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Plan my session

Plan a concrete work block with agenda, roles, preparation, and a copyable result artifact.

Method session30-90 min setup, then 15-30 min per forecastWorkshop or asyncForecast Percentiles

Session: Monte Carlo Forecasting

The plan translates the method into a concrete facilitated work block. Your inputs flow directly into the session brief and work artifact.

Derived automatically

Method session with 1-8. The plan uses the existing method logic and the runsheet.

Runsheet
Participation logic
Team round, shared work and alignment

Use the session for shared understanding. Contributions are collected visibly, assumptions are aligned, and open differences remain traceable in the artifact.

Outcome logic
Finish artifact

The session works directly toward Forecast Percentiles. After the session, the artifact should be shareable, reviewable, or reusable.

  1. 1

    Phase 1: Refine forecast question

    10-15 min

    Choose one of two standard questions: "When are N items done?" or "How many by date X?" Fix scope and target date. Set confidence threshold (typically 70% or 85%). Hint: Unclear questions create unclear answers. Treat multiple concurrent questions separately. No probability statement without threshold.

    FacilitatorForecast Percentiles
  2. 2

    Phase 2: Check and clean data set

    15-20 min

    Pull throughput data from last 6-12 weeks. Flag or exclude anomalies (holidays, outages). Check representativeness: does data match the upcoming period? Hint: "Garbage in, garbage out." Data quality is forecast quality. When team structure changes, collect new data; ignore old data.

    FacilitatorThroughput Dataset
  3. 3

    Phase 3: Run simulation

    5-10 min

    Start the tool with 1000+ simulations. For "when done," randomly draw weekly throughput per simulation and accumulate until scope is reached. For "how much," draw each week until target date. Hint: At least 1000 simulations, otherwise percentiles are too coarse. In tools with built-in options, pay attention to distribution type (resampling from historical data is best practice).

    FacilitatorRisk Communication
  4. 4

    Phase 4: Evaluate and communicate percentiles

    15-20 min

    Calculate percentiles: 50%, 70%, 85%, 95%. Phrase communication as a probability statement: "85% chance done by date X." List assumptions explicitly. Hint: A point estimate without probability is incorrect. "We deliver by 15.06." is wrong; "85% probability by 15.06." is correct.

    FacilitatorForecast Percentiles
  5. 5

    Phase 5: Update weekly

    10 min per week

    Add one new data point each week and recalculate forecast. Document trend: does the 85% value move forward or backward. Hint: A stagnant or moving-back 85% value is an early warning sign. Investigate scope growth, throughput decline, or item delay as causes.

    OwnerThroughput Dataset
  6. 6

    Publish artifact

    10 min

    Check the artifact for completeness, define location, set version or status, and name review recipients.

    OwnerForecast Percentiles
Usable artifact

Session Brief

For invitations, boards, tickets, PR descriptions, or workshop notes.

session-brief.md

Session Brief: Monte Carlo Forecasting

Goal

Artifact: Forecast Percentiles

Working 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?

Context

Concrete forecast question ("When done?" or "How much by date X?"); historical throughput data; remaining scope (number of items); known anomalies in data window (holidays, outages, hires/aborts).

Setup

  • Format: Method session
  • Duration: 30-90 min setup, then 15-30 min per forecast
  • Mode: Workshop or async
  • Participants: One analyst (Tech Lead, Flow Master, or coach) who extracts data and runs simulation; one Product Owner as forecast requester; team for assumption plausibility checks; optional sponsor.
  • Owner: One analyst (Tech Lead, Flow Master, or coach) who extracts data and runs simulation
  • Participation mode: Team round, shared work and alignment
  • Outcome logic: Finish artifact

Participation Logic

Use the session for shared understanding. Contributions are collected visibly, assumptions are aligned, and open differences remain traceable in the artifact.

Outcome Logic

The session works directly toward Forecast Percentiles. After the session, the artifact should be shareable, reviewable, or reusable.

Input

Throughput or cycle-time dataset as CSV or sheet; Monte-Carlo tool (Actionable Agile, Throughput Forecaster, Python script, Excel with random functions); forecast questionnaire; stakeholder-briefing documentation.

Preparation

Fix the data window (rule of thumb: at least 11 points, typically 12 weeks). Mark anomalies. Configure tool for 1000+ simulations. Phrase forecast question precisely (scope, target date, probability).

Agenda

  1. Phase 1: Refine forecast question (10-15 min) Owner: Facilitator Action: Choose one of two standard questions: "When are N items done?" or "How many by date X?" Fix scope and target date. Set confidence threshold (typically 70% or 85%). Hint: Unclear questions create unclear answers. Treat multiple concurrent questions separately. No probability statement without threshold. Output: Forecast Percentiles

  2. Phase 2: Check and clean data set (15-20 min) Owner: Facilitator Action: Pull throughput data from last 6-12 weeks. Flag or exclude anomalies (holidays, outages). Check representativeness: does data match the upcoming period? Hint: "Garbage in, garbage out." Data quality is forecast quality. When team structure changes, collect new data; ignore old data. Output: Throughput Dataset

  3. Phase 3: Run simulation (5-10 min) Owner: Facilitator Action: Start the tool with 1000+ simulations. For "when done," randomly draw weekly throughput per simulation and accumulate until scope is reached. For "how much," draw each week until target date. Hint: At least 1000 simulations, otherwise percentiles are too coarse. In tools with built-in options, pay attention to distribution type (resampling from historical data is best practice). Output: Risk Communication

  4. Phase 4: Evaluate and communicate percentiles (15-20 min) Owner: Facilitator Action: Calculate percentiles: 50%, 70%, 85%, 95%. Phrase communication as a probability statement: "85% chance done by date X." List assumptions explicitly. Hint: A point estimate without probability is incorrect. "We deliver by 15.06." is wrong; "85% probability by 15.06." is correct. Output: Forecast Percentiles

  5. Phase 5: Update weekly (10 min per week) Owner: Owner Action: Add one new data point each week and recalculate forecast. Document trend: does the 85% value move forward or backward. Hint: A stagnant or moving-back 85% value is an early warning sign. Investigate scope growth, throughput decline, or item delay as causes. Output: Throughput Dataset

  6. Publish artifact (10 min) Owner: Owner Action: Check the artifact for completeness, define location, set version or status, and name review recipients. Output: Forecast Percentiles

Closeout

  • Update result artifact: Forecast Percentiles
  • Define location, version, and review recipients.
  • Define owner, next step, and review date.
Usable artifact

Work artifact

Pre-filled starting point based on the matching template.

work-artifact.md

Forecast Percentiles: Monte Carlo Forecasting

Working 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?

Context

Concrete forecast question ("When done?" or "How much by date X?"); historical throughput data; remaining scope (number of items); known anomalies in data window (holidays, outages, hires/aborts).

Participants

  • Owner: One analyst (Tech Lead, Flow Master, or coach) who extracts data and runs simulation
  • Participants: One analyst (Tech Lead, Flow Master, or coach) who extracts data and runs simulation; one Product Owner as forecast requester; team for assumption plausibility checks; optional sponsor.

Input

Throughput or cycle-time dataset as CSV or sheet; Monte-Carlo tool (Actionable Agile, Throughput Forecaster, Python script, Excel with random functions); forecast questionnaire; stakeholder-briefing documentation.

Template

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.

Completion Check

  • Forecast Percentiles is complete enough for review:
  • Location:
  • Version / status:
  • Review by:
  • Next step:

Next Step

  • Review result
  • Mark open questions
  • Schedule review or decision
Template base

Monte Carlo Forecasting Working Template

View 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.

Ready to use when
  • Working question, owner, and target artifact are visible.
  • The result fits Forecast Percentiles.
  • Archive one snapshot per forecast date. Keep trend table with date and 50%/70%/85%/95% values and scope status. Change forecast question => create a new forecast ID; do not overwrite old.
  • Open questions are noted as follow-ups.
  • The next review or decision point is scheduled.
ProfileRunsheet