Analyse

Smart Analysis

Intelligently select and apply the most appropriate Kaizen analysis technique based on what you're analyzing.

Description

Analyzes context and chooses best method: Gemba Walk (code exploration), Value Stream Mapping (workflow/process), or Muda Analysis (waste identification). Guides you through the selected technique.

Usage

/analyse [target_description]

Examples:

• /analyse authentication implementation
• /analyse deployment workflow
• /analyse codebase for inefficiencies

Variables

• TARGET: What to analyze (default: prompt for input)
• METHOD: Override auto-selection (gemba, vsm, muda)

Method Selection Logic

Gemba Walk → When analyzing:

• Code implementation (how feature actually works)
• Gap between documentation and reality
• Understanding unfamiliar codebase areas
• Actual vs. assumed architecture

Value Stream Mapping → When analyzing:

• Workflows and processes (CI/CD, deployment, development)
• Bottlenecks in multi-stage pipelines
• Handoffs between teams/systems
• Time spent in each process stage

Muda (Waste Analysis) → When analyzing:

• Code quality and efficiency
• Technical debt
• Over-engineering or duplication
• Resource utilization

Steps

1. Understand what's being analyzed
2. Determine best method (or use specified method)
3. Explain why this method fits
4. Guide through the analysis
5. Present findings with actionable insights

Method 1: Gemba Walk

"Go and see" the actual code to understand reality vs. assumptions.

When to Use

• Understanding how feature actually works
• Code archaeology (legacy systems)
• Finding gaps between docs and implementation
• Exploring unfamiliar areas before changes

Process

1. Define scope: What code area to explore
2. State assumptions: What you think it does
3. Observe reality: Read actual code
4. Document findings:

• Entry points
• Actual data flow
• Surprises (differs from assumptions)
• Hidden dependencies
• Undocumented behavior

1. Identify gaps: Documentation vs. reality
2. Recommend: Update docs, refactor, or accept

Example: Authentication System Gemba Walk

SCOPE: User authentication flow

ASSUMPTIONS (Before):
• JWT tokens stored in localStorage
• Single sign-on via OAuth only
• Session expires after 1 hour
• Password reset via email link

GEMBA OBSERVATIONS (Actual Code):

Entry Point: /api/auth/login (routes/auth.ts:45)
├─> AuthService.authenticate() (services/auth.ts:120)
├─> UserRepository.findByEmail() (db/users.ts:67)
├─> bcrypt.compare() (services/auth.ts:145)
└─> TokenService.generate() (services/token.ts:34)

Actual Flow:
1. Login credentials → POST /api/auth/login
2. Password hashed with bcrypt (10 rounds)
3. JWT generated with 24hr expiry (NOT 1 hour!)
4. Token stored in httpOnly cookie (NOT localStorage)
5. Refresh token in separate cookie (15 days)
6. Session data in Redis (30 days TTL)

SURPRISES:
✗ OAuth not implemented (commented out code found)
✗ Password reset is manual (admin intervention)
✗ Three different session storage mechanisms:
  - Redis for session data
  - Database for "remember me"
  - Cookies for tokens
✗ Legacy endpoint /auth/legacy still active (no auth!)
✗ Admin users bypass rate limiting (security issue)

GAPS:
• Documentation says OAuth, code doesn't have it
• Session expiry inconsistent (docs: 1hr, code: 24hr)
• Legacy endpoint not documented (security risk)
• No mention of "remember me" in docs

RECOMMENDATIONS:
1. HIGH: Secure or remove /auth/legacy endpoint
2. HIGH: Document actual session expiry (24hr)
3. MEDIUM: Clean up or implement OAuth
4. MEDIUM: Consolidate session storage (choose one)
5. LOW: Add rate limiting for admin users

Example: CI/CD Pipeline Gemba Walk

SCOPE: Build and deployment pipeline

ASSUMPTIONS:
• Automated tests run on every commit
• Deploy to staging automatic
• Production deploy requires approval

GEMBA OBSERVATIONS:

Actual Pipeline (.github/workflows/main.yml):
1. On push to main:
   ├─> Lint (2 min)
   ├─> Unit tests (5 min) [SKIPPED if "[skip-tests]" in commit]
   ├─> Build Docker image (15 min)
   └─> Deploy to staging (3 min)

2. Manual trigger for production:
   ├─> Run integration tests (20 min) [ONLY for production!]
   ├─> Security scan (10 min)
   └─> Deploy to production (5 min)

SURPRISES:
✗ Unit tests can be skipped with commit message flag
✗ Integration tests ONLY run for production deploy
✗ Staging deployed without integration tests
✗ No rollback mechanism (manual kubectl commands)
✗ Secrets loaded from .env file (not secrets manager)
✗ Old "hotfix" branch bypasses all checks

GAPS:
• Staging and production have different test coverage
• Documentation doesn't mention test skip flag
• Rollback process not documented or automated
• Security scan results not enforced (warning only)

RECOMMENDATIONS:
1. CRITICAL: Remove test skip flag capability
2. CRITICAL: Migrate secrets to secrets manager
3. HIGH: Run integration tests on staging too
4. HIGH: Delete or secure hotfix branch
5. MEDIUM: Add automated rollback capability
6. MEDIUM: Make security scan blocking

Method 2: Value Stream Mapping

Map workflow stages, measure time/waste, identify bottlenecks.

When to Use

• Process optimization (CI/CD, deployment, code review)
• Understanding multi-stage workflows
• Finding delays and handoffs
• Improving cycle time

Process

1. Identify start and end: Where process begins and ends
2. Map all steps: Including waiting/handoff time
3. Measure each step:

• Processing time (work happening)
• Waiting time (idle, blocked)
• Who/what performs step

1. Calculate metrics:

• Total lead time
• Value-add time vs. waste time
• % efficiency (value-add / total time)

1. Identify bottlenecks: Longest steps, most waiting
2. Design future state: Optimized flow
3. Plan improvements: How to achieve future state

Example: Feature Development Value Stream Map

CURRENT STATE: Feature request → Production

Step 1: Requirements Gathering
├─ Processing: 2 days (meetings, writing spec)
├─ Waiting: 3 days (stakeholder review)
└─ Owner: Product Manager

Step 2: Design
├─ Processing: 1 day (mockups, architecture)
├─ Waiting: 2 days (design review, feedback)
└─ Owner: Designer + Architect

Step 3: Development
├─ Processing: 5 days (coding)
├─ Waiting: 2 days (PR review queue)
└─ Owner: Developer

Step 4: Code Review
├─ Processing: 0.5 days (review)
├─ Waiting: 1 day (back-and-forth changes)
└─ Owner: Senior Developer

Step 5: QA Testing
├─ Processing: 2 days (manual testing)
├─ Waiting: 1 day (bug fixes, retest)
└─ Owner: QA Engineer

Step 6: Staging Deployment
├─ Processing: 0.5 days (deploy, smoke test)
├─ Waiting: 2 days (stakeholder UAT)
└─ Owner: DevOps

Step 7: Production Deployment
├─ Processing: 0.5 days (deploy, monitor)
├─ Waiting: 0 days
└─ Owner: DevOps

───────────────────────────────────────
METRICS:
Total Lead Time: 22.5 days
Value-Add Time: 11.5 days (work)
Waste Time: 11 days (waiting)
Efficiency: 51%

BOTTLENECKS:
1. Requirements review wait (3 days)
2. Development time (5 days)
3. Stakeholder UAT wait (2 days)
4. PR review queue (2 days)

WASTE ANALYSIS:
• Waiting for reviews/approvals: 9 days (82% of waste)
• Rework due to unclear requirements: ~1 day
• Manual testing time: 2 days

FUTURE STATE DESIGN:

Changes:
1. Async requirements approval (stakeholders have 24hr SLA)
2. Split large features into smaller increments
3. Automated testing replaces manual QA
4. PR review SLA: 4 hours max
5. Continuous deployment to staging (no approval)
6. Feature flags for production rollout (no wait)

Projected Future State:
Total Lead Time: 9 days (60% reduction)
Value-Add Time: 8 days
Waste Time: 1 day
Efficiency: 89%

IMPLEMENTATION PLAN:
Week 1: Set review SLAs, add feature flags
Week 2: Automate test suite
Week 3: Enable continuous staging deployment
Week 4: Train team on incremental delivery