Smart Analysis

Selects and applies one of the three Kaizen observation methods below (Gemba / VSM / Muda).

Method Selection Logic

Gemba Walk — analyzing code implementation, doc-vs-reality gaps, unfamiliar codebase areas Value Stream Mapping — workflows, pipelines, bottlenecks, handoffs, cycle time Muda (Waste Analysis) — over-engineering, duplication, technical debt, resource waste

If the target is not an observation task, hand off instead of forcing a fit:

The target is	Use
A problem needing a full writeup (background → countermeasures → follow-up)	kaizen-analyse-problem (A3)
A causal question — "why does this happen?"	kaizen-analyse-problem/root-cause-methods.md (Five Whys, Fishbone)
A change you want to test and measure	kaizen-plan-do-check-act
A bug, test failure, or unexpected behavior	systematic-debugging

Steps

Select the method (or hand off per the table), explain why it fits, execute it, then present findings as actionable recommendations.

Method 1: Gemba Walk

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

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, hidden dependencies, undocumented behavior
5. Identify gaps: Documentation vs. reality
6. Recommend: Update docs, refactor, or accept

Method 2: Value Stream Mapping

1. Identify start and end: Where process begins and ends
2. Map all steps: Including waiting/handoff time
3. Measure each step: Processing time, waiting time, owner
4. Calculate metrics: Total lead time, value-add vs. waste, % efficiency
5. Identify bottlenecks: Longest steps, most waiting
6. Design future state: Optimized flow
7. Plan improvements: How to get there

Method 3: Muda (Waste Analysis)

The 7 Wastes (Applied to Software)

1. Overproduction: Unused features, premature abstraction, unnecessary complexity
2. Waiting: Slow builds, review delays, blocked dependencies
3. Transportation: Unnecessary data transformations, redundant API layers
4. Over-processing: Redundant validation, excessive logging, over-normalized data
5. Inventory: Unmerged branches, half-finished features, untriaged bugs
6. Motion: Context switching, manual deployments, repetitive tasks
7. Defects: Production bugs, flaky tests, technical debt, incomplete features

Scope the area, examine each waste type for concrete instances, quantify impact (time, complexity, cost), prioritize by impact, then propose elimination strategies.