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Iterative PDCA cycle for systematic experimentation and continuous improvement
Plan-Do-Check-Act (PDCA)
Apply PDCA cycle for continuous improvement through iterative problem-solving and process optimization.
Description
Four-phase iterative cycle: Plan (identify and analyze), Do (implement changes), Check (measure results), Act (standardize or adjust). Enables systematic experimentation and improvement.
Usage
/plan-do-check-act [improvement_goal]
Variables
GOAL: Improvement target or problem to address (default: prompt for input)
CYCLE_NUMBER: Which PDCA iteration (default: 1)
Steps
Phase 1: PLAN
Define the problem or improvement goal
Analyze current state (baseline metrics)
Identify root causes (use /why or /cause-and-effect)
Develop hypothesis: "If we change X, Y will improve"
Design experiment: what to change, how to measure success
Set success criteria (measurable targets)
Phase 2: DO
Implement the planned change (small scale first)
Document what was actually done
Record any deviations from plan
Collect data throughout implementation
Note unexpected observations
Phase 3: CHECK
Measure results against success criteria
Compare to baseline (before vs. after)
Analyze data: did hypothesis hold?
Identify what worked and what didn't
Document learnings and insights
Phase 4: ACT
If successful: Standardize the change
Update documentation
Train team
Create checklist/automation
Monitor for regression
If unsuccessful: Learn and adjust
Understand why it failed
Refine hypothesis
Start new PDCA cycle with adjusted plan
If partially successful:
Standardize what worked
Plan next cycle for remaining issues
Examples
Example 1: Reducing Build Time
CYCLE 1
───────
PLAN:
Problem: Docker build takes 45 minutes
Current State: Full rebuild every time, no layer caching
Root Cause: Package manager cache not preserved between builds
Hypothesis: Caching dependencies will reduce build to <10 minutes
Change: Add layer caching for package.json + node_modules
Success Criteria: Build time <10 minutes on unchanged dependencies
DO:
- Restructured Dockerfile: COPY package*.json before src files
- Added .dockerignore for node_modules
- Configured CI cache for Docker layers
- Tested on 3 builds
CHECK:
Results:
- Unchanged dependencies: 8 minutes ✓ (was 45)
- Changed dependencies: 12 minutes (was 45)
- Fresh builds: 45 minutes (same, expected)
Analysis: 82% reduction on cached builds, hypothesis confirmed
ACT:
Standardize:
✓ Merged Dockerfile changes
✓ Updated CI pipeline config
✓ Documented in README
✓ Added build time monitoring
New Problem: 12 minutes still slow when deps change
→ Start CYCLE 2
CYCLE 2
───────
PLAN:
Problem: Build still 12 min when dependencies change
Current State: npm install rebuilds all packages
Root Cause: Some packages compile from source
Hypothesis: Pre-built binaries will reduce to <5 minutes
Change: Use npm ci instead of install, configure binary mirrors
Success Criteria: Build <5 minutes on dependency changes
DO:
- Changed to npm ci (uses package-lock.json)
- Added .npmrc with binary mirror configs
- Tested across 5 dependency updates
CHECK:
Results:
- Dependency changes: 4.5 minutes ✓ (was 12)
- Compilation errors reduced to 0 (was 3)
Analysis: npm ci faster + more reliable, hypothesis confirmed
ACT:
Standardize:
✓ Use npm ci everywhere (local + CI)
✓ Committed .npmrc
✓ Updated developer onboarding docs
Total improvement: 45min → 4.5min (90% reduction)
✓ PDCA complete, monitor for 2 weeks
Example 2: Reducing Production Bugs
CYCLE 1
───────
PLAN:
Problem: 8 production bugs per month
Current State: Manual testing only, no automated tests
Root Cause: Regressions not caught before release
Hypothesis: Adding integration tests will reduce bugs by 50%
Change: Implement integration test suite for critical paths
Success Criteria: <4 bugs per month after 1 month
DO:
Week 1-2: Wrote integration tests for:
- User authentication flow
- Payment processing
- Data export
Week 3: Set up CI to run tests
Week 4: Team training on test writing
Coverage: 3 critical paths (was 0)
CHECK:
Results after 1 month:
- Production bugs: 6 (was 8)
- Bugs caught in CI: 4
- Test failures (false positives): 2
Analysis: 25% reduction, not 50% target
Insight: Bugs are in areas without tests yet
ACT:
Partially successful:
✓ Keep existing tests (prevented 4 bugs)
✓ Fix flaky tests
Adjust for CYCLE 2:
- Expand test coverage to all user flows
- Add tests for bug-prone areas
→ Start CYCLE 2
CYCLE 2
───────
PLAN:
Problem: Still 6 bugs/month, need <4
Current State: 3 critical paths tested, 12 paths total
Root Cause: UI interaction bugs not covered by integration tests
Hypothesis: E2E tests for all user flows will reach <4 bugs
Change: Add E2E tests for remaining 9 flows
Success Criteria: <4 bugs per month, 80% coverage
DO:
Week 1-3: Added E2E tests for all user flows
Week 4: Set up visual regression testing
Coverage: 12/12 user flows (was 3/12)
CHECK:
Results after 1 month:
- Production bugs: 3 ✓ (was 6)
- Bugs caught in CI: 8 (was 4)
- Test maintenance time: 3 hours/week
Analysis: Target achieved! 62% reduction from baseline
ACT:
Standardize:
✓ Made tests required for all PRs
✓ Added test checklist to PR template
✓ Scheduled weekly test review
✓ Created runbook for test maintenance
Monitor: Track bug rate and test effectiveness monthly
✓ PDCA complete
Example 3: Improving Code Review Speed
PLAN:
Problem: PRs take 3 days average to merge
Current State: Manual review, no automation
Root Cause: Reviewers wait to see if CI passes before reviewing
Hypothesis: Auto-review + faster CI will reduce to <1 day
Change: Add automated checks + split long CI jobs
Success Criteria: Average time to merge <1 day (8 hours)
DO:
- Set up automated linter checks (fail fast)
- Split test suite into parallel jobs
- Added PR template with self-review checklist
- CI time: 45min → 15min
- Tracked PR merge time for 2 weeks
CHECK:
Results:
- Average time to merge: 1.5 days (was 3)
- Time waiting for CI: 15min (was 45min)
- Time waiting for review: 1.3 days (was 2+ days)
Analysis: CI faster, but review still bottleneck
ACT:
Partially successful:
✓ Keep fast CI improvements
Insight: Real bottleneck is reviewer availability, not CI
Adjust for new PDCA:
- Focus on reviewer availability/notification
- Consider rotating review assignments
→ Start new PDCA cycle with different hypothesis
Notes
Start with small, measurable changes (not big overhauls)
PDCA is iterative—multiple cycles normal
Failed experiments are learning opportunities
Document everything: easier to see patterns across cycles
Success criteria must be measurable (not subjective)
Phase 4 "Act" determines next cycle or completion
If stuck after 3 cycles, revisit root cause analysis
PDCA works for technical and process improvements
Use /analyse-problem (A3) for comprehensive documentationdon't have the plugin yet? install it then click "run inline in claude" again.