| name | codexkit-kanban-flow-analyzer |
| description | Analyze Kanban flow metrics to identify bottlenecks and improve team throughput. Covers Cycle Time, Throughput, WIP, and Flow Efficiency using Little's Law and Theory of Constraints. Use when a team wants to optimize delivery flow or improve predictability. Do not use for sprint-based Scrum planning — use sprint-planning-assistant instead. |
| version | 1.0.0 |
| category | data |
Kanban Flow Analyzer
Purpose
Transform raw task completion data into actionable flow insights — find bottlenecks, set WIP limits, and forecast delivery dates with confidence intervals.
When to use
- team wants to reduce cycle time or improve delivery predictability
- WIP is growing and tasks are stalling in certain board columns
- management asks "when will this be done?" and the team has no data-driven answer
- transitioning from time-boxed sprints to continuous flow
When not to use
- sprint planning with story points (use sprint-planning-assistant)
- strategic roadmap or portfolio prioritization
- teams with fewer than 4 weeks of historical data
Inputs
- task data with start date and completion date (minimum 4 weeks history)
- board column names and WIP limits (if any)
- team size and working days per week
- specific questions or concerns about flow
Procedure
- Calculate 4 core flow metrics:
- Cycle Time: days from "In Progress" to "Done" — report P50 (median) and P85
- Throughput: items completed per week — report average and trend
- WIP: items currently in progress — compare to team capacity
- Flow Efficiency: (active work time / total elapsed time) × 100%
- Typical: 15–25% | High-performing: > 40%
- Apply Little's Law: Cycle Time = WIP ÷ Throughput
- If cycle time is high but throughput is stable, WIP is too high
- Detect bottlenecks using Theory of Constraints (5 Focusing Steps):
- Identify the column with highest average queue time
- Exploit: maximize throughput at the constraint
- Subordinate: slow upstream to match constraint capacity
- Elevate: add capacity or change process at constraint
- Repeat: new constraint will emerge
- Set WIP limits: initial recommendation = team size × 1.5 per column, then adjust based on data.
- Forecast delivery using Monte Carlo simulation:
- "If avg throughput = 12 items/week, 60-item backlog takes ~5 weeks at P85 confidence"
Output
- flow metrics dashboard (Cycle Time, Throughput, WIP, Flow Efficiency)
- bottleneck analysis with identified constraint and root cause
- WIP limit recommendations per board column
- top 3 actionable improvements with expected impact
- Monte Carlo forecast (items by date at P50, P85, P95 confidence)
Definition of done
- all 4 flow metrics are calculated and reported
- at least one bottleneck is identified with a root cause
- WIP limit recommendations are provided
- forecast includes at least P85 confidence level
Examples
- "Analyze our Jira board data for the last 8 weeks — why are tasks stuck in Code Review?"
- "Our cycle time went from 5 days to 12 days. What happened?"
- "Set WIP limits for a 7-person team with columns: To Do, In Progress, Review, QA, Done."
Quality Criteria
Verification (4C)
| Check | Question |
|---|
| Correctness | Are formulas, aggregations, and statistical methods applied correctly? |
| Completeness | Does the analysis cover all requested metrics and time ranges? |
| Context-fit | Are the chosen metrics relevant to the business question being answered? |
| Consequence | If this data were used for a decision today, what blind spots remain? |
Edge Cases
- Missing or incomplete data — Document gaps and their potential impact on conclusions. Provide ranges instead of point estimates.
- Outliers skewing results — Report with and without outliers. Document the decision to include or exclude.
- Changing data definitions mid-period — Split analysis at the change boundary and note the schema difference.
Changelog
