Multiple Feature Cut Line Forecaster

Monte Carlo simulation for multiple features in priority order. Determines which features can be completed by a target date and forecasts completion dates at a chosen confidence level.

4. Story split rate
5. Throughput

The current inputs are stored in the URL for sharing.

6. Features / Epics (in priority order)
#Feature NameLow StoriesHigh StoriesComplexity
1
2
3
4
5
7. Monthly Throughput Adjustments (optional)

Multiply throughput by a factor each month (e.g., 0.5 for December holidays).

URL Parameters (advanced)

Append query parameters to prefill the form for testing or shared scenarios. Complex values such as feature rows and monthly adjustments are stored in compact encoded strings instead of raw JSON.

ParameterMeaning
startDate, targetDateForecast window in YYYY-MM-DD format.
targetLikelihoodConfidence level as a decimal, such as 0.85 for 85%.
splitLow, splitHighStory split-rate range applied before simulation.
durationIdxThroughput unit index: 0=week, 1=2 weeks, 2=3 weeks, 3=4 weeks.
throughputModeUse estimate or data.
tpLow, tpHighEstimate-mode throughput inputs.
samplesTextHistorical throughput samples, separated by commas or new lines.
focusIdxFocus index: 0=100%, 1=75%, 2=50%, 3=25%.
monthDeltasCompact month:value pairs for non-default months, such as 7:0.8,8:0.8,12:0.7.
featuresTextCompact feature rows in the form name~storyLow~storyHigh~complexityIdx, separated by |.
numTrialsSimulation trials, typically 100-10000.

Estimate mode with a prioritized five-feature backlog

Prefills an estimate-based scenario with five feature rows and seasonal throughput adjustments.

Open example

/multi-feature?startDate=2026-04-07&targetDate=2026-07-14&targetLikelihood=0.85&splitLow=1&splitHigh=1.7&durationIdx=0&throughputMode=estimate&tpLow=4&tpHigh=7&focusIdx=1&monthDeltas=7%3A0.8%2C8%3A0.8%2C12%3A0.7&featuresText=Authentication%7E5%7E9%7E0%7CBilling%7E8%7E13%7E1%7CReporting%7E13%7E21%7E1%7CAudit%2520Trail%7E8%7E18%7E2%7CAdmin%2520Console%7E21%7E34%7E2&numTrials=800

Historical-data mode with three higher-uncertainty features

Uses throughput samples and feature rows encoded directly in the URL for a shared scenario.

Open example

/multi-feature?startDate=2026-05-01&targetDate=2026-08-28&targetLikelihood=0.7&splitLow=1.1&splitHigh=2&durationIdx=1&throughputMode=data&samplesText=3%2C4%2C5%2C6%2C4%2C7%2C5%2C6&focusIdx=2&monthDeltas=&featuresText=API%2520Migration%7E12%7E20%7E1%7CWorkflow%2520Rules%7E15%7E28%7E2%7CPartner%2520Integrations%7E20%7E36%7E3&numTrials=600