Complex Organizations are… Complex.

  • February 12, 2010
  • Scott
  • 0 Comments

Great article by Keith Swenson explaining why large and complex organizations are difficult to model using “scientific management”.

I think Keith makes a couple of great points that are worth letting soak in:

  • Complex systems are unpredictable
  • Organizations that “Learn” can adapt when the environment becomes less predictable.
  • If you divide your organization between brains and brawn, you are failing to reach your organization’s full potential.
  • Learn from *near* mistakes – not just the *actual* mistakes.

Of his takeaways for BPM- my favorite is transparency:  people involved in the process need to understand the part they play and the context of the process if they are to help the organization learn and improve upon it.

Worth the read and there’s a good comment-stream as well.

On a tangent relating this to political science…

For very complex organizations, it may well be worth applying some theories from political science and international relations to your thought process. One of the most useful political science theories is “Realism” – the notion that nations act in their own best self-interest, in a rational and calculating way.  My first exposure to this was in Dr. Krasner’s course on International Politics.  Usually deviations from what appears to be self-interest can be explained by imperfect information or miscalculation – but in general realism probably most closely describes how most states will act at critical moments of international relations.  In large, complex organizations, it helps to assume that departments, teams, and divisions will apply a healthy dose of Realism to the way they behave.  Part of how you deal with overly complex systems is by having useful abstractions that give you a shortcut to understanding, not ALL of the minutiae of cause and effect, but rather the SUM TOTAL of these effects.  It is much like the difference between micro and macro economics. Realism does not deny the other effects, but it argues that they are secondary effects rather than the primary correlating explanation.

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