Eiffel Over

Tag: ConvergentThinking

  • When optimisation is a good idea

    There are times when optimisation is a good idea. For example:

    When the technology involved is mature. With a rapidly changing technology, process optimisation may not keep pace with technological evolution.

    When the environment is stable. It is easier to optimise a structure for a prevailing wind than for blustery conditions. 

    When customer behaviour is constant. If customer demand is broadly unchanging, then we can optimise around how we carry on giving them the same thing.

    When you have good feedback. This is critical. Without good feedback from the system you are operating in, you don’t know if what you are putting into that system is meeting your aims. And you can’t see if the system conditions are changing.

    When there are no disruptors. These disruptors could be technological. Or they could be a group of engineers (or other humans) with an approach that is changing things up. It is too late for optimisation when no one needs what you are offering.

    In short, optimisation is good when the conditions are steady. 

    But if our operating environment is changing, then we need to dedicate at least some of our resources to asking, do we need a different approach?

  • The trap of the same way as before

    It is easy to do something the same way we did it before. 

    The previous time acts as a guide.

    Using the same approach as last time gives us something to improve on. We can see the shortcomings and improve on them. 

    We need to do less mental work when we use a tried and tested and optimised approach.

    Using the same way as before avoids us challenging whether the way we’ve been doing things for all these iterations is still fit-for-purpose.

    The same way as before gets us out of difficult negotiations with the other people who are also invested in the same-way-as-before approach.

    And it avoids us having to do the hard work of imagining and creating something different.  Something better. Something more appropriate. Something that the system we are working in needs more than the same way as before.

  • A suboptimal walk in the hills

    Here’s a made-up story I usually tell in our How to Have Ideas workshops at Constructivist. It is a story from the distant past when humans lived without phones in their pockets…

    Imagine you and a friend agree to meet for a walk in the hills. You agree to meet at the bench on the top of the hill nearest the carpark. It’s easy to see from the carpark, and it is a nice spot to sit and wait for each other. 

    But when you arrive, you find a thick fog has descended upon the valley. You have no idea which way the hill is. So you start walking and notice that you are going uphill. Feeling encouraged, you carry on finding your way through the fog by following the line of steepest slope. Eventually, this method brings you to the top of the hill, where you see the bench and sit down. 

    The fog begins to clear.

    Gradually you begin to see that on the other side of the carpark there is another hill, with another bench on it, and there is your friend waving back at you.

    Ok, it’s quite a crude story, but it serves as a lesson in constant optimisation. Sometimes, following the line of steepest slope – making what seems like the optimal decision at every step – only gets us to the top of the nearest hill. But to reach the top of the tallest hill we might need to stumble around in different directions in the fog before the best way forwards becomes clear. 

  • Convergent poem

    Zero in

    Figure out

    Tidy up

    Manage down

    Validate

    Mitigate

    Prioritise

    Optimise

    Strip it back

    Keep it clear

    Make the risks 

    All disappear.

    These all sound like good things to do on a project, and are what engineers (and other humans) spend a lot of time being trained to do. And it makes sense – we manage projects that come with big risks and sometimes big budgets. 

    All of these processes are forms of convergent thinking: ways of working that take a situation with many possibilities, inefficiencies and uncertainties and reduce it to something more refined, more singular, more known.

    This approach is fine if what you are starting with contains the elements of the right answer. You can take some approximately right answers and iteratively improve them to make them better and better. 

    But if the starting point isn’t the right approach, optimising it won’t make it better. You just make the wrong answer more efficient.

    So we need to balance convergent thinking with divergent thinking that opens up the problem, that sees what else might be possible. But first, let’s go for a walk…[to be continued tomorrow].