How the Ohno Report Was Built

A step-by-step walkthrough of AI-assisted problem-solving

1
The Idea (Walking in a Park)

Art was walking in a park when he thought: what if we recreated Taiichi Ohno's famous 5 Why example as a complete 8-step problem-solving report? He pulled out his phone, opened Telegram, and spoke the idea to his AI assistant.

AI Understood
Context from past conversations, the structure of an 8-step report, what the 5 Why method looks like, that this was a Toyota historical example
Art Provided
The concept, the historical significance, direction on what kind of deliverable he wanted (interactive web page, not a static document)
Why this matters:

Art had been thinking about creating this re-creation for years. But the administrative overhead — structuring the HTML, coding the interactive elements, formatting the 5 Why chain — always kept him from starting. With AI handling that work, the only barrier was having the idea itself.

2
Providing Context (Still on the Phone)

The AI needed more detail. Art clarified: this should be a teaching tool showing how the 5 Why method fits into a complete problem-solving structure. It should include historical context about Toyota's management system in the 1960s. And it should be interactive, with buttons readers can click to explore deeper.

AI Pulled From
Skills files on problem-solving structure, past examples of interactive elements Art had created, the historical timeline of Toyota's system development
Art Clarified
Audience (lean practitioners learning problem-solving), format (web-based, not PDF), desired interactivity level
Voice input
→
AI processes
→
Clarifying questions
→
Final direction
3
First Draft (Generated Remotely)

The AI generated the complete problem-solving framework with all eight steps, the 5 Why analysis embedded within the root-cause analysis section, and placeholder areas for visual analysis and countermeasures.

The 8-Step Framework:
1. Clarify the Problem
2. Break Down the Problem
3. Set the Target
4. Root-Cause Analysis
5. Countermeasures
6. See Countermeasures Through
7. Monitor Results
8. Standardize Success
The Famous 5 Why Sequence (Step 4):
1
Why did the machine stop?
2
Why was there an overload?
3
Why was lubrication inadequate?
4
Why was the pump not working?
5
Why did chips penetrate the system?
90% complete in 2 hours
4
Editing and Refinement (Desktop)

Art switched to his desktop to review the complete draft. He challenged the AI's reasoning where it was weak, added historical context, and refined the countermeasure logic to match how the problem would actually have been solved at Toyota.

AI Handled
Formatting, section structure, initial logic flow, web page layout, interactive button placement
Art Handled
Correcting weak reasoning, verifying historical accuracy, ensuring countermeasures address root cause not symptoms, quality control
The Division of Labor:

The model provides general intelligence. The skills files provide domain expertise. The human provides direction, refinement, and quality control. The system gets better through use, the same way a good standard does.

Draft review
→
Challenge reasoning
→
Refine content
→
Verify accuracy
5
Adding Historical Context

Art reached out to John Shook, LEI Senior Advisor and fellow Toyota veteran, to add organizational context about what was happening at Toyota at the time. This layer of historical detail turned the report from a problem-solving exercise into a teaching tool.

Context Added:
  • Toyota's organizational structure in the 1960s
  • How the 5 Why method was being developed and taught
  • The role of machine operators in problem solving
  • Management system evolution at Toyota during this period
Interactive Elements:

Each step in the problem-solving report includes a button readers can click for further historical context. This transforms a static document into an interactive learning experience where the organizational development story runs parallel to the technical problem-solving story.

6
Publishing to Web

Once the content was finalized, Art pushed it live to his website. Because he had migrated off WordPress to a modern platform that works natively with AI tools, publishing was straightforward and required no fighting with editors or plugin management.

Final review
→
Format check
→
Push live
→
Published
The entire project — from concept to published interactive web page — took 2 hours to reach 90% completion
Why This Matters:

Art had thought about creating this resource for years but never started because the administrative work — formatting, coding the interactive elements, structuring the HTML, managing the historical footnotes — felt too onerous to justify. AI removed that barrier. The valuable work (applying judgment, verifying accuracy, adding context) stayed with Art. The administrative overhead disappeared.

7
The Breakthrough

This wasn't just about creating one report faster. It was proof that decades of postponed projects — ideas Art had shelved because the administrative burden was too high — were suddenly feasible.

"AI is making me more productive every day in ways that are concrete and low stress — not because the task was beyond me, but because the administrative work was too high."

— Art Smalley

What Changed
The barrier between having an idea and making it real collapsed. Art went from 50,000 lines of custom code to zero. He went from weeks of work to hours. He went from postponing valuable projects to completing them on weekend walks. The technology didn't replace his judgment — it removed the friction that kept his knowledge locked in file cabinets.
Humans + AI > Problems

Read Art's full story at lean.org
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