Chapter 3 summary Just-in-time from our book “Connecting the dots…”

It’s been a few months already that our book is out, and we have received a lot of good feedback so far. Thank you for those who purchased the book, read it and shared your thoughts.

Thought would share a short summary from one of the chapters of the book, which is about Just-in-time. This is exclusively for this newsletter subscribers.

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Just-In-Time (JIT) not as a scheduling technique or a kanban ritual, but as a way of thinking about time, waste, and demand.

The origin story is deceptively simple. A missed train in England led Kiichiro Toyoda to a profound realization: being even slightly late can cause massive downstream disruption. From that moment, JIT emerged as a principle rooted in timing not efficiency theater or machine utilization.

At its core, JIT asks a radical question: Why make anything before it is actually needed?

Toyota learned early that producing “just in case” hides problems. Inventory cushions mask defects, delays, and poor coordination. By deliberately reducing inventory, Toyota exposed these problems—forcing the organization to learn, adapt, and improve.

What’s striking is that JIT existed before kanban. Early implementations relied on simple daily production targets, strict discipline, and a deep respect for the system as a whole. Tools came later. The thinking came first.

It was Taiichi Ohno who operationalized JIT by treating inventory like water in a river: lower it, and the rocks (problems) appear. Kanban then evolved as a control mechanism not the goal, but a means to sustain flow and learning.

The deeper lesson of Just-In-Time is this:

Efficiency is not about keeping everyone busy. It is about aligning work with real demand and letting problems surface early.

For modern organizations, especially in IT and knowledge work, JIT challenges us to rethink backlogs, queues, multitasking, and “just in case” planning. Flow, responsiveness, and learning matter more than utilization charts.

Just-In-Time is ultimately not about manufacturing faster.
It’s about thinking better about time, uncertainty, and waste.

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Here are the links to purchase the book

eBook purchase link: https://leanpub.com/connecting-the-dots
Physical copy (paper back): Link
Hard bound copy : Link 

What you will find in this book ?

The book is organized around the two Houses of Toyota, which represent the structure of the system. Within these houses, we explore the key pillars and supporting concepts:

• Just-in-Time: Creating flow, reducing waste, and building responsiveness into the system.

• Jidoka: Stopping to fix problems and embedding quality into the process.

• Respect for People: Moving beyond slogans to understand what this principle demands in practice.

• Continuous Improvement (Kaizen): Why improvement must be ongoing and how it shapes learning.

• Genchi Genbutsu: Going to the source and seeing reality for yourself.

• Thinking about the Thinking Production System: A deeper reflection on TPS as a way of framing problems.

• Cultural Foundations: How Japanese history and philosophy influenced TPS principles. Leadership and Ethics: Learning from Toyota without becoming Toyota, and addressing the challenges of AI and efficiency-driven thinking.

Behavioural Fluency model and its practical applicability in organisations

Behavioral Fluency originates from behavioral science and performance psychology, particularly the work of Carl Binder, and focuses on what it actually takes for people and systems to perform reliably in the real world.

Fluency goes beyond knowledge or competence; it is the ability to perform critical behaviors accurately, at speed, with low cognitive effort, and under pressure. In organisations, leaders and coaches often assume that training, frameworks, or alignment automatically translate into performance.

In practice, many failures occur because key behaviors are not fluent they degrade when complexity, urgency, or stress increase. For example, leaders may understand systems thinking and Agile principles, yet revert to command-and-control decisions during a crisis. The issue is not mindset, but fluency. Organisations that deliberately build fluency through repeated practice, fast feedback, stable operating conditions, and clear standards develop leadership and teams that can sustain performance, adaptability, and learning as scale and complexity grow.

Practical applicability

A common organisational problem is the breakdown of communication between two dependent teams, for example, a product team and a platform team. On paper, both teams understand the importance of collaboration, have agreed interfaces, and attend coordination meetings. Yet under delivery pressure, misunderstandings increase, handoffs become slow, and frustration rises.

From a Behavioral Fluency perspective, the issue is not intent, structure, or attitude it is a lack of fluency in key communication behaviors. Critical behaviors such as clarifying assumptions, making dependencies explicit, negotiating trade-offs, and closing feedback loops are not fluent enough to hold under stress.

Applying the fluency model shifts the intervention away from more meetings or escalation paths and toward deliberate practice: defining a small set of observable communication behaviors, creating frequent, low-risk opportunities to practice them (for example, joint backlog refinement or dependency reviews), and providing fast feedback on speed, clarity, and reliability. Over time, communication improves not because people “try harder,” but because the system builds fluency in the behaviors that real collaboration depends on.

Everyone works with AI agents, but who controls the agents?

The AI problems in 2026

The average number of applications in organizations has risen from 897 to 957. Of these, only 27 percent are integrated with each other. Among IT decision-makers, 35 percent believe that integrating applications and data will hinder their AI ambitions. 

In addition, 40 percent say they are dealing with outdated IT infrastructure that prevents the organization from using data for AI purposes. The well-known data silo problem, discussed for years, is still very much a reality.

However, we are not there yet; 42 percent are still busy mapping out the risks. Consider how the organization remains compliant and whether security is properly arranged with the current AI implementations. To make things even harder, 41 percent say that their organization currently lacks sufficient AI expertise to develop AI agents and AI processes. There is therefore a great need for more knowledge within organizations. 

Read the rest of the article here

The AI boom is so huge it’s causing shortages everywhere else

The AI boom is so huge it’s causing shortages everywhere else

JPMorgan calculated last fall that the tech industry must collect an extra $650 billion in revenue every year — three times the annual revenue of AI chip giant Nvidia — to earn a reasonable investment return. That marker is probably even higher now because AI spending has increased.

Apple told its investors last week that the company is having trouble buying enough of two different types of computer chip that are essential for iPhones and Mac computers.

Read the rest of the article here