7 Little-Known Thinking Models to Tackle Any Business Challenge

7 Little-Known Thinking Models to Tackle Any Business Challenge

Learn the 7 universal thinking models to face any of your business challenges.

7 Little-Known Thinking Models to Tackle Any Business Challenge
Image by zentro from Depositphotos.

“Thinking Models,” not to be confused with “mental models,” are typically adopted to solve real-world business problems. In my line of work, when solving business problems, I never found much use for “mental models.” They are typically used to represent how something works. On the other hand, “thinking models” help immensely in solving problems.

As a strategy consultant, I help companies develop and launch new products and services that dominate their niches. I experimented with many tools, from “design thinking” to startup acceleration programs, and while some were reasonably successful, the outcome was inconsistent. It’s these thinking models that helped me improve my success rate from the low teens to 85%.

Over the years, I’ve gathered seven universal heuristic thinking models that I have found to be most effective at tackling almost every problem I’ve encountered in the course of my work. I have included these models in our internal induction decks to help new recruits hit the ground running.

In this article, I outline how these models work and how they can be used to solve any business problem or challenge that entrepreneurs, business owners, business leaders, and business consultants might encounter in their day-to-day work.

Where possible, I will provide a rule of thumb as a simple and straightforward method to memorize and apply them to the problem at hand.

“If I have an hour to solve a problem, and my life depended on it, I will spend the first 55 minutes determining the proper questions to ask, for once I know the questions, I could solve the problem in less than 5 minutes” — Albert Einstein.

Many books and disciplines cover the topic of Strategic Thinking. However, here’s a rule of thumb to help narrow the thinking down to a single sentence: “Ask the right questions.”

It’s not important if Einstein said these words or not, but the idea holds because Strategic Thinking is all about asking the right questions. When we recruit strategy consultants from top MBA schools, we screen for candidates who demonstrate an ability to think strategically. We do so by checking if they can come up with the right set of questions when solving problems in case interviews. In consulting, we interchangeably use this term with analytical thinking, and it’s how we gauge the suitability of prospective candidates first and foremost. If a candidate fails on a case by one of the interviewers, they’re automatically rejected.

As you’ll discover next, asking the right questions is essential to every step of problem-solving.

“The propensity to excessive simplification is indeed natural to the mind of man since it is only by abstraction and generalization that he can stretch his puny faculties to embrace a minute portion of the illimitable vastness of the universe” — James Goerge Frazer, The Magic Art and the Evolution of Kings.

Abstraction thinking, not to be confused with “abstract thinking,” may sound abstract but really isn’t. Abstraction thinking helps the mind establish a mentally comprehensible hierarchy for complex problems.

I first learned about abstraction during my early years as an engineer, where it’s frequently applied in hierarchical representations of complex systems. For example, machine code or firmware is a layer that is several hierarchical layers below the application software and the algorithm. Other examples include:

  • Construction: the blueprint representation of a building is one level of abstraction below the actual building and is designed to capture the essential features.
  • Music: the layer of a symphony would be the notes, and another layer would be the instruments and orchestra.

The idea is that every layer can exist without the layers above it but requires the layers below it to function.

Abstraction is particularly useful in complex or nonlinear systems. As I’ll demonstrate later, solving real-world business problems requires the ability to hone in on the relevant data to support a hypothesis. Doing so efficiently is a big challenge in today’s highly complex world where we’re swamped with data.

When we consider data, we may be talking about billions of data points. To a person’s mental capacity, a number like a billion is a highly abstract thing as most humans can mentally process double- or triple-digit numbers. Abstraction helps the human mind make sense of abstract concepts by removing successive levels of detail from a representation to capture only the relevant features of a system. It reduces a system’s complexity to focus only on what is essential to a single issue or sub-issue.

“Boundaries are about providing structure, and structure is essential in building anything that thrives.” Henry Cloud, Boundaries

One of the most valuable skills I learned during my early years as a management consultant is structured thinking. The importance of structured thinking may not be as apparent, but every aspect of work and life revolves around being able to effectively structure thoughts, plans, data, etc. This kind of thinking requires quite a bit of experience to master. However, there are several heuristics and techniques that people can apply to advance their structured thinking abilities.

One such tool is the issue diagram, also referred to as the hypothesis-driven approach. In the issue diagram, a hypothesis is formulated based on the issues and sub-issues from the strategic objectives derived from the main problem.

The entire thinking process would be led by issues and hypotheses, followed by asking the right questions that help prove the hypothesis in a tree structure (shown below). As a final step in the process, data analysis would be guided by the answers to the questions designed to prove the hypotheses.

Image By Sam Schreim from bmh.ai

Guessing the hypotheses before conducting the analysis may appear challenging, but it isn’t. The idea is to make a series of good guesses at hypotheses, even when they may (later) turn out to be wrong.

Accordingly, the issue diagram is referred to as the hypothesis-driven approach because hypotheses are what guide the problem-solving analysis. This also helps in testing feasibility and realism.

As a rule of thumb, to validate whether the analysis proves and/or disproves hypotheses, simply check the outcome of the analysis by asking: So What?

If you feel compelled to dive deeper into the subject, you can read about it in Barbara Minto’s international best-selling book The Pyramid Principle. It is similar to the issue diagram but dives much deeper into the concept. The principle was McKinsey’s initial attempt to formalize training for fresh MBA graduates, which Minto later popularized in her book.

“The secret to happiness is to put the burden of proof on unhappiness” — Robert Brault.

Critical thinking is nothing other than what we were taught in high school, a topic I’ve learned to love, but many of my schoolmates hated it. Nevertheless, critical thinking is an essential part of the problem-solving chain of thinking.

In this context, the basic idea is to compare it to a court of law. For each claim, act as the prosecution would. Prosecutors have the “burden of proving” their allegation beyond a reasonable doubt.

As such, the hypothesis would be similar to the allegation, while the data and analysis would be similar to the evidence in court.

As a rule of thumb, it is much easier to disprove than to prove a claim. So when analysis is complete, take a step back and try to disprove it. All it takes is one counter-example to show that the hypothesis does not hold true.

“Fermi estimation can cut through bullsh*t like a hot knife through butter” — David Epstein, Range: Why Generalists Triumph in a Specialized World.

Fresh out of college, I recall my first interview question: “How many aircraft tugs are in the world?”

I didn’t know at the time as I thought to myself, what in the world would such a question serve? It wasn’t until I started working that I realized the usefulness of what’s commonly referred to as Fermi Thinking.

Image by an unknown user from Picryl.

Fermi thinking is a heuristic estimation technique named after the physicist Enrico Fermi. Fermi was known for his ability to make good approximate calculations with little data. He became known for his estimate of the potency of the atomic bomb that detonated at the Manhattan Project in 1945. Fermi’s estimate of 10 kilotons of TNT is based on the distance traveled by pieces of paper he dropped from his hand during the blast.

Fermi problems are often extreme and cannot be solved mathematically or scientifically. If you’ve heard of Fermi, you’re probably familiar with the Fermi Paradox and Drake Equation, which estimates the number of intelligent civilizations in the galaxy. But in this context, Fermi thinking is applied to problems that require estimations.

While Fermi estimates are almost always inaccurate, they provide approximations for quick error checking and identifying faulty assumptions.

Fermi thinking usefulness extends to various dimensions; here are a few examples of how Fermi Thinking can be leveraged.

  • Validating a claim or a hypothesis before diving into data analysis
  • Estimating and/or validating potential faulty estimates or data sources
  • Assessing market opportunities and identifying white spaces
  • Conducting “quick and dirty” back-of-the-envelope estimations

“Systems thinking is a discipline of seeing whole” — Peter M. Senge

Systems thinking is a vast subject, but in this context, the idea is to determine if the problem at hand is in a linear or nonlinear system.

In a linear system, problems can usually be isolated by, for example, examining and identifying the weakest link in a linear chain. Therefore, they can be easily modeled, understood, and diagnosed.

In contrast, a nonlinear or complex system has many interdependent components and often interact with one another. This makes it difficult to model because these interactions make it impossible to separate the parts from the whole. Therefore, a holistic approach is a must-have for problem-solving. Examples of nonlinear complex systems include supply chains, organization structures, team interactions, development projects, etc.

The way to overcome this challenge is by navigating problems in complex systems by modeling such systems in abstractions.

As a rule of thumb, for systems thinking, always distinguish a linear from a nonlinear system before conducting an analysis.

“Emergence of ever more complex structures seems to be programmed into the nature of our evolving cosmos.” ― Alex M. Vikoulov, The Syntellect Hypothesis: Five Paradigms of the Mind’s Evolution.

Image by an unknown user from Wikimedia Commons.

Why do birds flock and fish school? There are several reasons and hypotheses. First, it makes sense to believe that predators will assume that the flock or the school is a single large organism that is possibly threatening, which will keep predators from attacking. It’s also evident that predators find it much more challenging to target individuals in a flock or a school than target single animals. The other way around is also possible: The flocks or schools are more effective at catching prey by cooperative hunting than individuals on their own.

Image by an unknown user from Wikimedia Commons.

The term “Emergence” has its roots in nature and refers to these complex formations, which are properties of self-organization in complex systems. The idea is simple: “simple rules lead to complex results.”

In the 1980s, Craig Reynolds presented a very simple model of this phenomenon, known as Boid’s model. His goal was to develop realistic computer graphics of flocking or schooling behavior. As a result, he wrote a well-known paper titled: Flocks, herds, and schools: A distributed behavioral model. In that paper, he provides a simple model in which individuals obey three rules in order of importance.

  • The first rule is collision avoidance, which has only one purpose — to avoid collisions with their neighbors.
  • The second rule is velocity matching, which ensures that individuals are synchronized in speed and direction by matching their velocity to their neighbors.
  • The third and final rule is flock centering, a proximity rule to ensure that individuals remain close to their neighbors.

It’s counter-intuitive to think that this set of simple rules can create these complex systems without any central command. These complex formations are made entirely possible by having every individual stick to the three simple rules.

Researchers at the Max Planck Institute at the University of Konstanz that studied the school of fish, concluded that being ignorant and uninformed can be a very positive component to the resilience and integrity of the school and the survival of the group. They determined that having uninformed individuals participate in decision-making ends up democratizing group decision-making and prevents extremist individuals from having a disproportionate influence overall.

For example, when leading teams or organizations, collective intelligence can be achieved through decentralization and individualism. Such rules minimize the trade-offs between influence and collective intelligence without killing the momentum of creative problem-solving and, with it, the emergence of co-creation, cooperation, and equal evaluation of ideas.

Moreover, it is the ability to tackle an increasingly complex world by identifying a set of simple rules and adopting them as principles and rules of thumb.

And in the context of problem-solving, more optimized solutions can emerge by applying the 6 thinking models + Emergence Thinking as rules when tackling problems.

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Entrepreneur&#39;s Handbook – Medium

Author: Sam Schreim