How can we become better problem solvers?
Whatever you call yourself, CEO, salesman, barista, tinker, taylor, soldier, spy. One of your primary roles in whatever employment you do is that of problem solver. Abstractly, the only differences in problems are matters of type, magnitudes, and involvement, and how effective you are helping provide solutions.
To become a better problem solver I believe you first need to break business problems into categories and types, and then identify what type of analysis to implement. This approach has always helps me identify the best tools to use for best solution discovery.
Just like we live in a 3 dimensional world (length, width, and height) and one temporal dimension (time) imposed on them . Most business problems also have root causes in 3 dimensions and one that is imposed on them . They are processes, people, systems, and data (analytics) is imposed on them.
So in categorizing problems or better yet their root causes, I use these 3 primary dimensions (Processes, People, Systems) and 3 secondary dimensions (Interaction of Process-People, Systems-People, Processes-Systems) as a starting point. Using simple root cause analysis like cause affect diagrams or cause mapping, I break the problem components into the 6 categories. I realize there could be more categories, but these 6 are a great starting point for quick solutions.
Once you categorize where your problems are coming from, you can use different methodologies (tools) to help you identify possible solutions.:
After identifying all categorical root causes, I then look at what type of problem it falls under.
Binary Landscape Problem
The simplest problem type is a "Binary Landscape" problem.
For example:
I'm cold I put a jacket on now I'm not cold anymore.
To solve this type of problem, you just provide what is needed and your done.
Now most people think that almost every problem falls under this type. They will often use words like "that is THE PROBLEM, and this is THE SOLUTION".
However these problems are the minority. Most problems are not binary, most problems are degrees of complication and complexity.
Mt. Fuji Landscape Problem
The next type of problem after binary is what is called a problem of local optimum.
To solve this problem or improve it, you use continuous improvement methods.
You will improve up to the point of a local optimum, and if you continue you can over-process and/or start breaking things in other areas.
You can use the analogy of walking up a mountain to get to the top. Once there you can't go any higher, any direction and you will start going down.
That's why according to Professor Scott E. Page in his work
"Understanding Complexity" this problem is called a
"Mount Fuji Landscape" problem.
Mountain Range Landscape Problem
The third type is called a global optimum problem .
We are now dealing with more complicated issues.
To solve these types of problems you need innovative breakthroughs or disruptive technologies.
With this type you need to go from the local optimum to a higher global optimum. You reach new Heights through leaps and bounds not directly.
That is why this problem is called a
"Mountain Range Landscape" Problem.
A clear example is,
"If you used continuous improvement with Horse and Carriage, you would have ended up with bigger horses and lighter carriages, not the Automobile".
Dancing Landscape Problem
Last you have complex problems, these are problems where there are no solutions, the best you can do is respond nimbly (Agility) and quickly (responsively) to the problem.
Note:
You can look up VUCA (Volatility, Uncertainty, Complexity, Ambiguity) for more information.
Because it moves on you all the time, this is what's called a "Dancing Landscape" problem.
What works today may not work tomorrow.
Analysis
After identifying categories and types, I then clarify what objective and subjective analysis I need to use to help solve or mitigate the problem.
For Objective analysis, I use
Descriptive Querying (tell me where I am),
Predictive Modeling (tell me where i'm going),
Prescriptive Optimization & Stochastics (Tell me how to get there), and
Cognitive Pattern Recognition (Provide context and Gaps Analysis).
For Subjective analysis, I used non-structure tools like
Sentiment analysis,
Cultural and Cognitive Diverse analysis,
and now new AI tools..
Go forth and Solve!!!
Just like with automobile mechanical problems, you need information to troubleshoot and fix them (Make, Model, Issue). When troubleshooting business problems look at (Category,Type,Analysis) to help you troubleshoot and solve them.
Keep in mind that problems usually have combinations and permutations of all categories and types, "peel back the proverbial onion" and break it down to manageable components.
I hope this process helps you become a better problem solver.