Quantitative Complexity Management: A simple 5 step process

There are plenty of self-anointed subject matter experts happy to pontificate upon complexity. Sources, causes and their own approach to solutions but what they all (except Ontonix) lack is the means to establish a sound (verifiable) quantitative basis from which to commence the process of ‘complexity management’, monitoring and maintenance.

Check it out, for FREE, on-line!:

cid_part6_04050208_01010701ontonix.gifThe following five-step process forms the backbone of all our service engagements. It reflects our extensive experience in Quantitative Complexity Management in a multitude of applications spanning a wide variety of industrial sectors. It also illustrates the typical structure and workflow in a business simplification and ‘robustification’ project.

via Ontonix – Complex Systems Management, Business Risk Management.

One Response to Quantitative Complexity Management: A simple 5 step process

  1. Hi David, as much as we agree on the issue of complexity being misunderstood we do not agree on the approach to deal with it. While we are all self-appointed experts and so is Ontonix.

    My first point is that complexity is not a bad thing. It is a fact of life and it the driver behind evolution. Resilience actually comes from the inherent ability of a complex system to adapt to outside change stresses. One problem is that businesses are no longer complex, but they are overly complicated, structured rigidly and managed by reliance on too many numbers.

    While measuring makes a lot of sense particularly in the sense of enumerable process goals, all other measurements especially those that require complex data acquisition and model assumptions have to be seen as useful approximations and no more. The same is true for measuring the complicatedness of a business structure. But you are not measuring or actually quantifying anything. It the same approach that is used to identify your probability of having a heart attack based on a statistical model in which you enter your lifestyle parameters. In reality this has nothing to do with you. You might change something in your lifestyle that improves your statistical probability and you get the heart attack from the things you haven’t changed. The changes you make to improve your odds might be bad for you, like for example taking medication to reduce cholesterol.

    A measurement is only valuable if it tells me what is wrong. And then the measurement might still be inaccurate or the measurement model might be false. The assumed causality does not exist and or the action taken simply supresses the measurable symptom. That is a very common approach to solving problems. A business can be very small and not have complex management structures but the way people interact is not right. It is not complex and still wrong. And exactly the opposite, that a large business is run as many small self-managed units, so the total is complex but not the manageable unit. It works.

    So estimating complexity as a number (aka Rate-A-Business) through an abstract structural model where you have to invent your own values of what you want to measure does not excite me. The complexity now depends on what I perceive as a relevant structural entity. And then I have a number and it says it is good or bad. And then???

    Most certainly I do not see the outcome of such estimation as the silver bullet that solves the problems of businesses today.

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