The MECE framework is an exhaustive expression of information that must account for all conceivable scenarios. While the framework is used in categorizing information and data processing, it is commonly used in formulating problems and then solving them. The MECE framework is a means of the exhaustive grouping of information into categories that are both mutually exclusive (ME) and collectively exhaustive (CE).
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Understanding the MECE framework
The MECE framework argues that to understand and solve any large problem, potential factors must be sorted into two categories.
Mutually exclusive
Mutually exclusive means that each factor can only fit into one category at a time. In other words, there is no overlap.
Consider the example of a French cheese company, who is seeking to find the root cause of a problem with its distribution network. A framework that is not mutually exclusive may identify two items: distribution networks in France and camembert product distribution networks.
The reason for this lack of mutual exclusivity is that there is overlap between the two items. Since camembert is distributed in France, the item is counted twice.
Thus, a mutually exclusive problem may choose to analyze camembert distribution in France and camembert distribution in Italy. Here, there is no overlap between each item because they occupy different geographic areas.
Collectively exhaustive
Collectively exhaustive means that each factor covers all possible causes of a problem.
Returning to the cheesemaker with a distribution problem, simply looking at France and Italy is not collectively exhaustive. The company also exports to Spain and the UK, so assessing France and Italy in isolation may cause analysts to overlook the root cause of the problem.
Ultimately, the MECE framework allows businesses to investigate every potential cause in isolation. They do not have to worry that a specific cause may potentially influence the role of another cause in creating the same problem.
Five steps to developing a MECE hypothesis
- Understand the problem in detail. What outcome does the business hope to achieve?
- Write down the problem statement, ensuring that there is no room for ambiguity.
- Then, list potential options (solutions) to the problem using a MECE idea tree. In the case of the cheesemaker, each option must be both mutually exclusive and collectively exhaustive.
- With a list of potential solutions illustrated on the idea tree, consider the pros and cons of each individually. Remove any that seem illogical or add new solutions gleaned from greater insight into the problem itself.
- Select the best option and then present it to internal or external stakeholders. At this stage, it’s important that the option is proven, not obvious, and can be performed with a set of predetermined actions.
Applications of the MECE framework
Several frameworks across various disciplines have MECE principles at their core, including the:
- Cost-Benefit Analysis – which involves the systematic evaluation of the costs or benefits of a project, policy, or program.
- Porter’s Five Force Model – which is a powerful tool for understanding the competitiveness in a given industry.
- 4C Model – a tool for analyzing workplace psychology using core components of motivational theory.
Key takeaways:
- The MECE framework allows businesses to assess large amounts of information according to mutual exclusivity and collective exhaustion.
- The MECE framework forms the foundation of several other frameworks, but it is most commonly used in the rigorous and exhaustive solving of problems.
- Solutions to problems derived from the MECE framework must have proven effectiveness and be realistically achievable. Crucially, they must not be the first or most obvious solution encountered.
Other strategy frameworks:
- AIDA Model
- Ansoff Matrix
- Balanced Scorecard
- BCG Matrix
- Design Thinking
- Flywheel
- Lean Startup Canvas
- OKR
- Pestel Analysis
- Technology Adoption Curve
- Total Addressable Market
Additional resources:
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