Forecast delivery with an explicit confidence range
Use comparable completion history to communicate uncertainty about delivery.
How this works
Use comparable completion history to communicate uncertainty about delivery.
Forecast a comparable work item from an explicit historical distribution, making the sample and uncertainty visible. Distinguish a fresh item’s total cycle time from the remaining time of work already in progress, and do not present empirical percentiles as guarantees.
Before you start
Defined operating scope
Delivery teams have comparable historical work records and need to communicate scheduling uncertainty.
Required source material
Obtain Start and finish dates; Comparable work types. If connectors are unavailable, use authorized files with the same fields.
Decision and exception rules
The target item is already partly complete. Use a justified conditional method or explicitly limit the forecast to fresh work; do not subtract age blindly.
Prepare the inputs
- completed_history
- Required
Start and finish dates with work type
Exclude invalid timestamps and document exclusions.
- target_work
- Required
Work type and start or current state
Confirm comparability with the historical sample.
- time_convention
- Required
Calendar or working days and timezone
Use the same convention across history and forecast.
- forecast_method
- Required
Quantiles and sample rule
State the calculation method and data limits.
Implementation, step by step
Establish the working record
- Confirm the selected records, effective versions and responsible owner.
- Check completed_history, target_work, time_convention, forecast_method before beginning. Record the source version and the selected scope; an unavailable value stays unknown rather than becoming an inferred fact.
Checkpoint: The input record identifies the selected completed_history and any missing required fields.
Validate the historical sample
- Validate completed records and select comparable work, checking process changes and incomplete-history bias.
- Recompute durations and quantiles from the retained sample and stated method.
Checkpoint: A saved intermediate result for: Validate the historical sample.
Calculate completion-time distributions
- Calculate the completion-time distribution with a documented deterministic method and report sample size.
- Back-test forecast ranges on later comparable work and report observed coverage.
Checkpoint: A saved intermediate result for: Calculate completion-time distributions.
Publish a date range with its probability
- Translate selected quantiles into a date range for the defined start condition, explaining historical coverage and conditions that could invalidate the forecast.
- Recompute durations and quantiles from the retained sample and stated method.
Checkpoint: A saved intermediate result for: Publish a date range with its probability.
Reconcile the proposed result
- Recompute durations and quantiles from the retained sample and stated method.
- Back-test forecast ranges on later comparable work and report observed coverage.
Checkpoint: The listed discrepancies have a named reviewer or an explicit unresolved status.
Package the result and unresolved work
- Check changing process conditions; historical percentiles are not guarantees.
- Historical percentiles do not guarantee future dates or establish an SLA.
Checkpoint: A probabilistic delivery forecast. Unresolved items remain visible in the review record.
Decisions along the way
The target item is already partly complete.
Use a justified conditional method or explicitly limit the forecast to fresh work; do not subtract age blindly.
The process changed materially after the historical sample.
Restrict or replace the sample and label comparability uncertain.
The sample is too sparse for the requested confidence claim.
Show the observations and uncertainty instead of inventing a precise probability.
What you will produce
probabilistic-delivery-forecast-result.json
JSONA probabilistic delivery forecast. Label proposed and completed states separately.
Include: sample_definition · sample_size · duration_distribution · quantile_method · forecast_range · assumptions · coverage_check · status · evidence_reference · review_note
probabilistic-delivery-forecast-review.md
MarkdownKeep the evidence and unresolved decisions needed to apply this result, including exceptions involving forecast_method.
Include: source_reference · question · owner · next_action
Check the result
- Recompute durations and quantiles from the retained sample and stated method.
- Back-test forecast ranges on later comparable work and report observed coverage.
- Inspect completed_history and target_work in both the original and output; confirm that record identity and interpretation survived the transformation.
- Check changing process conditions; historical percentiles are not guarantees.
When something goes wrong
The target item is already partly complete.
Use a justified conditional method or explicitly limit the forecast to fresh work; do not subtract age blindly. Retain the affected evidence and recovery status.
The process changed materially after the historical sample.
Restrict or replace the sample and label comparability uncertain. Retain the affected evidence and recovery status.
The sample is too sparse for the requested confidence claim.
Show the observations and uncertainty instead of inventing a precise probability. Retain the affected evidence and recovery status.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Forecast delivery with an explicit confidence range for the following bounded example; prepare the result for review.
Inputs
Synthetic example: Ten comparable completed items took 2, 3, 3, 4, 4, 5, 5, 6, 8 and 10 calendar days. Use nearest-rank empirical quantiles for a fresh item.
Expected result
Synthetic expected result: Median is 4 days; the empirical 80th percentile is 6 days and 90th is 8. Report these as historical sample estimates, not a guaranteed 80% future completion promise.
Run a small pilot
Start small
Trial a representative batch containing the ordinary case and the exception demonstrated above.
Record the baseline
Record current preparation time, review effort and correction types for the same records.
Agree success criteria
Agree acceptable review and error limits; measure observed coverage of forecast ranges; sample-quality issues and forecast preparation time.
Stop or revise
The sample is too sparse for the requested confidence claim. Pause affected records and apply this recovery: Show the observations and uncertainty instead of inventing a precise probability.
Keep the scope clear
- Historical percentiles do not guarantee future dates or establish an SLA.
- Do not hide sample exclusions, process changes or uncertainty about work comparability.
Check before using
Check changing process conditions; historical percentiles are not guarantees.
Business activities
- Deliver recurring client work
- Review business performance
FROM A RECIPE TO YOUR WORKFLOW
Bring the company context together.
This workflow needs reliable inputs before it needs a better prompt. Moat8 Vault brings working information into a readable archive for the AI tools you use.
For this recipe: Start and finish dates · Comparable work types
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow