Our attitude about complexity has a major impact on the approaches we take. Many people take a simple approach, believing attending to just a few things will be sufficient. Others believe that if they just look long and hard enough at all relationships between people and the artifacts in play that they can figure things out and make accurate predictions about what will happen when they make changes. This approach comes in two flavors. One is that we can start with small teams and combine them together. This deals with complexity by taking an organic growth approach where each of the components are reasonably understandable. The second is to try to accommodate everything. The challenge with both is that we’re embedded in a complex adaptive system and making predictions based on the relationships between a system’s components is fraught with error and risk.
Both of these approaches, however, ignore the fact that understanding the relationships between components in a system does not provide us with the holistic view or predictability needed to improve the system.
An organization creating new products and services is a complex adaptive system. A CAS is a system in which a perfect understanding of the individual parts does not automatically convey a perfect understanding of the whole system’s behavior. This means one can’t be certain what a change in one part of the system will have on another. But this doesn’t mean that cause and effect doesn’t exist for the system as a whole.
The reality that is setting in with these approaches has led to yet another approach. This has us take an attitude that our system is too complex to have any meaningful predictability. We can only hope that positive changes will emerge as we make decisions. This approach is attractive for many reasons. Practitioners can avoid responsibility for failure by just acknowledging that their system was complex. Proponents of frameworks to improve companies can provide either simple or complicated solutions and, when failure occurs, simply ascribe it to the frameworks being difficult to master due to the complexity of the organization.
Even in complex adaptive systems there is a cause and effect when one deals with the system as a whole. Here are some examples:
“There is more value created with overall alignment than with local excellence.” Don Reinertsen
“It is easier to act yourself into a new way of thinking, than it is to think yourself into a new way of acting.” Millard Fuller
“A system must be managed. It will not manage itself. Left to themselves, components become selfish, competitive, independent profit centers, and thus destroy the system … The secret is cooperation between components toward the aim of the organization.” —W. Edwards Deming
“Operating a product development process near full utilization is an economic disaster.” Don Reinertsen
In addition, there are some maxims which can provide us with rules of engagement that, when ignored, will predictably cause problems:
“If you only quantify one thing, quantify the Cost of Delay.” Don Reinertsen
“Those who do not learn history are doomed to repeat it.” George Santayana
“Often reducing batch size is all it takes to bring a system back into control.” Dr Eli Goldratt
“Culture eats strategy for breakfast.” Peter Drucker
The ability to predict systems as a whole has been demonstrated many times over. However, one does need to understand the difference between micro-predictability (e.g., if the color at a roulette table will come up red, black or green) and macro-predictability (e.g., more money is staying at the table). But this predictability does not come from us creating models we can use to understand a complex system by being insightful. No, it requires finding the inherent simplicity that already exists in the system.
The presumption is that inherent in complex systems are rules that, when understood, enormously simplify how to look at the system. These rules already exist. We must find them and take advantage of them. Dr. Goldratt, creator of The Theory of Constraints, calls these rules Inherent Simplicity. He suggests we must recognize the need to come from is how to look at complex systems.
This is not just a better approach, it avoids a significant danger of not doing it. Thes reverence for complexity is deadly. Again, from Dr. Goldratt – “The first and most profound obstacle is that people believe that reality is complex, and therefore they are looking for sophisticated explanations for complicated solutions. Do you understand how devastating this is?” That is, we must avoid blaming failures on the complexity of the system.
This isn’t to say that the world isn’t complex. From The Choice – “He doesn’t claim that reality is not overwhelmingly complex; he acknowledges it in full. But what he says is that there is a way to realize that from another more important aspect, it is exceedingly simple”
Factors for Simplicity
Although I don’t believe there is any discrepancy between FLEX and Dr. Goldratt’s ‘inherent simplicity’ I believe the term “factor for simplicity” is both easier for people to grasp and avoids any possible misrepresentation of Dr. Goldratt’s work. This is a partial list of concepts that I have found to be useful to create a simple understanding of complex systems while enabling useful predictions of proposed changes:
- Batch Size
- How close to capacity are people working?
- Lower multi-tasking which lowers the capacity of people
- Attend to the effects of the system on people
Delays in workflow
- Delays between making an error and detecting it.
- Relationship between build and test
- Number of value streams people are in
- Degree of visibility present
- How well people collaborate
- Cadence and Synchronization
- Product/service quality
We are not looking for simple solutions. We are looking for a way to look at our problem so that the problem becomes simple and our solutions become effective.
“Things should be as simple as possible, but no simpler.” Albert Einstein
It is difficult to make predictions, especially about the future. – Mark Twain
For every complex human problem, there is a solution that is neat, simple and wrong. – H. L. Mencken
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