vendredi 4 janvier 2013

A tip to better understand the difference between simple system , complicated system, complex system and chaos.

In today’s world, it ‘s important to understand if we face complicated or complex system before to approach it.

In the article "learning to live with complexity, Gökçe Sargut andRita Gunther McGrath insist on how “it’s easy to confuse the merely complicated with the genuinely complex. Managers need to know the difference. If you manage a complex organization as if it were just a complicated one, you’ll make serious, expensive mistakes.”

To illustrate the difference, I've choosen the way JF Zobrist described the difference (
A simple system - we understand the operation and all potential uses (how it works or how to use it) of the first glance : “a wheel”
A complicated system is a system governed by 2 rules : “a plane”
•  A plane has many parts and interactions are not easily understood. Without prior training, we can’t understand the operation and all potential uses. Training is mandatory.
•   When we’ve learned the operating mode, we’re able to predict all potential failures, and thus ensure zero risk.”
Try it. Take a plane, you disassemble it and reassemble it according to the manual and the training. This is a complicated system but following instructions step by step, you get there and it works again.
In a complicated system, the success of the approach mainly depends on the execution of a plan, a process,…
A complex system is a system governed by 3 rules : “ a group of bird”
• It is impossible to predict in advance the behavior, the outcome
• It is the most reactive system to its environment
• It is governed by a limited number of rules, but simple which can not be derogated. If we derogate, we fall into the CHAOS
The group can be composed of hundreds thousands of birds, if a plane passes around the group, the whole reacts, instantly as a single individual! A complicated system with a great leader is not able to relay information to react so fast! Two simple rules  govern the group :
- Each bird in the periphery has to return to the center and so allows to realign the the movement of all because
- Each bird constantly monitors in order not to collide with its immediate neighbors.
If one of these two rules is not respected, the system is in CHAOS.
CHAOS is characteristic of systems that are unable to establish complicated rules and are unable to respect simple laws/rules. Usually these systems are developed according to two criteria: the size of the community and/or the degree of empathy.”
In today’s world, system are becoming more and more complex. Most of the approaches used to address complex system are approaches for complicated system ( Six Sigma, Balance Scorecard, …). But how to work in a complex system ? According to Gökçe Sargut and Rita Gunther McGrath , they recommend to
· Improve the way you forecast by using tools/models that simulate the behavior of the system. Process Behaviour Chart (see Shewart), I mean.
· Improve the way you mitigate risk by minimizing the need to rely on predictions/expectations to experiment, by…
· Make different resource tradeoffs by providing diversity of thoughts and by investing in incremental and small investment in new project/approaches.

Nothing new ! Perhaps.  But coming from a different framework and purpose as Deming, they describe some of the essential parts of " a system of profound knowledge" : appreciation for a system, knowledge about variation, theory of knowledge, and psychology. The system of profound knowledge is a complex system as we've to consider it as a network of interdependent components that work together to accomplish the purpose of the system. The  4 interrelated parts can't be understood if separated from one another.

As Deming said : "Rational prediction requires theory and builds knowledge through systematic revision and extension of the theory based on comparison of prediction with observation."(Deming, The New Economics).  The system of profound knowledge is based on the premise that management is prediction.   If we fail to predict what we expect  ("Theory), we fail to predict the results of our experiment to improve, we fail to analyze the results of our experiment and we fail to learn about our system. So we don't improve. 

So today, I invite each of you to think about those questions :

Do you address a complex system with an approach/tools for complicated system ? What would you recommend to address a complex system ? Do you share the recommendations of the authors to address a complex system ? What do you think of SPK to adress complex systems ?

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