Scientific Revolution, Disruptive Innovation and Theory of Constraints

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“Innovation”, “Disruption” have been buzzwords for a while. Pundits remind us how Nokia, Kodak didn’t act on time and got disrupted into oblivion. These stories are scary enough to whip up board rooms into frenzied action. Top managements of large companies now seek to “act” and do something “proactively” before they become similar case studies.

Hence, today, a ‘digital strategy’ formulated by a reputed management-consulting firm is a must for many large companies. Interestingly, none of the disruptive technologies of recent years can be traced back to management consulting firms. As The Economist remarks, the new lords of business in the Internet economy are engineers in hoodies, not MBAs in pinstripes.

At same time, companies cannot depend on in-house talent to make sense of the overwhelming tech fads around us. Going out seems to be the only way.

However, organizations need to be cautious because innovations also have a dubious history. It is true that major disruptions in industries are always because of a radical innovation. Not all innovations, however, lead to disruption. In fact, most innovations die out without creating any impact. Statistically speaking, the probability of an innovation failing is extremely high.

This implies that it is not really the expertise of generating ideas, which is the problem. It is the thinking algorithm behind those ideas, which needs closer examination. Such an investigation would require us to not just visit the successes but also study the graveyard of failed innovations.

Most failed innovations die in oblivion and become dark secrets of companies; they don’t occupy much space in public historical narratives. Only successful ones are publicly well researched, documented and celebrated. One cannot learn much from the few successes while ignoring the vast majority of failed innovations.

Perhaps, the best place to investigate the thinking behind radical innovations is the history of hard sciences. Unlike other fields of knowledge, hard sciences have vast archives of failed theories – corpuscular theory of optics, geocentric model of universe, concept of phlogiston, amongst many others. The documentation of failed theories in hard sciences also captures the assumptions and the logical narrative behind them. At the same time, the revolutionary new theories are well documented; their disruptive contribution in terms of new ways of experimentation, instrumentation, research and applications have been well highlighted. The historical narratives of failures and successes allow us to understand the thinking that led to innovations.

Learning from Hard Sciences

Breakthrough knowledge in hard sciences is created when a genius recognizes an anomaly with current theory – an unresolved problem, which most fellow scientists have dismissed as an experimental error or just ignored its presence. The obsession forces the genius to spend significant time to find an explanation for the aberration.

After burning copious amounts of midnight oil, he or she conjectures a logical explanation, beyond the confines of current theory. Once this conjecture is verbalized, it is subject to rigorous criticism by peers. At this stage, many new theories drop out and finally the last round of testing is carried out.

Problem Recognition and Conjecture -> Criticism -> Testing is the accepted process of knowledge creation in hard sciences.

At every stage, a widely accepted, rigorous method is followed.

1. Phase of Problem Recognition and Conjecture

On the face of it, conjecturing a solution for a problem seems easy. In fact, we all conjecture every other moment. Human beings are “meaning-making” machines. We continuously seek to explain phenomena around us, and hence conjecturing comes naturally to us.

Does it mean all of us are following the first step of the scientific process?

In the world of science, the process of recognition and conjecture is bound by rigor. A story from the field of science will reveal why this is not easy!

At the age of 16, Einstein had a thought– if he can pursue a ray of light at the same speed as light itself, he should see light frozen at one place. But it was not what the prevailing equations predicted. It was the starting point of a conflict which troubled him enough, till he found a way to explain the contradiction with relativity.

Without an underlying conflict or contradiction, a problem would not persist for long. A problem is well understood when the underlying contradiction or conflict is well verbalized. Hence, a scientific conjecture is an idea, which always tries to solve a problem by “breaking” the underlying contradiction or a conflict.

The ability to see a problem as a conflict or a contradiction between two seemingly opposing realities is also a starting point of major innovations in the field of engineering and technology. Hence, conjecturing or ideating out of a conflict is not just limited to theoretical hard sciences; this can be seen in other breakthrough innovations around us.

A mind obsessed with resolution of the identified conflict, without any compromises, is always alert to cues from seemingly unrelated areas. History of engineering innovations is full of such examples. For example, the thunderclap a bullet train created while emerging from a tunnel bothered one Japanese engineer enough to devise a way to silence the thunderclap. His inspiration came while observing a kingfisher diving into water without a splash.

Scientists and engineers are attracted to chronic problems because these unresolved conflicts present opportunities to invent something powerful. When they hit upon an idea, the excitement can fire them to jump out of bathtubs, naked, shouting “eureka”!

2. Phase of Criticism

Conjectures cannot straight away be put to the testing phase because testing can be extremely expensive and at times, non-conclusive.

A cost effective filtering mechanism is needed to kill bad ideas. One such mechanism is formulating logical contra-argument, also known as thought experiments. At this stage, for theories to be critique-worthy, causal explanations (the logical narrative between conjecture and resolution of the identified problem or a phenomenon) need to meet the following criteria

i. Non Variability
ii. No Logical Substitutes
iii. No Logical Gaps

i. Non Variability

Scientific explanations need to stay rigid and non-variable, post facto. This is an important criterion because if explanations change arbitrarily to “accommodate” contradicting observations, then no conclusive, logical arguments can happen.

Let us take the example of a mythical hypothesis that an angry God brought floods to a particular area. A counter argument could be, ‘why was the flood restricted to the specific area?’ As an answer to this question, proponents of such myths might come with a new creative explanation; the theory of “cause leading to effect” will not be discarded. You can never close the argument.

The post-facto variability in logical arguments makes such subjects “non-critiqueable”. Hence ideas which don’t meet this criteria are also not conducive for experimentation.

ii. No Logical Substitutes

It is common practice, more so in social sciences, to quote studies to back up claims, even though there is no uniform agreement or even a detailed logic.

When we read a sentence starting with “as per a study by the University of” most of us make up our mind that it is science. But the fact is, a respectable authority’s endorsement is not enough to give full credence to a topic.

Joel Best, professor of sociology in university of Delaware, very well highlights the problem of “studies” in social sciences, in his book aptly named “Damned Lies and Statistics”. As he says, in hard sciences, a word like “Atomic Weight” is precisely defined using a universally accepted standard of measurement. But, in social sciences, words like “lunacy”, “sexual harassment”, or even “inventory” are open to interpretation. The definition of a variable as “too broad” or “too specific” is left to individuals doing a specific study. At the same time, there are no accepted standards for measurement methods. So, varying results from studies on the same topic often “opens a can of worms” among researchers.

Plethora of topics, associated words, and lack of standard definitions and measurement methods can make studies vulnerable to errors and biases.
Hence, a back up study is not enough to kill a “bad” conjecture; a topic has to be conducive to logical argument, so that it can be filtered out, at the phase of criticism.

iii. No Logical Gaps

Criticism requires detailed logical connections from the fundamentals of the topic. One cannot use analogies from other subjects to conclude arguments on a topic. Analogies are often used to give credence to proponents of mythical knowledge. Explanations made using analogies, though impressive, usually contain logical gaps. For example, some astrologers, to explain the impact of stars and planets on human beings, refer to the subject of gravity from physics.

Such an explanation does not satisfactorily say how a force from a distant object alters human biology, and ultimately, the fate of individuals. The logical gaps are glaring.

Let us take a case of the “scientific” explanation put forth on the impact of the full moon on human behavior. One of the arguments goes thus: “If the moon can cause ocean surfaces to bulge, leading to tides, just imagine what it will do to the water which constitutes 70% of our body”. This explanation is easily challenged. Calculations show that the impact of the moon’s gravity on the human body is extremely negligible. A mother holding a baby would exert much higher gravitational force on the baby than the far away moon! Even if we accept the lunar force to be significant, we still need more details on how the resultant ‘tiny’ tidal waves of water inside our body can cause chemical or biological imbalances; how can these lead to mood swings, while our other daily chores (which involve much greater interaction with water) have no impact? It is very easy to see that all myths, which endeavor to borrow partial ideas from science, are demonstrably incomplete.

A subject is “critiqueable” only when it meets the above three criteria (of non-variability, no logical substitutes and no logical gaps); and only then can it be amenable for the important and a very cost effective filter of dropping bad ideas.

Not everything needs to be or can be immediately tested. However, if the conjecture survives the onslaught of criticism, then comes the last phase of actually testing – the final check on validity.

3. Phase of Testing

Tests, as opposed to a study, help directly establish the relationship between “cause” and “effect”, as prophesied by the hypothesis, in an environment where other variables are reasonably isolated to avoid noise.

The real insight behind the three-step method is that it completely discards the past paradigms and experiences to create a new ones.

Science and Management

The above three-step method is not followed widely in the field of management. Here, experience driven heuristics often disguised as “best practices” hold sway. Most companies want to benchmark and copy these best practices of the industry. People, with “experience”, those who have seen it all, many times over and have acquired a quiver full of heuristics, are highly respected. This is one of the reasons why companies prefer to hire managers from the same industry. It is assumed that “rules of thumb” that have evolved from handling similar issues in the past offer the best methods for solving current problems.

Emulating these “best practices” do help bring in incremental improvements or solve some common irritants (like ideas on better packing material, cheaper transportation modes, cheaper supply source, value engineering ideas, product features, organization structuring ideas etc.) Unfortunately, relying solely on past experiences has also led to the acceptance of many difficult and chronic problems as given, non-changeable “facts” of life.
The list of chronic industry problems that have persisted over decades is long. A few examples:
• Large infrastructure projects will always face delay.
• Month-end skew in production and dispatches are inevitable for consumer goods industry.
• Generic pharma companies will have a year-end skew of filings to FDA.
• Trader scheme has to be offered in consumer goods industry, even if it is the prime reason for the repetitive and devastating bullwhip effect.
• Software will have bugs and developers will be overworked close to releases.
• Employees will always be unhappy with any performance appraisal system.

Breakthrough innovations that solve such issues are a rarity in most organizations. This is because most are designed to reject the scientific three-step process of conjecture, criticism and testing.

Conjectures in Management

Any conjecture created in the mind factory with logic, and not data, is frowned upon, and is likely to be instantly rejected. Try saying, “I am just making a logical guess” in a meeting, and you would be rejected for having committed the ultimate corporate sin! Instead, if you say, “I have a few examples and evidence to back my idea” while indulging in wild conjecture (supported by anecdotal evidence or even bad data) and you may be hailed as “brilliant” without question.

Criticism in Management

The culture of criticism, so vital for the three-step process, is also vastly absent in companies with rigid hierarchies. Criticism is likely to lead to one person being proven wrong, which can be career damaging either to the critic or the critiqued, depending on the power structure of the company.

Testing in Management

Most companies account for negative variances by apportioning blame on departments. They avoid doing a practical test, which has non-negligible chances of failures. Though exceptions exist, most companies are culturally oriented to discard the scientific process of Conjecture, Criticism and Testing. And without the scientific approach, it is impossible to create any major breakthrough. Industry always stays busy managing the problems of status quo, which entails heuristics and benchmarking. This is one of the reasons why industries don’t disrupt themselves, outsiders do it.

Whenever radical disruptions came about in business history, it has always been with “outside” thinking. For instance, Taichi Ohno found inspiration in the way super markets operate to conjecture the concept of Kanban for the auto industry when it was faced with the conceptual problem of devising a pull system for wide-variety environment. Similarly, Apple, an industry outsider, redefined manufacturing and design paradigms to revolutionize, and finally, dominate the category of smart phones.

Outsiders have a “fresh” view which tends to be difficult for those weighed down by popular heuristics or experience of the industry. This doesn’t mean that people who have more “experience” of a domain cannot innovate. They can. It is possible if they get out of their “default” mode of experience-backed heuristic thinking and apply the three-step scientific approach.

TOC Thinking Tools – a guide for innovative thinking

The thinking processes of Theory of Constraints are a practical way of applying the three-step process to management and avoid the typical experiential biases.

Problem Definition and Conjecture

The starting point of many inconsequential innovations in organizations is a fascination for a tool or technology. Then follows the process of ideation on how to use the technology in a specific environment and finally assess the potential benefits. This tool-based thinking approach leads to ideas like using chat bots for customer service. The benefit of manpower cost reduction becomes the promise. However, the fact that in most cases they are not as effective as human beings makes the idea disruptive, only for customer experience.

It is very critical to focus on a chronic unresolved problem of customers, as a starting point of thinking, to make a breakthrough innovation. When the problem is unresolved for ages, it implies that no one else has done it –it can be a source for a competitive advantage, if solved. These are problems, which even the customer will not voice, as he is accustomed to it.

However, picking up minor symptomatic problems, based on voice of customer, for resolution, won’t lead to a major breakthrough. For example, ideas like auto-replenishing, by taking signals of stock directly from a fridge of a customer, do not really solve a major problem for the customer. It is no wonder that the idea did not take off, despite the hype and investments around it in the late ‘90s.

One needs a well-defined thinking template to do a proper systemic analysis and identify a chronic, yet significant problem. Problem definition tools of TOC enable users to clearly differentiate between core problems and minor symptomatic issues.

Chronic fundamental problems also present themselves as severe conflicts or contradictions – any current ideas of dealing with them only leads to another problem. Hence a breakthrough approach requires one to break the conflict without compromises. The thinking tools of TOC help verbalize the conflict or the contradiction and the underlying assumptions. This approach forces users to challenge the very conflict and not look for optimization solutions.


The tools of solution detailing forces users to specify boundary conditions and the detailed logical narratives of the solutions so that the proposed solutions are open to criticism and modification if reality differs from the stated assumptions.


The thinking tool for designing implementation plan focuses on sequencing solutions in terms of small yet significant steps. This allows one to develop a robust implementation plan while containing the risks at every stage. This approach provides the courage to try out new ideas without the fear of big failures.

These thinking tools provide the necessary “thought-ware” for creating breakthrough innovations.

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