Monte Carlo simulations for Scrum/Kanban (Agile Gadgets)

Generate probabilistic forecasts based on historical throughput to reduce delivery uncertainty.

Predict completion timelines for large initiatives and epics.

Forecast based on Epic-level estimates to plan larger bodies of work with consistent sizing.

Model forecasts using an alternative throughput source when the current scope has limited or missing history.

Model delivery scenarios by adjusting resource allocation coefficients.

Compare statistical forecast against planned projections to assess risk exposure.

Simulate alternative delivery paths before committing to scope or deadlines.

Use the same analytics engine across Jira Software, Jira Product Discovery, Jira Service Management, and Confluence.

Use data from Scrum, Kanban, Jira Spaces, Cross-Releases, multi-select Epics, dynamic Epic lists, hierarchies, or custom JQL filters.

Aggregate multiple Jira boards or other data sources into consolidated cross-team analytics.

Run analytics across issue hierarchies—epics, initiatives, and parent/child structures—to understand delivery progress at every level.

Slice delivery metrics by any Jira field without using external BI tools.

Configure all charts from a unified interface instead of searching multiple gadgets.

Generate ready-to-use charts instantly while retaining advanced configuration options.

Use the app on the space page to discover breakdowns and metrics during review and planning sessions without leaving the work context.

Add visual health indicators to dashboards for executive reporting.

A focused live walkthrough to help your team configure the right charts and metrics from day one.

Guaranteed response window and accelerated fixes for production-critical issues.

Tailored expert-led demo, onboarding, and best-practice sessions for delivery teams and stakeholders.

Hands-on support for Server/DC → Cloud transitions, configuration alignment, and data validation.