A central tenet of structuring your problem solving is your considering all the possible answers to your question exactly once; that is, how you organize these answers must be mutually exclusive and collectively exhaustive (sometimes written as “mutually exclusive and completely exhaustive”)—or MECE (pronounced “mee see”).
MECE thinking is popular with strategy consultancies, including the McKinsey, Bain, and BCG of the world (see, for instance, Davis et al. and Kazancioglu et al., referenced below). In fact, the case interview that these companies use to filter their applicants require you to think in a MECE way. It is understandable: Striving to make your thinking MECE usually results in you being more clear and more creative. So, let’s look at what it means and how you can become an effective MECE thinker.
“Mutually exclusive” means “no overlaps”
“Mutually exclusive”—ME—means “no overlaps.” At the crossroad, you can go straight or turn left but, at any one time, you cannot do both: Doing one precludes you from doing the other, so the two options don’t overlap (they are disjoint).
Two (or more) sets of answers are mutually exclusive when they don’t intersect: You cannot have an answer belonging to both sets at the same time.
When you are mutually exclusive in your thinking, you never consider an answer more than once, hereby ensuring that you do not duplicate efforts. Imagine you’re driving down a road and get to an intersection. Here, you can go straight or turn left, but you can’t do both.
Mutually exclusive thinking forces you to consider the details, seeing the individual tree as opposed to the forest. It helps you ensure that each element differs from the others.
So if your key question is “How can I go from New York City to London?” and you organize means of transportation by dividing them between “by flying” and “by traveling by sea,” you are organizing the possible solutions to your problem in a mutually exclusive groups (since if you’re flying you are not traveling by sea at the same time).
“Collectively exhaustive” means “no gap”
Groups of answers are collectively exhaustive when, among themselves, they include all the possible answers to your question.
Collectively exhaustive thinking means that you do not forget possible solutions; that is, you’re being innovative, considering even “dumb” ideas.
Thinking in a collectively exhaustive fashion when you’re considering ways of going from NYC to London means that you might consider way other than air- and sea. Maybe by bringing London to you? Maybe by projecting yourself in London? What else can you think of?
So, whenever you’re facing a new problem, actively look for a MECE way to categorize its root causes or its solutions, and ensure that all your question maps are MECE.
In day-to-day life, you can also train yourself to be better at thinking in a mutually exclusive and collectively exhaustive way: Each time you’re looking at a series of items, ask yourself if they are MECE. Whenever you see or hear a list of things—listening to the latest tirade of your favorite politician or the verbose argument of a close friend—ask yourself if it is indeed MECE. Become obsessive about it. If you start waking up in the middle of the night yelling “This is not MECE!”, then you’re on …
Use help wherever you can
Sometimes, you’ll be lucky enough to have existing frameworks that you can use to decompose your problem in MECE parts. If one of these is insightful enough for your situation, use it.
If you’re not that lucky, you’ll have to develop your own MECE set. That can be tedious and rewarding!). Then it may be a good idea to enlist others to question your logic and help you push yourself.
Learn more about thinking MECE
Check out my book, in which MECE thinking is everywhere (pp. 10, 11, 58–61, etc.)
Here are additional ideas about the MECE concept.
MECE thinking also has limitations; here is more on that.
See Austhink’s take on MECE.
Chevallier, A. (2016). Strategic Thinking in Complex Problem Solving. Oxford, UK, Oxford University Press.
Davis, Ian, David Keeling, Paul Schreier and Ashley Williams. “The Mckinsey Approach to Problem Solving.” McKinsey Staff Paper, no. 66 (2007): 27.
Eppler, Martin J. “Toward a Pragmatic Taxonomy of Knowledge Maps: Classification Principles, Sample Typologies, and Application Examples.” In Information Visualization, 2006. IV 2006. Tenth International Conference on, 195-204: IEEE, 2006.
Gardner, Howard. Frames of mind: The theory of multiple intelligences. Basic books, 1985.
Kazancioglu, Emre, Ken Platts and Pete Caldwell. “Visualization and Visual Modelling for Strategic Analysis and Problem-Solving.” In Information Visualisation, 2005. Proceedings. Ninth International Conference on, 61-69: IEEE, 2005.