Does it shock you knowing that virtually ALL of our thinking is based on the past? Do you really think business schools heavily rely on forecasting? Most, teach how business was done in the past. Hence;
A great deal of our business thinking stems from Frederick Taylor’s Scientific Management.
Modern management thinking is also infused with the U.S. 1920’s Automobile Industry with Ford and General Motors.
Wherever these practices stemmed from, rest assured it was in an era long gone. Market conditions have changed in the last century. Nonetheless,
They remain so pervasive that they are essentially invisible. It is simply how things are done.
The most striking is our “17th century thinking.”
How is our understanding of business based on 17th century logic?
Much of modern business thinking is still rooted in 17th-century logic, particularly the rationalist and empirical frameworks that emerged during the Scientific Revolution. Here is how:
1. Cartesian Rationalism (René Descartes, 17th Century)
- Reductionism: Businesses often break down problems into smaller parts to analyze them separately, just as Descartes suggested in his Discourse on Method. This approach dominates management science and business analytics.
- Mechanistic Thinking: Many businesses operate under a cause-and-effect, machine-like model of efficiency, where humans are seen as interchangeable parts rather than complex, adaptive beings.
2. Empiricism & Capitalism (John Locke & Adam Smith, 17th-18th Century)
- Data-Driven Decision Making: The rise of empirical science (observation and experimentation) in the 17th century laid the foundation for modern business analytics, market research, and evidence-based strategy.
- Free Market Ideology: Early economic theories like those of Smith (influenced by 17th-century thinkers) framed business as a self-regulating system guided by rational self-interest—principles still central to economic thought today.
3. Newtonian Thinking in Business
- Predictability & Control: Businesses often assume that markets, organizations, and productivity can be measured and controlled like physical systems, similar to how Newton described the laws of motion.
- Linear Problem-Solving: Many strategies assume straightforward, linear progressions, ignoring complex, adaptive dynamics.
Is This a Problem?
Yes, because 21st-century businesses operate in complex, interconnected, and adaptive systems, not just predictable mechanical ones.
Deming’s philosophy moves beyond 17th-century thinking by emphasizing systems theory, variation, learning, and continual improvement – more aligned with modern complexity science than rigid rationalist models.
Management must adopt one or the other as a guiding principle:
Mere conformance to specifications or continual process improvement.
Management has been trying the first alternative since the beginning of the industrial revolution. After almost 200 years, the goal has not been met. The legacy of focusing solely upon conformance specifications has been a lack of progress. There is no reason to believe it will be different in the future.
Determinism:
Determinism and Newtonian physics versus randomness and uncertainty (focus on immediate problem rather than systematic chain reaction, seeing the situation in its greater context)
Misidentifying variation is the root of all evil: All processes have variation. Patterns of variation can reveal insights into future defects. SPC allows you to statistically predict defects before they occur. According to SPC, managers should not waste their time trying to fix every single problem, instead, they should identify which ones can be predicted and fix them. Identify the ones that will likely never happen again, and avoid the knee-jerk decisions. As a result, managers can spend their time on things they can control and waste very little of their time on things they cannot control.
Determinism versus pragmatism: A story
In Clarence Irving Lewis’s book Mind and the World Order
He outlines two key types of knowledge:
- A priori knowledge (knowledge independent of experience)
- A posteriori knowledge (knowledge derived from experience).
Before the fact and after the fact. One theorizes, the other experiments and observes.
This was rooted in the thinking of Aristotle. And Descartes: “I think therefore I am.”
A priori thinkers believe they can reason their way out of anything inside their own heads.
A posteriori thinkers (pragmatists) believe they can reason their way through something only by doing it. They believe experience is the best teacher.
The story of accuracy and standardization is really a story of pragmatism. At any point in time, a standard is a measure that suffices, and that everyone agrees upon.
Ex: Measurement of Pi. Pi (π) is a mathematical constant representing the ratio of a circle’s circumference to its diameter, approximately equal to 3.14159… This is an irrational number, meaning it cannot be expressed as a simple fraction. When we do the math, we quickly discover the division just keeps going and going. Our teachers tell us to round to two decimal places, to simply use 3.14.
A pragmatist says 3.14 is good enough for what we are doing here. It does not make sense to find its absolute value. We have too many other things to do here.
The determinist would spend his life calculating all the digits in π.
The pragmatist would say I cannot spend my life doing a math problem. I have real problems to solve. Two decimal places is close enough.
While this simple example sounds like common sense. Most people are still stuck in the absolute deterministic approach, believing that perfection can and should be reached.
Pragmatists begin with observations and empirical data, in other words, hard evidence, then work their way backward. This might sound simple, but it’s actually quite rare for people to think like this.
Most rely on common knowledge, knee jerk reactions and going along with the way we’ve always done it around here.
In Taylor’s and Ford’s time, quality inspection was essentially a question of whether a product was close enough to perfect to pass. However,
Shewhart, bringing a pragmatic outlook, asked Why wasn’t it perfect? In so many words he said perfection is an illusion. We will never reach it. Yet, if we use a posteriori thinking, we can systematically improve how we manage and how we manufacture (the issue at in Shewhart’s time).
Pragmatism was the basis for the heart cycle of continuous improvement, what would come to be known as Deming’s PDSA: Plan, Do, Study, Act.
A modern example of the perfection mentality:
There was a time when the owners and managers of banks, insurance companies and major retailers, wanted their websites to have 100% uptime. They didn’t want their websites to be down, ever. That may have been the goal, but the reality was too chaotic to adhere to corporate policy.
If they could not have 100%, how about 99% or 99.9%? Could the IT guys get up to 99.99%? Of course 99.999 would be even better. Just how many extra nines could IT department get to? This went on for years and still goes on today. (Six Sigma)
Then Google published a book called Site Reliability Engineering. It shed light on how Google had grown by gathering data from the exponential explosion in the number of websites in the world. Back when Yahoo launched they were about 3000 sites. Right now that number is in the 10 figures. How did Google management its systems to collect all that data? They realized that absolute reliability was impossible and abandoned the goal of 100% uptime. Instead, they asked themselves 2 simple questions.
- How much more money would make with an extra 9?
- Is adding an extra 9 cost more than we would make?
Because Google makes money primarily from advertising, it could calculate how much money it lost during the web outage, down to a fraction of a cent. As such, they could calculate the company’s pragmatic limit. For the sake of simplicity, let’s say Google makes $10 million a month from selling ads. That’s roughly $240 a minute. For this example. Let’s further say they have 99.9% reliability, meaning the website is down for only 43 minutes a month. That means they lose $10,300 a month. A new manager takes over. He says this is not good enough. He needs to brag to his golfing buddies that his systems have 99.99% reliability. That would equal only 4.3 minutes of downtime a month. So, much that extra 9 cost? If going from 99.9% to 99.99% up time cost only $10,000 a month, they would net an extra $300 a month. In this case we would probably invest the time resources. However, if the cost of that extra 9 was $100,000 a month, they would lose almost $90,000 a month. In this scenario, Google would not pursue 99.9% reliability. It is simply not economical to make the website more reliable than it already is.
Creating a perfect operating process is a never-ending process.
You have to find the pragmatic limits.
(ZERO carbon emissions)
A “show me” state of mind:
Again, two types of knowledge:
- A priori drawing your conclusions beforehand
- A postiori: Drawing conclusions after the fact.
Pragmatists dismiss a priori thinkers. In their view, you cannot know anything without first starting with evidence. This ties in neatly with nondeterminism. You cannot be certain the apple will hit the ground until you drop it and it hits the ground. Pragmatists would not assume Schrodinger’s cat is dead or alive. They would simply open the box.
Let us look at another instance of a priori thinking with a typical survey in DevOps:
An annual survey might send out multiple choice questions such as: “On average, how often do you deliver software?
The choices might be once a year, once a month, once a week, once a day, once an hour.
Before getting answers back, the analysts behind the survey have already concluded that organization delivering once an hour or a day should be categorized as high performers. Those delivering once a week or a month, are medium performers. Those delivering once a year are low performers.
The analysts draw their conclusions even before collecting the data. That’s a priori thinking.
(This is alive and well in 2025 with the example of Meta following the POTUS and Jack Welch philosophy…)
However, the reason a low performing team might deploy only once a year could be because that’s when their space probe is in the line of sight. This does not necessarily make them low performing. You cannot assume you know answers ahead of time.
A pragmatist on the other hand, might ask the same question: “On average, how often do you deliver software?” However,
Instead of predetermining the responses, they might engage in a question and answer type of feedback. Example:
Respondent #1: Every day, but it’s hard to do on Mondays and Fridays.
Analyst: Why is it hard on those days?
Respondent #2: Whenever it’s ready to be shipped.
Analyst: Do you need approval to deliver it?
Respondent #3: Only when my manager tells me I can.
Analyst: When is that?
The pragmatic analyst would collect the data AND THEN draw conclusions about which software teams were high, medium, or low performance, that is, posterior or after the fact.
When Shewhart introduced Deming to the philosophy of pragmatism in 1927, the young protégé was amply prepared to receive it.
Nondeterminism had shown Ed that what everyone knew and accepted was either more nuanced or simply wrong. The only reliable source of knowledge came from empirical evidence. Pragmatism simply sense. Ever thereafter, what Ed (Deming) taught never came from traditional or hearsay, but from hard facts and careful study.
Shewhart used the philosophy of pragmatism to completely rethink manufacturing. Most know the result of this today as the PDSA cycle: Plan, Do, Study, Act. What Ed called the Shewhart cycle. It has become the template to improve virtually every type of system or process.
- First, gather evidence to create a hypothesis. What needs to change?
- Second, make the change.
- Third, review what happened. Is the process better or worse? Why?
- Last, decide where to go from here. Revert to begore? Iterate further?
By continually gathering data, you can continually improve your process.
The traditional alternative: “If it ain’t broke, don’t fix it.” Consistency. Good enough. Quantity. The home run. Make money. Management by objective…
Management must adopt one or the other as a guiding principle:
- Mere conformance to specifications (consistency), or
- Continual process improvement
Management has been trying the first alternative since the beginning of the industrial revolution. After almost 200 years, the goal has not been met. The legacy of focusing solely upon conformance specifications has been a lack of progress. There is no reason to believe it will be different in the future…
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