World Economic Forum published top 10 job skills of tomorrow. In our blog series you will get a deeper dive into each one of these skills of tomorrow.
We have complex problems among others in science, in our society, in businesses and in human relationships. Chaos theory, Cynefin-framework and David Snowden’s ideas  give some ideas how to handle them.
Simple, Complicated, Complex and Wicked problems
Simple problems can be repeatedly solved with best practices because cause and effect relationships work consistently.
Complicated problems have a variety of influencing and contributing factors that experts can analyse and decide what kind of solution fits to each particular case.
Surprises make problems complex. In these cases cause and effect relationships are difficult to observe. They are non-linear, they have feedback loops and time delays. Our reasoning techniques do not work. Let’s say that we start to market our new product. At first nothing happens. We have no customers. Then suddenly we have so many customers that that we can’t deliver our orders. We build more capacity just to see that buyers are now gone.
In wicked problems the state of affairs emerges from the collaboration of several influencing factors. Observations look chaotic and statistics do not show enduring correlations. You may find boundaries within which observations vary. Wicked chaos is not random. It just looks like that because we lack evidence and we can’t analyse forever. We have to act now.
Each area of science has its own research methods. A generic scientific method starts with selecting the problem and analysing it. Analysis results in hypothesis about possible solutions. Experiments are then conducted to see whether the hypothesis is false. If it passes the tests it may become a theory and we start to act based on the solution that we found.
This scientific method is based on empiricism. Organizations thrive with the culture of experimentation. In mathematics we use just logic. Elsewhere we need both real world evidence and logic.
Analyzing Complex Problems
At first we select the problem that we are dealing with. Why is it a problem? How should we reframe it?
To analyse a problem we Identify the influencing factors. Fishbone diagram  can be used to describe these. For example our sales depend on our product, its quality and price. Our marketing helps our customers to find us. We may have problems with production capacity and our competitors.
Influencing factors may overlap and they depend on other factors. Root cause analysis is lean technique that often uses several why-questions to find the ultimate factor that we should solve  .Removing just the symptoms is not a good alternative. For example we can analyse influencing factors of quality in this diagram. Fishbone diagram changes to a tree that has many branches and subbranches. Kepner-Tregoe problem solving  gives a structured approach to problem solving with root cause analysis.
Business intelligence slices and dices the problem and its influencing and contributing factors. At first we can use question words how, how much, what, who, where, when and why to understand the problem. For example our sales revenue has dropped 20% while the prices were down 5 % and contracts 15 %. The customers that reduced purchases the most were young people in age group 20 to 40 years old. This happened in city areas during this winter and spring.
Next step is to make our analysis quantitative. Big data adds numbers to the analysis.
Systems thinking says that our problems emerge from a network of influencing factors and their relationships. We can use causal loop diagram to visualize them .
The example diagram shows that our sales increase when demand increases and that demand decreases when price increases. There are reinforcing and balancing loops that work in the same or the opposite directions. Delay between sales and capacity results in a non-linear relationship between them.
We can try to understand for example price elasticity using our simple model and data that we have gathered. Just correlation of factors is not enough. We need to understand the mechanism. We may also have a mechanism but not the effect. We don’t know for sure which one is the cause and which one is the effect. Does demand drive price or price drive demand or both. Timing and the numbers has an impact on the behavior of the system, because the relationships are non-linear and we have feedback through capacity in this example. We can use calculations and computer models in addition to tests and experimentation.
Testing the hypothesis
When we understand the system of influencing and contributing factors of our problem, we brainstorm to create interventions to the system that resolve the problem. For example we might get more sales if we give discounts to our customers. This would increase demand and sales as a consequence.
We need to be careful and test whether our sales are better with discounts than without because there are unknown factors in complex systems. We don’t know what will the competitors do. There might be opportunities in the business environment that we can’t predict. We can’t analyse forever. We have to act now.
We can use randomized double-blind study that the gold standard in medical research. We need to know whether the changes of our sales are due to discounts or something else. In an experiment we divide customers randomly into two groups: 1. those who get a discount and 2. those who don’t . This kind of A/B-testing is widely used.
Results depend on the sample and customer selection. Because there is a possibility of chance, experiments have be repeatable. In complex world they are not. Rules of our game change. We will never have all the data and we don’t know and understand all the contributing factors perfectly.
Because we can’t predict the outcome in uncertain world, we proceed step by step. We adapt after each step because, we cannot make a comprehensive plan. We are moving forward all the time. Error steps are possible and allowed.
Leaders are humble servants of others that catalyse the discovery of new problems and better solutions. Life in uncertainty is like life of a scientist and a gardener.
We tolerate and appreciate different opinions. Evidence-based solutions succeed and bad ideas fail quickly when we have freedom of science, discussion and communication. Censorship is detrimental to creativity.
- 2.David Snowden: A Leader’s Framework for Decision Making:https://hbr.org/2007/11/a-leaders-framework-for-decision-making
- Why did Titanic sink ? https://www.youtube.com/watch?v=38RlXdr4Np0 (4m39s)
– Pentti Virtanen
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