This week’s provocation: Mavericks, intellectual curiosity, and changing entire industries
I once did a fascinating project with the operations team of a big pharma business helping them to understand how they could combine agile ways of working with Lean manufacturing techniques. As part of the research for it I did a dive into the origins and principles of the Toyota Production System (TPS) from which Lean arose, and discovered that (just like Agile) TPS is not merely a collection of tools like Just-in-Time (JIT), the five whys, or Kanban, but rather a deeply embedded mindset that has beneficial application across a far broader set of contexts than just manufacturing. This is a mindset that is really focused on:
Continuous improvement (Kaizen): A relentless pursuit of identifying and eliminating waste (muda) and inconsistencies (mura) in every process, no matter how small.
Respect for people: Recognising that empowered and engaged employees are the key to innovation and problem-solving, encouraging their active participation in the improvement process.
Going to the source (Genchi Genbutsu): Physically going to the workplace to observe and understand problems firsthand, rather than relying on reports or assumptions (oh how I wish more leaders would do this).
Building a culture of problem solving: Creating an environment where errors are seen as opportunities for learning and improvement, and employees are encouraged to stop production when a problem arises to address it immediately (jidoka).
Whilst there were some key differences between Lean and Agile which meant that context was all important in understanding the role for each, there were also some fundamental similarities in philosophy - notably challenging the status quo, fostering a culture of continuous learning and improvement, and empowering people to find innovative solutions to problems, thereby enabling the organisation to adapt and thrive in an ever-changing environment.
Understanding more about TPS also taught me a lot about transforming systems. The story of how one engineer, Taiichi Ohno, created a system that went on to change manufacturing forever is a lesson in systematically questioning assumptions, deeply observing reality, persistence and the value of intellectual curiosity.
Constraint as catalyst
After World War II, Japan’s economy was on the floor. Resources in the defeated country were extremely scarce. Toyota, a small carmaker at this time, faced an existential problem - how could they survive against American car giants like Ford and GM who were operating with massive scale and seemingly abundant capital?
At the time the accepted wisdom for manufacturing was rooted in mass production and the kind of system that Henry Ford had perfected with large inventories, long production runs, standardised products, and economies of scale. In post-war Japan that model just couldn’t work. Toyota couldn’t afford to keep huge inventories, demand was low, capital was tight, and mistakes were expensive. But despite the fact that Toyota was operating at a loss and it couldn’t afford to hire new people or purchase machinery and equipment to compete, the company’s President set an ambitious strategic goal to catch up with the Americans within three years.
Intellectual curiosity and observation
Taiichi Ohno was a relentlessly curious production engineer at Toyota who learned through deep observation. He spent hours on the factory floor with a stopwatch, watching how workers moved, how inventory piled up, and where time was being wasted. He believed problems lived in plain sight, if only people would stop to see them.
Ohno’s big insight was that waste (or muda, as he called it) was everywhere. And not just the obvious kind like excess inventory or idle machines, but also wasted movement, overproduction, waiting time, defects, and unnecessary processes. He began documenting what he later called the ‘seven wastes’ and treating them as enemies of value.
One of Ohno’s most important ideas came from a less than obvious source. In the 1950s Toyota sent Ohno to America to learn about American manufacturing techniques and he toured production facilities like those owned by the Ford Motor Company. But the American experience that influenced him the most was not a tour of a factory, but a visit to a supermarket. He was intrigued by how American supermarkets replenished goods: customers pulled what they wanted from shelves, and the store restocked based on consumption. This was the opposite of pushing goods onto shelves without knowing demand. Ohno took this principle and reimagined it as ‘Just-In-Time’ production (nothing should be made until it was needed downstream). He flipped the entire logic of production from push to pull.
The silent revolution
There’s a lot to like about how Ohno scaled the new system. He didn’t impose the TPS from the top down but instead started with one factory. He took an inclusive approach, making frontline workers part of the process and ensuring they were active problem-solvers. He created visual systems (like Kanban) that signalled when parts were needed. He reduced batch sizes. He encouraged workers to stop the production line when defects occurred - an act that would have been unthinkable in traditional mass production but which served to surface problems much earlier and improve quality.
When Ohno wanted to train managers to see inefficiencies, he would draw a chalk circle on the factory floor and ask them to stand in it for hours doing nothing but observing. Many managers found this tedious or pointless, but eventually they would start to notice small things that others had overlooked - inventory piling up unseen, workers walking too far to get a tool, or a machine sitting idle waiting for a part. It was like a fascinating kind of mindfulness practice that developed attentiveness as a leadership habit, encouraged managers to slow down and let go of assumptions, and to see waste with their own eyes, rather than relying on reports.
The idea that transformational change can start with attention and observation is a good lesson. Ohno’s system was a wholesale rethinking of how value was created and one that that redefined efficiency (flow not speed), broke the myth of scale (agility and quality trumps size and volume), shifted power to the workers (who were able to work smarter rather than harder), and institutionalised learning (every process was open to challenge and improvement).
For years, the West ignored Toyota. And then, in the 1980s, American automakers were stunned to find that Toyota produced higher-quality cars, faster, cheaper and with far fewer workers. MIT launched a study of Toyota’s methods and coined the term ‘Lean Manufacturing’ to describe it. It was a system that went on to impact healthcare, logistics, retail, construction and even financial services and government.
Yet its origin wasn’t a founder or a CEO, but one relentless engineer who had the curiosity, persistence and belief to make change happen. All power to the mavericks.
Rewind and catch up:
Separating fads from trends, and second and third order effects of AI
Using synthetic personas and research to explore ideas
Image source
If you do one thing this week…
AI is already impacting the consulting industry in a pretty major way, but it can be hard to determine how much of this is net positive (AI transformation consulting fees, greater efficiency in analysis and delivering solutions) and how much is net negative (AI replacing consultants). The WSJ had an interesting story this week titled “AI Is Coming for the Consultants. Inside McKinsey, ‘This Is Existential.’”, looking at how McKinsey is having to steer through its own existential transformation.
Ross Dawson had some interesting data points showing that the market performance of the big consultancies has been pretty poor so far this year, and offering up a sensible range of reasons why that might be so. Meanwhile, this recent paper (based on data from Belgium over twenty years) shows that consulting does indeed add value to businesses notably through increased productivity (HT Ben Evans for that one).
Links of the week
Sign of the times - Unilever has created an ‘AI Studio’ for its Beauty division to create an average of 400 creative assets per product (versus the 20 per campaign it used to).
A good point from Tey Bannerman based on a study analysing big AI models against cultural values from 107 countries worldwide which found that they reflected the assumptions inherent in English-speaking, Western European societies: ‘None aligned with how people in Africa, Latin America, or the Middle East actually build trust, show respect, or resolve conflicts’.
A nice counterpoint to the prevailing AI-will-take-junior-jobs narrative - I liked Antony Mayfield’s thought about hiring more young people in the age of AI, not less, ‘not because they understand AI or have “AI native skills” but because they have fewer preconceptions and prejudices about work and how it gets done’. On the same topic I also liked this, from Sangeet Paul Chowdray: ‘Ask someone to explain away their own work as simply a set of discrete tasks, and they’ll likely resist, insisting that it’s a lot more than just that. But ask them to break down someone else’s job, especially one they don’t understand well, and the task model starts to feel plausible. That’s the trap - believing other people’s work is just a bundle of tasks, because you don’t see the constraints their role resolves.’
A couple of useful updates on NotebookLM including video overviews (you can now get narrated video presentations generated from your source material), and the ability to store multiple outputs (for example for different audiences) in one Notebook
“One day, I wrote to it about my father, who died more than 55 years ago. I typed, “The space he occupied in my mind still feels full.” ChatGPT replied, “Some absences keep their shape. That line stopped me. Not because it was brilliant, but because it was uncannily close to something I hadn’t quite found words for. It felt as if ChatGPT was holding up a mirror and a candle: just enough reflection to recognize myself, just enough light to see where I was headed.” I can’t help feeling that using AI as a therapist is far from ideal for humanity but nonetheless this was an interesting piece on the topic (NYT subscribers, but you can read a bunch of quotes from the article here).
Quote of the week
“If I’d had my way, we would have left it in the lab for longer and done more things like AlphaFold, maybe cured cancer or something like that.”
Demis Hassabis, head of Google’s DeepMind on why AI is 10x bigger and faster than the industrial revolution and why he wishes that tech giants had moved more slowly with AI. Speaking of Deepmind, they’ve just created a model which can generate dynamic worlds in hi-fidelity, making it much easier to train AI agents with rich simulations.
And finally…
Quite the chart, from the World Uncertainty index (computed by counting the % of word “uncertain”, or its variant, in the Economist Intelligence Unit country reports)
Weeknotes
This week I’ve been overseas with work. I am next week too. It’s one of those times when there’s a lot of travel for work. I’m doing a whole series of leadership interventions and whilst I’m missing home the sessions have been good fun to do.
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My favourite quote captures what I try to do every day, and it’s from renowned Creative Director Paul Arden: ‘Do not covet your ideas. Give away all you know, and more will come back to you’.
And remember - only dead fish go with the flow.