Sunday, 1 March 2015

A Wiggly Path To Transformation


According to Sunday Times journalist, Carly Chynoweth, “Managers must learn how to adapt so they can solve problems they haven’t faced before.”

And:

“The fundamental impression they give is that the future can be organised and managed to achieve what you set out to achieve, and as long as you do it right it will come out as planned. But the reality is much more complex. Organisations are wiggly. They don’t operate in the neat, straight-line way conventional management thinking assumes”.

“It is not possible to predict outcomes — they emerge from people’s actions. You might have plans about what you are doing, but so does everyone else.”

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The culture and traditions of 100 year old Utility business make behavioual change hard. Power-plant Engineer's detailed and precise planning techniques don’t work for this sort of long-term change. In our case, over 70% of the business processes will change. The main hurdle for us was the lack of certainty and predictability over 5, 10 and 20 years. It was impossible to answer the basic questions; when, how and how much? There was discomfort over sanctioning any plans without the ‘details’. This resulted in a period of ‘decision-making-block’.

This is when we introduced ‘Transition State’ planning. This borrows ideas from Complexity Theory: Specifically, the characteristics of Complex Adaptive Systems. We started with the premise that we cannot predict the long range future.  We also accepted that outcomes will emerge at various points along the way.

Much like ’Scenario Planning’, we first developed a few hypothetical ideas. These were refined in workshops with subject experts until we reached a reasonable consensus of:
  • the principles we will apply to different aspects of the transformation ahead of us,
  • an initial guess of when certain outcomes are needed,
  • a list of the main influencing events/decisions assessed as ‘Known’, ‘Unknown’ or ‘Unknowable’ now – along with an assessment of risk.

Armed with this, we plotted-out a series of ‘Way-points’ over time. We call these ‘Transition States’. Each has a few goals (expected outcomes) that we estimate to complete.  At this stage, Transition States are not pinned to a hard date.  Rather they describe the sequence of outcomes, and roughly when we think they'll happen. Transition States are re-planning points; a time to reassess and re-estimate the next phase of work. The Transition State is an opportunity to amplify value-adding and extinguish value-detracting aspects.

After a few iterations, a more precise view emerged of the early Transition States and a traditional approach to planning and deliverables could be applied.

At first, we were quite concerned that this approach would be rejected by the culture; a plan must be very detailed and accurate before work can start. This, however, wasn’t the case, decision-makers could see the merits of a more agile and iterative approach to complex change. They liked the way ‘doable’ increments became clearer after a few iterations. They also liked the explicit opportunities for re-think and course-correction.


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Is a Complex Adaptive approach the only way to manage large-scale, long-running, change? I’d like to hear how others approach designing and managing business transformations and whether they see the merits of Complexity Theory that we've seen, 

6 comments:

  1. Comment from @Cybersal (problem posting)
    This makes a lot of sense to me and resonates with my experience. It seems to me that, in Cynefin terms, you are working with a blend of complex and complicated approaches - once things are more clearly understood, it becomes easier to identify chunks of work that are more amenable to traditional engineering approaches. The additional benefit of this, in my experience, is that the resulting systems are more flexible, because the context is understood more clearly. So subsequently, some things that were not part of the original requirements become very easy to do.

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    1. Thanks Sally - it is a blend, but requires the CAS approach at the outset. Nothing wrong with traditional engineering approaches at the right time.

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  3. Interesting post and resonates with my view - we hope that the future is predictable but it isn't, no matter how much data we have or how mature our models are
    [as an aside to this I recently heard a talk from the UK Met Office CIO who emphasised how hard predicting the weather was despite the enormous amount of data they now have, massive supercomputers and the maturity of physics models].

    I think that "iterative strategic planning" as you sort of allude to this being is a very good thing as, apart from anything else, it engages the right people in the process rather than them going about their day jobs in their own silos. It's a sort of lean startup type of approach - what's a minimum viable strategy we can have right now?

    In my world, operational pressures can easily crowd out strategic thinking especially if it is perceived to be too complex.

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    1. Thanks for the comment Martin. We were living: "Operational pressures can easily crowd out strategic thinking especially if it is perceived to be too complex" - but making incremental progress now.

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  4. This from @kimparker054 on LinkedIn: The problem is not that you can't predict outcomes but that you do predict outcomes. The accuracy of the prediction varies with the quality and quantity of the data that is available. A prediction is not the same as a guarantee. The approach described can help.

    and my response:

    Thanks for the comment Kim. We probably agree: its the quality of the prediction (rather than the complete lack of one). We have found making the degree of certainity of our predictions over time explicit has helped to keep discussions 'honest' and avoids often unhelpful 'Big Design/Plan Up Front' thinking, which in turn helps us think more about reslience (change will happen, so design it in) and improves agility by comceptually unbundling both business & technology components. I'd be interested to hear if you have taken a simlar approach or what else you might have in the 'big change' toolkit!

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