Move from intuition to shared sizing, uncertainty, and delivery forecast.
A forecast with size classes, assumptions, uncertainty, and review rhythm.
You can decide which delivery probability is communicated and which uncertainties must be actively reduced.
Regularly update the forecast with real cycle times and keep assumptions transparent.
How can the team quickly order many comparable items by relative size and discuss only genuine uncertainty?
Affinity Estimation quickly groups many items relatively and makes outliers visible.
Groups are then converted into communicable size classes.
Which rough relative size class describes each comparable item using shared anchors without false precision or hidden point conversion?
T-Shirt Sizing makes size scales understandable for team and stakeholders without false precision.
For critical items, optimistic, realistic, and pessimistic perspectives are then added.
What range and triangular expected value follow from a plausible optimistic, most likely, and pessimistic scenario?
Three Point Estimation makes uncertainty explicit and prevents a single false precision.
Estimate ranges can then feed into probabilistic forecasting.
What probability distribution for completion duration or delivered count emerges when actual historical throughput values are repeatedly resampled?
Monte Carlo Forecasting translates historical data and uncertainty into probabilities for delivery time or scope.
The forecast is communicated as a decision basis and updated regularly.
Artifacts stay collapsed until you actually need them.
Set subject, data, roles, flow, and review.
# Affinity Estimation: Worksheet
## Question
To be confirmed
## Desired outcome
To be confirmed
## Scope
Practice run
Time: 30–60 minutes for 20–50 comparable items; analyse disputed items afterwards.
Complete this worksheet on paper or in your own document during the work.
## Preparation for this scope
### Method setup
- **Materials:** Comparable refined items, shared Definition of Done, relative scale, reference items, and discussion pile
- **Roles:** Delivery team sorts · product owner clarifies · facilitator protects silent flow
- **Advance information:** Set item set, included work, and shared Definition of Done; remove unclear or unlike items first.
- **Overall time needed:** 30–60 minutes for 20–50 comparable items; analyse disputed items afterwards.
- **Setup:** Prepare a workspace with Frame · Anchors · Sort · Conflicts · Calibrate; separate example from live data.
### Prepare for the work steps
#### Frame: Comparison set
- Set item set, included work, and shared Definition of Done; remove unclear or unlike items first.
- Comparable refined items, shared Definition of Done, relative scale, reference items, and discussion pile
#### Anchors: Size anchors
- Comparison set
#### Sort: First sort
- Size anchors
#### Conflicts: Clarification pile
- First sort
#### Calibrate: Relative estimate
- Clarification pile
## Affinity Estimation · working structure
| Item | Included | Reference | First placement | Conflict | Outcome | Follow-up |
| --- | --- | --- | --- | --- | --- | --- |
| Delete token | | | | | | |
| Save card | | | | | | |
| Provider migration | | | | | | |
- **Delete token:** Add evidence and rationale.
- **Save card:** Add evidence and rationale.
- **Provider migration:** Add evidence and rationale.
The blank worksheet contains prompts only.
[Reference · original teaching example](https://www.mountaingoatsoftware.com/agile/story-points/affinity-estimation)
## Frame: Comparison set
Expected artifact: Comparison set
Set comparable items and one shared meaning of size.
Entry:
...
- [ ] Do all items include the same work types and Done standard?
## Anchors: Size anchors
Expected artifact: Size anchors
Place a known small, medium, and large item as relative orientation.
Entry:
...
- [ ] Are anchors understood and from the same work domain?
## Sort: First sort
Expected artifact: First sort
Team members silently place items relative to anchors; an item may be moved once.
Entry:
...
- [ ] Was placement independent before discussion or numeric anchoring?
## Conflicts: Clarification pile
Expected artifact: Clarification pile
On a second move request, put the item in discussion; resolve differences in scope and risk.
Entry:
...
- [ ] Does discussion expose different work models rather than preferences?
## Calibrate: Relative estimate
Expected artifact: Relative estimate
Name groups, inspect boundaries, and split or refer large and unclear items.
Entry:
...
- [ ] Are neighbourhoods plausible and open items assigned a next step?
## Open questions and next steps
...
Method guide: https://methodatlas.meierhoff-systems.de/en/methods/affinity-estimation/run-sheet
Set subject, data, roles, flow, and review.
# Monte Carlo Forecasting: Worksheet
## Question
To be confirmed
## Desired outcome
To be confirmed
## Scope
Practice run
Time: 60–120 minutes for data check, simulation, and decision review; rerun regularly.
Complete this worksheet on paper or in your own document during the work.
## Preparation for this scope
### Method setup
- **Materials:** Remaining count or target date, consistent historical throughput sample, system/WIP context, many-run simulation, and quantile report
- **Roles:** Service owner · flow/data expert · delivery team · decision makers
- **Advance information:** Choose one question: when will N items finish or how many by date D; check service boundary, Done, data window, and comparability.
- **Overall time needed:** 60–120 minutes for data check, simulation, and decision review; rerun regularly.
- **Setup:** Prepare a workspace with Question · Sample · Simulate · Quantiles · Check · Decide; separate example from live data.
### Prepare for the work steps
#### Question: Forecast question
- Choose one question: when will N items finish or how many by date D; check service boundary, Done, data window, and comparability.
- Remaining count or target date, consistent historical throughput sample, system/WIP context, many-run simulation, and quantile report
#### Sample: Throughput data
- Forecast question
#### Simulate: Simulation runs
- Throughput data
#### Quantiles: Probabilities
- Simulation runs
#### Check: Robustness view
- Probabilities
#### Decide: Forecast decision
- Robustness view
## Monte Carlo Forecasting · working structure
| Forecast part | Input/logic | Fictional value | Check | Outcome | Decision | Reforecast |
| --- | --- | --- | --- | --- | --- | --- |
| Question | | | | | | |
| Sample | | | | | | |
| Simulation | | | | | | |
| Quantiles | | | | | | |
- **Question:** Add evidence and rationale.
- **Sample:** Add evidence and rationale.
- **Simulation:** Add evidence and rationale.
- **Quantiles:** Add evidence and rationale.
The blank worksheet contains prompts only.
[Reference · original teaching example](https://www.prokanban.org/forecasting-pocket-guide)
## Question: Forecast question
Expected artifact: Forecast question
Set forecast type, remaining count/time window, start, and required risk level.
Entry:
...
- [ ] Is exactly one quantity or date question expressed in count units?
## Sample: Throughput data
Expected artifact: Throughput data
Check historical throughput from the same system and cadence; retain zeros and normal variation.
Entry:
...
- [ ] Are period, team, WIP policy, and Done comparable enough?
## Simulate: Simulation runs
Expected artifact: Simulation runs
Each run samples weekly values with replacement, accumulates until remaining count is reached, and stores duration.
Entry:
...
- [ ] Does each draw correspond to one observed throughput period?
## Quantiles: Probabilities
Expected artifact: Probabilities
Sort durations and report several quantiles as risk levels.
Entry:
...
- [ ] Are P50/P85/P95 explained as by-with-likelihood rather than guarantees?
## Check: Robustness view
Expected artifact: Robustness view
Test sensitivity to data window, scope, and WIP change; backtest prior forecasts where possible.
Entry:
...
- [ ] Does the decision survive plausible data windows?
## Decide: Forecast decision
Expected artifact: Forecast decision
Choose accepted risk, record assumptions, and reforecast after scope/system change.
Entry:
...
- [ ] Is a concrete action tied to the quantile?
## Open questions and next steps
...
Method guide: https://methodatlas.meierhoff-systems.de/en/methods/monte-carlo-forecasting/run-sheet