Seeing red

For instance, I was doing a press pass at a big printing company and we were talking about colour reproduction and checking colour accuracy. The director told me about a problem they’d had a while back when all of their work was coming out too red.

Now with modern printers (where just one press can cost £3,000,000), colour accuracy is exemplary. And expected. So it’s a whacking great problem if you, the major printer, are suddenly producing work that’s too red. Especially when you don’t know why. Is it the inks? The paper stock? The press computers? The guys operating the press? Too much ink? Too little? The way it’s drying? A problem with the varnish?

In the end, by going through every step of the process and examining them individually, they discovered the problem was … meat lamps.

Let me explain. Printers use viewing booths with special lighting so they can check colours accurately by eye. Your normal office strip lights just aren’t clean enough. Supermarkets use special lighting too, in their meat refrigerators. Lights that emit a lot of red. To make the meat seem … redder. Meatier. The tricksters.

And by mistake somebody had fitted the print viewing booth with those meat lamps. So the work was printing fine. But when they looked at it in their special booth designed to show the work most accurately, the red lights meant they were seeing it the least accurately.

A simple mistake. But one that had people seriously worried. And which could have been very costly.

Now, that problem could have been solved in a number of ways, I’m sure. But it seems to me that they did it the most sensible way: they went through every stage step-by-step and found that there was a genuine, rational issue (the wrong bulbs), which they then swapped for the right ones.

So if we’re going to be simplistic about it, this tool is a bit more left brain than some of the others. And by trialling both left-brain and right-brain approaches, you’ll get a wider range of solutions to choose between.

The first two steps

Break the problem down. Discuss it, examine it and write it out in a detailed step-by-step manner. Perhaps there’s someone in the group who is particularly good at teasing through the detail who could lead this action.

Then, interrogate each of those individual parts. Study them, discuss them, understand them and break them down further if needs be.

Now it should be clear which elements hold the issues. It might just be a tiny detail of the process, a small aspect of an individual, a discrete advantage held by the competition.

The problem now feels more manageable. It’s not a big problem like, ‘The competition has a much better product’, it’s ‘The competition has a product which has these three features ours doesn’t have, features which appeal most to people aged 50–65’.

That kind of detail you only get from rigorous interrogation and it can be critical to how you solve the problem.

In the case I’ve just mentioned, you know just to add those three features. Or stop marketing to 50–65-year-olds. Or think of a new feature that appeals to 50–65-year-olds more than those three features would.

Of course, traditional problem-solving models might start with an approach like interrogate and extrapolate: really studying every contributing element of the problem and analysing it. The issue I have with that is that you can quickly lose the holistic ‘let’s solve the big issue in one staggeringly impactful way’ view, because considering the problem in small pieces can encourage you to just tinker. It also takes up a lot of early energy and effort. That’s why I recommend you use the interrogative approach as a tool in its own right, rather than as an obligatory part of the set-up.

The final step

Once you’ve interrogated the problem and identified the specific issue – the faulty parts, if you like – extrapolate the solution. In other words, discuss the fix, mentally put it in place and run through the scenario again, checking that having corrected those smaller issues, the whole thing works and the problem’s solved. It’s rather like the zany, evergreen board game Mouse Trap, if you know that. You fix the position of the diver so that when the marble hits the board he leaps up and lands in the tub rather than just missing it … and suddenly the whole operation works smoothly from start to finish. You trap the mouse and win the game.

Using this tool in a group session, with a mix of people who have different insights, levels of experience and a range of skills, can be really useful. You’re discussing the aspects of the problem in detail, and by breaking it down into discrete elements everyone can understand, an ‘outsider’ can come up with a way of fixing that one little cog that will get the whole machine working again.

Zip it

If you interrogate the flow of traffic, the data, the simple fact that while there are three lanes open it seems odd to have most of the traffic create an extra-long queue in one of them … you’d realise that there was a better way. That way being the late merge (or ‘zipper’ system), as used (by law) in countries like Germany and Austria.

There, you’re expected to keep using all the lanes. Instead of being frowned on for not joining the slow lane a.s.a.p., you stay where you are. Then, when you get to the point where the road actually closes, everybody adopts a ‘zipper’ approach. So if you’re in the slow lane, you let one car in the middle lane filter in. Then you go. Then the car behind you lets one in. And so on. Like a zip.

In fact, the zipper system doesn’t mean more cars get past the road closure in any given time – it doesn’t increase ‘throughput’. But it does reduce the length of the queue (it’s shorter because it’s wider) and it does reduce the difference in speed between the different lanes, which increases safety. And you’d imagine it reduces the stress levels of everyone involved too. Those in the slow lane don’t feel that cars in the other lanes are jumping the queue, while people in the other lanes don’t have to fret about trying to squeeze their way in, because they know they’ll be let in, like the alternating teeth of a zip.

Several tools in this book might have helped develop the same zipper solution. But interrogate and extrapolate would seem to be a good bet for it. It’s a logical problem, a matter of maths and physics – finding a way to maximise the available space on the road while creating a simple and fair system for drivers to deal with the obstacle.

It can work really well for you too. Just, as I say, don’t turn to it first every time, or you may find you get bogged down in too much detail too early. Because, to offer a conflicting point of view, as Malcolm Forbes (publisher of Forbes magazine) posits, ‘It’s so much easier to suggest solutions when you don’t know too much about the problem.’