Are You Using the Right Tool to Solve Your Problem?

hand tools

There are many types of problems in the world, and even more methods that can be used to solve them.  The key is using the right method or tool to solve the problem at hand.

Let’s start with an analogy. You want to hang a picture on the wall. You have multiple tools at your disposal. Your tools include a screwdriver, a 3D printer, and a hammer and a nail. Now, you could use the handle of the screwdriver to punch the nail into the wall, which might be a bit slow and unsafe. You could use the 3D printer to make a hammer and then pound the nail into the wall,  but making the hammer may take a long time and be very costly to produce. Or you could just use the hammer. All three methods would produce the same result, but some may not be as fast or efficient as others. I like to keep this in mind when looking at how to solve a problem.

Lean (or continuous improvement), Agile, Systems Thinking Practice, and Design Thinking are all methods that can be applied to a problem to solve it. Just like the hammer, some are more effective in certain situations. But how do you determine what method will work best?

Start by determining what type of problem you are dealing with, then you can determine which tool will best suit your needs. Based on the Cynefin framework – problems can fit into four areas.

Obvious Domain

According to Cynefin’s framework, in the obvious domain a relationship between cause and effect for the problem exists. It is predictable and repeatable. This relationship can be identified in advance by a reasonable person.

Problems that fit into the obvious domain are very common. For example, patients are leaving the Emergency Department without being seen. This is often the result of wait times being too long. By focusing on wait time reduction, the issue can be resolved.

Many lean tools, like 5S, can be applied to problems in the obvious domain and be very successful.

Complicated Domain

In the complicated domain a relationship between cause and effect exists, but the relationship is not self-evident, and therefore it requires some expertise to solve.

An example of this type of problem when a hospital is working on the overall patient experience through the emergency room, to inpatient, to discharge. In this scenario the relationships between flow stoppers within the system are not usually apparent without doing some digging into the entire flow process.

One tool that might be very effective in solving this type of problem is a value stream map.

Complex Domain

The complex domain is a system without causality. Meaning the cause and effect relationships are only obvious in hindsight, with predictable, emergent outcomes. If an experiment succeeds we amplify it, if it does not we dampen it quickly.

An example of a problem in the complex domain would be an ED that is bogged down with many homeless people who are coming in for non-emergencies. This is potentially a population health issue. Are they coming in due to mental health issues, basic care problems, or loneliness? It would take multiple experiments to sense this problem, learn, and scale the actions that work.

Chaotic Domain

According to Cynefin’s framework, a problem would fall into the chaotic domain if no cause and effect relationship can be determined. In this type of situation, action must be taken quickly to stabilize the system.

If the Emergency Department is overflooded with cases due to a nearby building collapsing, that would fall into the chaotic domain. In this situation, the hospital may be able to execute their disaster protocols, but there would be instances where caregivers, managers, or leaders need to improvise to stabilize the situation.

Some problem-solving methods that are often applied to issues in the chaotic domain are hastily-formed networks and Antifragile methods.

To be an effective problem solver is to be an adaptive problem solver. It is important to be aware of the type of problem that you are working on and use an appropriate method to solve the problem. I recommend staying curious, learning more, and keeping your mental models open.

Please share your comments or learnings in the comments section below.

Jason Schulist, Faculty


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About Jason Schulist

Jason Schulist is the President of the Generative Local Community Institute (GLCI), a non-profit whose mission is to connect communities so that they can improve their problem-solving capability and accelerate community impact. Prior to forming GLCI, Jason served as Appvion’s Vice President of Continuous Improvement (CI) from 2013-2017. Appvion’s CI Deployment was awarded Runner-up in the PEX Global Award for Most Innovative Culture Change Deployment for 2016 and consistently exceeded Appvion’s Profit Improvement goals. Mr. Schulist worked in the Utility Industry from 2004-2013 with roles as DTE Energy’s Director of the Program Management Office (PMO) managing a $1B portfolio of projects and as Director of Continuous Improvement saving over $700M while building CI capability and winning the IPCC's Best Process Improvement Program in 2010. Prior to DTE Energy, Mr. Schulist held management positions in lean operations, business development, and corporate strategy with General Motors. Mr. Schulist earned a bachelor’s degree in Electrical Engineering and Computer Science from Marquette University and two Masters’ degrees in Electrical Engineering/Computer Science and Management from the Massachusetts Institute of Technology (MIT). Mr. Schulist is a Lean Six Sigma Black Belt and has a Project Management Professional (PMP) certification. Jason has a passion for generative local community and has founded the Skillsfest movement that applies Continuous Improvement to thorny community problems. He is a co-founder of the Michigan Lean Consortium and past Chair. He currently serves on the Boards of the United Way of the Fox Cities, CAP Services, the MIT Club of Wisconsin, and the POINT Poverty Initiative in Northeast Wisconsin. View all posts by Jason Schulist →

4 Responses to Are You Using the Right Tool to Solve Your Problem?

ernest mayer says: 01/08/2019 at 8:23 am

I am a little confused with the definitions and explanations. For example, in the chaotic domain, your example says no cause and effect exists, yet i see a very strong cause/effect relationship from building collapse to ED overflow. I have a similar issue with the other domains. In the complex domain, the experiment sentence does not make any sense, and it does not seem to me to explain anything about complex domain. Thanks!

Mark Darvill says: 01/08/2019 at 2:28 pm

I agree with you. The article seeks to draw a distinction but the examples given do not illustrate one. I do agree with the overall point, use the right tool for the job, but the explanation of the different domains needs more work.

Jason A Schulist says: 01/11/2019 at 7:52 am

Thanks for the feedback Mark. In the Adaptive Problem Solving workshop, we cover these domains in more detail using health care and other examples to bring clarity as well as what to do about it.

Complex problems need a different tool set in order to be effectively addressed. What becomes really interesting is that sometimes small, non-intuitive countermeasures can make a tremendous impact to a complex problem. For example, in one Colorado city, a large percentage of people calling 911 were elderly. What they discovered is that many of these calls were actually the result of the need for social connection – over 80% of the ambulance visits were not for an emergency. The city instead created a social worker position that engaged in monthly elderly home visits resulting in a significant drop in unnecessary 911 call and ambulance visits (at a much lower overall cost).

Thanks again for your feedback! Jason

Jason A Schulist says: 01/11/2019 at 7:36 am

Hi Ernest, Thank you for the feedback! When I wrote this blog, I intended to use the emergency room as an example for each of the 4 domains of problem solving. I agree that the effect the emergency room sees from a collapsed building is a large unexpected intake. The point in the chaotic domain is that these events themselves are not predictable. Where will the next catastrophe take place in the United States? We do not know. What we can do is manage and plan our response to unanticipated events.

With Complex Adaptive Systems (CAS), unless we understand all initial conditions of the system, we will not be able to “predict” or “solve” the problem. The best that we can do is sense the current patterns and see how our experiments may influence the pattern. Communities are complex adaptive systems and that leads to addressing many population health problems in a different way than the typical “abnormality from standard” problem that one might find in a hospital intake process.

Again thanks for the feedback! Jason


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