methodatlas
Playbook

Improve Estimation and Forecasting

Move from intuition to shared sizing, uncertainty, and delivery forecast.

Outcome

A forecast with size classes, assumptions, uncertainty, and review rhythm.

At the end you have

Size GroupsT-Shirt SizesEstimate RangeForecast

Decision point

You can decide which delivery probability is communicated and which uncertainties must be actively reduced.

Next step

Regularly update the forecast with real cycle times and keep assumptions transparent.

Ideal for

  • Product teams with delivery planning
  • Backlogs with similar work
  • Stakeholder questions about probability

Not good for

  • one-off research work
  • work without historical cycle times
Preparation

What should be clear before you start

Roles

  • Delivery lead or scrum master
  • Development team
  • Stakeholders with planning needs

Inputs

  • items cut to comparable size
  • historical cycle times where available
  • planning question and time horizon

Setup

  • Check item comparability
  • Name uncertainty openly
  • Frame forecast as probability
Flow

Method path

0 methods
    Completion criteria
    Templates

    Artifacts for this playbook

    Artifacts stay collapsed until you actually need them.

    CanvasShow template

    Affinity Estimation Working Template

    Compact working template for Affinity Estimation with context, input, output artifacts, and next step.

    # Affinity Estimation Canvas

## Context

What is this method used for?

## Core question

Which question should be answered at the end?

## Input

Which data, observations, or materials are available?

## Working area

- Area 1:
- Area 2:
- Area 3:
- Relationships / patterns:

## Output artifacts
- Affinity Size Map:
- Grouped Estimates:
- Unclear Items:

## Open questions

- ...

## Next step

Owner, date, success signal.
    MarkdownShow template

    Monte Carlo Forecasting Working Template

    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.