dive

In detail

Monte Carlo Forecasting uses historical delivery data to simulate many plausible future paths. Instead of estimating a single duration, throughput or cycle time is drawn randomly from observed data and aggregated across many simulations. The result is a probability distribution, such as a 50, 70, or 85 percent statement. That lets teams communicate risk explicitly and refresh forecasts as new data arrives.

Facilitation

Clarify system boundaries, data window, and forecast question up front. Communicate probabilities instead of guarantees and show assumptions transparently. Refresh forecasts regularly and use deviations to improve flow policies and slicing.

Output artifacts
Forecast PercentilesThroughput DatasetRisk Communication
Tags
forecastingflowdelivery

Sources

Monte Carlo Simulation | IBMibm.comMonte Carlo Analysis | Atlassianatlassian.com
Artifact templates
Monte Carlo Forecasting Working TemplateCompact working template for Monte Carlo Forecasting with context, input, output artifacts, and next step.
markdown

monte-carlo-forecasting-working-template.md

Compact working template for Monte Carlo Forecasting with context, input, output artifacts, and next step.

Monte Carlo Forecasting Working Template

Goal

Predicts delivery time or scope probabilistically using historical throughput or cycle time data.

Context

When and for what do we use this method?

Input

Which data, observations, decisions, or materials are available?

Execution

Short notes along the runsheet.

Output artifacts

  • Forecast Percentiles:
  • Throughput Dataset:
  • Risk Communication:

Assumptions and open questions

  • ...

Decision / Next step

Owner, date, and success signal.

When to choose differently

Short decision aid for existing alternatives.

PERT Estimation

Statt Monte Carlo Forecasting, wenn ihr Erwartungswerte aus Spannbreiten ableiten wollt und Unsicherheit explizit machen möchtet.

Story Points

Statt Monte Carlo Forecasting, wenn ihr zuerst relative Komplexität im Team verstehen wollt und noch keine Flussdaten habt.

Similar methods

All methods
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Value Stream Mapping

Turns scope, sequence, and delivery flow into a tangible result through selecting a value stream, mapping current state, and designing a future state.

Agile
NoEstimates

Moves backlog, sprint work, and team flow toward a concrete result through "clarify the planning question", "slice work smaller", and "calibrate regularly with reality".

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Cost of Delay

Moves scope, order, and delivery flow toward a concrete result through "adopt the initiative list", "clarify value types for each initiative, such as revenue, efficiency, or risk", and "derive a sorted sequence".

Delivery
Communication Plan

Turns scope, sequencing, and delivery flow into a tangible result by listing stakeholders and audiences, clarifying information needs, and planning review and adaptation.

Delivery
Shape Up

Structures product work around shaped pitches, bets, fixed cycles, hill charts, and scope hammering.

Decision Making
PERT Estimation

Moves options, criteria, and risks toward a concrete result through "collect three values", "explain the formula", and "document the planning assumption".

Statt Monte Carlo Forecasting, wenn ihr Erwartungswerte aus Spannbreiten ableiten wollt und Unsicherheit explizit machen möchtet.