Solve Problems Really Fast

This post is influenced the book Bulletproof Problem Solving, written by two McKinseyites, Charles Conn, and Robert McLean. I recommend picking it up if you want a deep dive into problem solving frameworks.

Problem disaggregation

“Problem disaggregation” can be summarised as: breaking down a problem into logical components so that it can be understood.

Problem solving is effectively understanding a situation well enough so it can be resolved. It’s often said that a well understood problem is already half solved.

To demonstrate the importance of understanding a problem before trying to solve it, Abraham Lincoln famously said “Give me six hours to chop down a tree and I will spend the first four sharpening the axe.”

In this sense, problem disaggregation is a tool that exposes the different parts of a problem, making it easier to understand, prioritise, and work with.

How to disaggregate any problem

The best way to learn the disaggregation process is by example, so let's look at a simple example followed by a more complex one.

Example 1: Simple problem


You need to get from your hotel to the airport with all your luggage in time for a flight home.


You're in a country you've never visited before, and you don't speak the language. You know there are several transport options available, each with tradeoffs.


  • Transport options: Uber, Taxi, Coach.

  • Time: you've got 3hrs until boarding.

  • Luggage: one large suitcase and a small bag.

  • Assistance: one helpful member of hotel staff speaks English.

Confidence values

With the components identified, they can be compared to give a confidence rating for each transport option.

The formula to find the confidence value for each transport option

Confidence is subjective but it's still measurable. By weighting each variable based on its subjective importance, the different components can be independently measured.

Exploring which transport option is safest based on weighted variables (raw data)

Example 2: Complex problem

This example is borrowed from Bulletproof Problem Solving.


A family is moving home. Where should they live?

Components (weighted)

  • Proximity to good schools [0.35]

    • …2x sub variables

  • Nice environment [0.25]

    • …3x sub variables

  • A cool, friendly town [0.25]

    • …6x sub variables

  • Not too far from everything [0.15]

    • …1 x sub variable

Weighted score

Exploring which location is best based on weighted variables (raw data)

Further use cases

I hope you've found learning about disaggregation theory as revealing as I did when I first stumbled into it.

The disaggregation framework is a real Swiss Army knife. It's useful across domains, and with problems of different complexities. It can be applied to business questions like "should we market to segment X or Y," or "which product idea higher risk".

If you want to learn more about framing the problem well, read my previous post which explains how to define problem statements.

Catch you soon,
– Jason

PS – You can explore the data used in this post in this Google sheet!