Managing messes

Making the Most of Mess suggests we manage strategy/policy the same way control engineers manage complex physical systems … which is exactly what system dynamics has made possible for the last 50 years, since it is precisely the use of control-theory principles for understanding and managing social systems. It has its limits but we can go a long, long way …

We may need first to clarify what we mean by “mess”. I haven’t been all through the book, but skimming the early part implies it means “complicated and with uncertainties” [Just had an energetic debate on another forum about “complexity”, which many take to mean the same, rather than the more extreme definition implied by complexity science]. If so, then as Emery Roe suggests, we manage messes in the same way engineers manage complex physical systems – with simulations and control theory principles.

Two differences for us may be [a] that many things in our business systems are less easy to specify and manage and [b] many of those and others are intangible … but we know how to deal with uncertainty – run plausible ranges of values and assess the resulting range of likely outcomes … and we know how to deal with intangibles – firms measure them all the time, the problematic ones concern ‘state of mind’ [morale, reputation …], and while it is tough to know how every individual feels and responds, patterns are easy to detect on the larger scale [if that were not so, then marketing would be impossible].

A colleague asks how we might deal with ingenuity and other emotions.

Emotions [= state-of-mind] are accumulating factors see – I get more annoyed if my regular train is more often late, and by more minutes each time, but I can’t stay furious for ever and my annoyance decays if the train is on time again. A bank measures what may be happening to customer satisfaction by recording “miserable moments” that annoy them and “magic moments” that make them happier. And all kinds of business track customer and staff satisfaction, and have counter-measures to improve them – so I guess we already have algorithms for dealing with at least some important emotions. Not perfect, and doesn’t cover everything, but it’s a good start.

Ingenuity seems a harder issue to tackle. We can understand, model and manage the creative output of teams to a degree [e.g. new product development in consumer technology, pharma etc] – like any capability, see, it is made up of the skills of the team, the procedures they follow, and the information they use. And we can understand both the volume and quality of the output – the number and appeal of new products. But that runs out of explanatory power with the creative industries [how to model the audience appeal of films from a movie studio or clothes from a fashion house?] and doesn’t get us to the ‘inspired’ leaps, like iPad.

Leave a Reply

Your email address will not be published. Required fields are marked *