Systems thinking at XANT is a process of understanding how systems behave, interact with their environments, and influence each other.
A system is composed of parts generally identified as people, processes and technologies. Systems receive inputs and transform them into outputs for other systems in the larger environment.
A system must contain all of the resources for transforming inputs into outputs. For example, a bicycle is not a system until a cyclist puts it in motion.
The goal of a system is to optimize the transformation from input to output. This is accomplished through continuous testing and improvement.
Step 1: Evaluate (Just the facts)
Evaluating is the process of gathering baseline data about the people, processes, technologies, inputs, outputs and intentions of the system. It is important at this stage to simply gather data without judgment. Seek to understand how inputs are being transformed into outputs, at what cost (efficiency) and at what pace (speed).
For example, consider a system that consumes a certain amount of animal proteins, grains, fruits, vegetables, minerals, water and oxygen, and produces an egg. I measure the inputs and outputs and find that, with the current set of inputs, the system produces 1 egg every 35 hours. The next step is to analyze this data.
Step 2: Analyze (Ask why? Apply assumptions.)
Analyzing requires examining the data gathered in the evaluate step, applying assumptions about the relationships between inputs, people, processes, technologies and outputs, and making decisions based on my intentions for the system.
If my intention is to maximize the number of eggs produced, I might look at an egg and decide that because it is primarily protein, fat, minerals and water, the primary inputs for my system — which happens to be a hen — should include those resources in the same proportions that exist in an egg. Then by doubling the amount of inputs, I anticipate a doubling of the egg production.
Step 3: Design (Good enough)
Designing involves creating or modifying parts of a system in order to effect a change in the transformation from inputs to outputs. Designs don’t need to be perfect. In fact, designs need to be just good enough to introduce change into the system in such a way that the effect of the change can be measured. Because I want to increase the number of eggs produced, I concoct a special diet of primarily protein, fat, minerals and water for the system.
Step 4: Implement (Just do it.)
Implementing is putting the change into practice in the system in such a way that the effect of the change can be measured. I double the amount of food (my special concoction) and water I give the system/hen.
I measure the output of eggs and find …
Back to Evaluate
… that egg production actually falls from 1 egg every 35 hours to 1 egg every 40 hours. I also notice the system/hen responding negatively to neighborhood cats.
So I ask why …
… and learn that chickens are opportunistic eaters. They will eat what they need to produce eggs as long as there is plenty of everything. They will not overeat (i.e., get fat). I also learn that stress reduces egg production. I assume that because I made such a fuss and changed the system/hen’s diet, the stress was a stronger influencer than diet.
So I …
… decide to go back to the original free-range diet and take efforts to keep the neighborhood cats from bothering the henhouse.
I fill the chicken run with a variety of foods that the system/hen seems to prefer and I put up visual barriers so the system/hen can’t see the neighborhood cats. I enjoy eggs for a couple of weeks then …
… measure an increase in egg production to 1 egg every 28 hours …
And so on.
And that, my friends, is systems thinking.
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Image credit: Ruben Alexander