Exploring the intersection of Lean thinking and AI innovation to drive organizational excellence
An amusing internet tale about Toyota often goes something like this: the company’s legendary pull system sprang from Taiichi Ohno's epiphany during a visit to a Memphis-based supermarket chain called Piggly Wiggly. According to the legend, Ohno visited the store during a 1956 U.S. visit and was amazed by the way shelves were instantly refilled as customers shopped. And the thus the concept for his replenishment “supermarket” style of production was born. Despite the fact that no one can seem to locate a primary source for this story, it has been widely repeated for decades on internet blogs, LinkedIn, Wikipedia, and even by major news outlets like Reuters.
Toyota Motor Corporation is often described through the lens of manufacturing, especially the Toyota Production System (TPS). But Toyota’s long-run performance also depends on a set of less-visible, tightly linked systems: product development, production engineering, quality, marketing, finance, and various other critical management systems. This article provides a structured history of Toyota’s product development engineering organization, tracing its evolution from a small automotive group inside a loom manufacturer in the 1930s to today’s mobility-era development structures. Future articles will examine the early loom business, the parallel evolution of production engineering and other disciplines.
Exploring how leaders can combine Lean thinking with today's narrow AI tools through five practical levels of collaboration—from basic chat to agentic systems—while keeping humans at the center.
Humans perceive and verify end-to-end, while LLMs reason middle-to-middle. How combining both creates a powerful two-layer PDCA loop for learning together.
The signals surrounding AI couldn't be more confusing — warnings of massive unemployment, glitzy robot demos, yet quiet transformation happening in software and consulting. The key lies in understanding Big AI versus Narrow AI, and partnering AI with lean rather than treating it as an all-encompassing solution.
Drawing from 30 years of Lean implementation, this framework shows why successful AI adoption requires three elements working together: Technology, Behavior, and Management systems. When any element is zero, impact is zero. Learn how to multiply results instead of adding them.
Most Lean stories focus on the assembly line—kanban, standardized work, 5S—but overlook Toyota’s hidden engine of excellence: Production Engineering. Beneath the visible face of the Toyota Production System lay a decades-long effort to raise process capability through deep engineering kaizen—redesigning chucks, spindles, bearings, and fixtures. While consultants documented the daily management tools (Track 3) and researchers studied product development (Track 1), the middle layer—Track 2 Production Engineering (Seisan Gijutsu)—remained invisible. This “missing link” explains how Toyota moved from 5 % scrap rates to 0.05 % while holding micron-level precision. The story reminds us that Lean is not just about managing work—it’s about engineering process capability that others can’t see.
After spending some time building various coaching tools with AI, I began to see clear patterns in how people collaborate with large language models. The result is a five-level framework for AI proficiency — from casual chat use to full system integration. It explains why some users create breakthrough results while others see only hype, and how Lean thinking can guide us toward smarter human-AI collaboration.
Tokens vs Torque: 4 Types of Situations and how Ai can augment our abilities.
How I went from a full blown skeptic to a believer in the positive use cases of Ai in conjunction with topics like problem solving and Lean Thinking
