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

1. 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%
2. Apply Little's Law: Cycle Time = WIP ÷ Throughput
   • If cycle time is high but throughput is stable, WIP is too high
3. 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
4. Set WIP limits: initial recommendation = team size × 1.5 per column, then adjust based on data.
5. 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

• Data sources and assumptions are explicitly stated
• Calculations are reproducible from provided inputs
• Visualizations or tables have clear labels, units, and time ranges
• Caveats and confidence levels are documented for estimates

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

• v1.0.0 — Initial release