Month: May 2013

Improvement Science is all about making some-thing better in some-way by some-means.

There are lots of things that might be improved – almost everything in fact.

There are lots of ways that those things might be improved. If it was a process we might improve safety, quality, delivery, and productivity. If it was a product we might improve reliability, usability, durability and affordability.

There are lots of means by which those desirable improvements might be achieved – lots of different designs.

Multiply that lot together and you get a very big number of options – so it is no wonder we get stuck in the “what to do first?” decision process.

So how do we approach this problem currently?

We use our intuition.

Intuition steers us to the obvious – hence the phrase intuitively obvious. Which means what looks to our minds-eye to be a good option.And that is OK. It is usually a lot better than guessing (but not always).

However, the problem using “intuitively obvious” is that we end up with mediocrity. We get “about average”. We get “OKish”.  We get “satisfactory”. We get “what we expected”. We get “same as always”. We do not get “significantly better-than-average’. We do not get “reliably good”. We do not get improvement. And we do not because anyone and everyone can do the “intuitively obvious” stuff.

To improve we need a better-than-average functional design. We need a Reliably Good Design. And that is invisible.

By “invisible” I mean not immediately obvious to our conscious awareness.  We do not notice good functional design because it does not get in the way of achieving our intention.  It does not trip us up.

We notice poor functional design because it trips us up. It traps us into making mistakes. It wastes out time. It fails to meet our expectation. And we are left feeling disappointed, irritated, and anxious. We feel Niggled.

We also notice exceptional design – because it works far better than we expected. We are surprised and we are delighted.

We do not notice Good Design because it just works. But there is a trap here. And that is we habitually link expectation to price.  We get what we paid for.  Higher cost => Better design => Higher expectation.

So we take good enough design for granted. And when we take stuff for granted we are on the slippery slope to losing it. As soon as something becomes invisible it is at risk of being discounted and deleted.

If we combine these two aspects of “invisible design” we arrive at an interesting conclusion.

To get from Poor Design to OK Design and then Good Design we have to think “counter-intuitively”.  We have to think “outside the box”. We have to “think laterally”.

And that is not a natural way for us to think. Not for individuals and not for teams. To get improvement we need to learn a method of how to counter our habit of thinking intuitively and we need to practice the method so that we can do it when we need to. When we want to need to improve.

To illustrate what I mean let us consider an real example.

Suppose we have 26 cards laid out in a row on a table; each card has a number on it; and our task is to sort the cards into ascending order. The constraint is that we can only move cards by swapping them.  How do we go about doing it?

There are many sorting designs that could achieve the intended purpose – so how do we choose one?

One criteria might be the time it takes to achieve the result. The quicker the better.

One criteria might be the difficulty of the method we use to achieve the result. The easier the better.

When individuals are given this task they usually do something like “scan the cards for the smallest and swap it with the first from the left, then repeat for the second from the left, and so on until we have sorted all the cards“.

This card-sorting-design is fit for purpose.  It is intuitively obvious, it is easy to explain, it is easy to teach and it is easy to do. But is it the quickest?

The answer is NO. Not by a long chalk.  For 26 randomly mixed up cards it will take about 3 minutes if we scan at a rate of 2 per second. If we have 52 cards it will take us about 12 minutes. Four times as long. Using this intuitively obvious design the time taken grows with the square of the number of cards that need sorting.

In reality there are much quicker designs and for this type of task one of the quickest is called Quicksort. It is not intuitively obvious though, it is not easy to describe, but it is easy to do – we just follow the Quicksort Policy.  (For those who are curious you can read about the method here and make up your own mind about how “intuitively obvious” it is.  Quicksort was not invented until 1960 so given that sorting stuff is not a new requirement, it clearly was not obvious for a few thousand years).

Using Quicksort to sort our 52 cards would take less than 3 minutes! That is a 400% improvement in productivity when we flip from an intuitive to a counter-intuitive design.  And Quicksort was not chance discovery – it was deliberately designed to address a specific sorting problem – and it was designed using robust design principles.

So our natural intuition tends to lead us to solutions that are “effective, easy and inefficient” – and that means expensive in terms of use of resources.

This has an important conclusion – if we are all is given the same improvement assignment and we all used our intuition to solve it then we will get similar and mediocre results.  It will feel OK and it will appear obvious but there will be no improvement.

We then conclude that “OK, this is the best we can expect.” which is intuitively obvious, logically invalid, and wrong. It is that sort of intuitive thinking trap that blocked us from inventing Quicksort for thousands of years.

And remember, to decide what is “best” we have to explore all options exhaustively – both intuitively obvious and counter-intuitively obscure. That impossible in practice.  This is why “best” and “optimum” are generally unhelpful concepts in the context of improvement science.

So how do we improve when good design is so counter-intuitive?

The answer is that we learn a set of “good designs” from a teacher who knows and understands them, and then we prove them to ourselves in practice. We leverage the “obvious in retrospect” effect. And we practice until we understand. And then we then teach others.

So if we wanted to improve the productivity of our designed-by-intuition card sorting process we could:
(a) consult a known list of proven sorting algorithms,
(b) choose one that meets our purpose (our design specification),
(c) compare the measured performance of our current “intuitively obvious” design with the predicted performance of that “counter-intuitively obscure” design,
(d) set about planning how to implement the higher performance design – possibly as a pilot first to confirm the prediction, reassure the fence-sitters, satisfy the skeptics, and silence the cynics.

So if these proven good designs are counter-intuitive then how do we get them?

The simplest and quickest way is to learn from people who already know and understand them. If we adopt the “not invented by us” attitude and attempt to re-invent the wheel then we may get lucky and re-discover a well-known design, we might even discover a novel design; but we are much more likely to waste a lot of time and end up no better off, or worse. This is called “meddling” and is driven by a combination of ignorance and arrogance.

So who are these people who know and understand good design?

They are called Improvement Scientists – and they have learned one-way-or-another what a good design looks like. That lalso means they can see poor design where others see only-possible design.

That difference of perception creates a lot of tension.

The challenge that Improvement Scientists face is explaining how counter-intuitive good design works: especially to highly intelligent, skeptical people who habitually think intuitively. They are called Academics.  And it is a pointless exercise trying to convince them using rhetoric.

Instead our Improvement Scientists side-steps the “theoretical discussion” and the “cynical discounting” by pragmatically demonstrating the measured effect of good design in practice. They use reality to make the case for good design – not rhetoric.

Improvement Scientists are Pragmatists.

And because they have learned how counter-intuitive good design is to the novice – how invisible it is to their intuition – then they are also Voracious Learners. They have enough humility to see themselves as Eternal Novices and enough confidence to be selective students.  They will actively seek learning from those who can demonstrate the “what” and explain the “how”.  They know and understand it is a much quicker and easier way to improve their knowledge and understanding.  It is Good Design.