Layers

JUST IN TIME (JIT)

JIT is a Mechanism for Learning and Problem Solving

• While JIT is traditionally viewed as a method for controlling material flows and inventory, its deeper function is to facilitate problem solving, process improvement, and learning.

• JIT integrates the "routine" work of production with the work of problem identification, ensuring performance is evaluated with every exchange of products or information.

Converting Work into Structured Experiments

• By combining strict specifications with embedded tests, JIT converts static work designs into frequent, structured experiments.

• This approach validates the assumptions built into the work design; when the system fails to perform as specified, it signals that the design assumptions were incorrect, prompting immediate investigation.

The Role of Embedded Diagnostic Tests

• Systems must be designed with embedded tests that signal immediately and unambiguously when work is not proceeding as planned.

• These signals must be unambiguous regarding three factors:

  1. Trigger: A signal is sent every time a problem condition occurs.

  2. Responder: The signal indicates exactly who is supposed to respond.

  3. Action: The signal dictates exactly what the responder is supposed to do (e.g., restorative and corrective action).

Strict Specification of Pathways and Connections

• To make tests effective, product and information flows must be strictly specified rather than "pooled".

• Connections between customers and suppliers must be direct and binary. For example, a specific worker (customer) makes a request to a specific supplier via a single, unambiguous mechanism (like a kanban card or manifest).

• This specification allows for a direct comparison between what was requested and what was delivered, making errors immediately visible.

The "Close-in" Rule for Problem Solving

• For problem solving to be effective, it must occur close in time, place, process, and person to the origin of the problem.

• This immediacy ensures that information does not perish or lose context, which is common when problem solving is delayed or removed from the shop floor.

Inventory as a Diagnostic Tool

• Contrary to the popular belief that JIT strictly eliminates inventory, systems may use calculated buffers (e.g., a specific number of racks or spaces) to accommodate minor process fluctuations.

• These buffers act as "attention focusing mechanisms" (similar to control limits); if inventory drops below or rises above the specified range, it signals a loss of process control and triggers an investigation.

Standardization as a Baseline for Improvement

• Standardization (specification) is not used to control workers or extract maximum effort, but to provide a stable baseline for continuous improvement.

• JIT is not a tool for "static optimization" but a dynamic system that repeatedly tests and improves the way individual efforts are combined.

John Boyd Inventor of The OODA loop

The OODA loop is a four-stage decision-making model—Observe, Orient, Decide, Act—developed by U.S. Air Force Colonel John Boyd for making rapid, effective decisions in dynamic environments. The process starts by gathering information (Observe), then making sense of it in context (Orient), selecting a course of action (Decide), and finally, implementing it (Act), with the outcome feeding back into the observation phase to create a continuous cycle of learning and adaptation. The goal is to cycle through the loop faster than an opponent to gain a competitive advantage.

Karl Popper, one of the most influential philosophers of the 20th century, fundamentally challenged conventional views about scientific methodology. The classical positivist approach emphasised knowledge accumulation through observation and induction — drawing general conclusions from specific observations. Popper critiqued this approach, arguing that scientific knowledge is inherently provisional, representing the best understanding available at any moment, subject to revision when new evidence emerges.

At the core of Popper's philosophy is the principle of falsification. He proposed that for a theory to be considered scientific, it must be testable and capable of being proven false. This stands in contrast to the idea that scientific theories are validated through repeated confirmation. Popper argued that science progresses not by proving theories correct but by rigorously attempting to disprove them. For example, the hypothesis "all swans are white" is scientific because it can be falsified by the observation of a single black swan.

Key principles of falsificationism

The Demarcation Problem: Popper's theory is an answer to the "demarcation problem," which asks how to distinguish science from pseudoscience.

Active refutation: A scientist's goal is not to confirm a theory but to make bold predictions that could potentially be proven wrong through rigorous testing.

Contrast with induction: This contrasts with the classical view of induction, which argues that a theory becomes true through repeated confirmation. Popper argued this is a psychological process and not logically valid.

Scientific progress: Science advances by eliminating false theories rather than by proving theories true, which is a logically invalid process.

Risk-taking: Scientific theories must make risky, specific predictions. A theory is considered pseudoscientific if it can explain away any prediction to avoid being falsified, making it unfalsifiable.

Example of falsification: The phlogiston theory of combustion was falsified by Lavoisier's experiments, and Newton's corpuscular theory of light was disproven by Young's two-slit experiment.

Example of unfalsifiable theory: Popper famously criticized Freud's psychoanalysis and Marxism as unfalsifiable because their proponents could find a way to interpret any observation to fit the theory, making it immune to refutation.

The Mathematics of Creativity

What if I told you that creativity, something we usually think of as wild, mysterious, and unexplainable, actually follows mathematical patterns? From the way artists generate ideas to how scientists make breakthroughs, hidden equations and statistical rules shape our creative lives. Today we're going to break down the mathematics of creativity and by the end, you'll see imagination in a whole new way.

The Law of Large Numbers

Creativity isn't random. We often imagine creativity as lightning—sudden, chaotic, impossible to predict. But researchers like Dean Keith Simonton, a leading psychologist of creativity, argue otherwise. He studied thousands of works by composers, scientists, and inventors and found something surprising: Creative success follows statistical probability. The more attempts someone makes, the more likely they are to produce a masterpiece.

In other words, creativity has a law of large numbers. Think of Thomas Edison. He filed over 1,000 patents, most of them forgettable. But hidden in that pile were the light bulb and the phonograph. Same with Picasso. He created more than 20,000 works, but only a fraction define him today. Mathematically, it's simple: Quantity breeds quality. Every attempt increases the odds of a breakthrough.

Zipf’s Law of Ideas

But it's not just about trying more. There's also a distribution pattern in play. Enter Zipf's law, a principle from linguistics and mathematics. It says that in any large set, the frequency of outcomes follows a predictable curve. A few things are extremely common, most are mediocre, and a tiny fraction are extraordinary.

Apply this to creativity. Most of your ideas will be average. Some will be pretty good, and a rare few will be brilliant. That curve shows up everywhere: hit songs, bestselling books, viral TikToks. Mathematically, most creativity is noise, but the signal is in the outliers.

Combinational Creativity (Boden)

Another formula for creativity comes from Margaret Bowden, a pioneer in cognitive science. She argues that creativity is mostly combinatorial—taking existing elements and recombining them in novel ways. If you model this mathematically, it's like permutations and combinations. A limited number of building blocks can produce an astronomical number of new arrangements. That's why hip-hop sampling, meme culture, and scientific theories all feel new but are built from recombining what's already there.

Time & Growth (Gladwell)

Then there's the role of time and effort. You've probably heard of Malcolm Gladwell's 10,000-hour rule; though it's debated, it echoes a real mathematical truth. Skill follows an exponential curve. At first, progress is slow. But as hours accumulate, ability accelerates, and breakthroughs become more likely. Think of it like compound interest. The longer you invest in creative practice, the faster your growth rate. That's why mastery looks like magic from the outside, but underneath, it's math.

Edge of Chaos (Complexity Theory)

Here's another fascinating angle: complexity theory. Creativity often emerges at what scientists call the edge of chaos—the delicate point between total randomness and rigid order. Too much chaos, and nothing makes sense. Too much order, and nothing new happens. But in between lies the sweet spot where unexpected but meaningful connections form. Mathematicians model this with systems like cellular automata, showing that the richest patterns appear not in pure noise, not in rigid repetition, but right in the balance. That's essentially where creativity lives.

The Formula for Creativity

So what's the big takeaway? Creativity is probability plus combinations plus time plus balance. It's math hiding in plain sight. If you want to be more creative, the formula is simple:

• Produce more. Quantity matters.

• Recombine relentlessly. Mix old things into new forms.

• Stick with it. Time compounds your growth.

• Find the edge of chaos. Balance structure with freedom.

The next time you think creativity is magic, remember it's mathematics at work. And maybe the real equation is this: Creativity = attempts × combinations × time × chaos-order. https://youtu.be/6aohcF4XBSc?si=kRr03UxFfizmAeeU