Executive Summary
A 12-engineer team at CloudSync Solutions consistently delivered only 58% of sprint commitments despite working extended hours. Analysis of 847 pull requests over 90 days revealed the root cause wasn't estimation accuracy—it was work-in-progress overload creating invisible queues. After implementing WIP limits and flow-based practices, sprint completion rose to 89% within 8 weeks.
The Initial Hypothesis
CloudSync's engineering manager, James Okonkwo, had a theory: the team was bad at estimation. Stories consistently took 2-3x longer than planned. The obvious solution seemed to be estimation training and story point recalibration.
The data told a different story entirely.
Key Finding: The WIP Problem
Analysis of GitHub data revealed a critical pattern:
Average Work-in-Progress: 4.7 open PRs per developer
Each engineer was simultaneously juggling nearly 5 pieces of work. The cognitive load was unsustainable, and the queuing effects were devastating.
Cycle Time Distribution
| Percentile | Observed | DORA Elite | Gap |
|---|---|---|---|
| P50 (Median) | 5.2 days | 1.5 days | +247% |
| P75 | 8.1 days | 3.0 days | +170% |
| P95 | 12.8 days | 7.0 days | +83% |
The high variance (P95 nearly 2.5x P50) made sprint planning essentially guesswork. No amount of estimation training would fix this—the problem was throughput, not sizing.
Root Cause: Little's Law in Action
The data confirmed what queuing theory predicts: high WIP causes exponential delay growth. With 4.7 concurrent items per developer and reviews averaging 18 hours wait time, work spent 42% of its lifecycle waiting—not being worked on.
Results After Intervention
| Week | Cycle Time (P50) | WIP/Dev | Sprint Completion |
|---|---|---|---|
| 0 (baseline) | 5.2 days | 4.7 | 58% |
| 2 | 4.1 days | 3.2 | 67% |
| 4 | 2.8 days | 2.4 | 78% |
| 8 | 1.9 days | 2.1 | 89% |
Three months post-intervention, sprint completion stabilized at 87-92% with no increase in working hours.