We're going to start the segmental forecasting now. And first let's get an overview of the segments of Kion group. We have ITS as it's called and this might also be called OE or OEM. And this is where they manufacture new trucks and then deliver them to the customer.

We've then got services which is still within ITS and is distinct from the OEM. This might be called AM, aftermarket. And this is the services which would be performed after the sale of the truck. So for example, customizing the truck post-sale or maybe repairing it.

We've then got an entirely distinct segment, so those were sub-segments of ITS that we were just looking at. And we have a distinct segment called SCS, Supply Chain Solutions. And this is the area of the business which is more to do with warehouse automation. So potentially a newer area of the business for Kion.

For some areas of the business, we're going to do detailed backlog analysis and that's because backlogs are very common in industrial companies. So for example, historically we can see that there is a order intake which increases the opening backlog and then there's revenue recognized as the trucks are sold and delivered and this reduces the backlog.

We can model that forwards using the forecast order intake. We can let the opening be the last closing.

We can then model next year's order intake by saying it will be a modification to last year's order intake.

I multiply that by last year, that will complete that. Now notice orders have definitely slowed down since 2022, and that's because that is still part of the post and during pandemic disruptions to supply chains. And so industrials tend to get large orders when things are perceived to be going wrong. And this is what's happened to Kion. They got these mega orders where companies were ordering lots of trucks because they were anticipating problems and they were trying to get higher up in the backlog list. That's gone away a bit, okay, definitely in 23 and as a softening generally of new orders stabilizing to about 5% increase.

Now to get the revenue recognized, we actually have to forward predict the revenue, which we're gonna do down here. And you can see that we're predicting a very modest increase in revenue.

And we can then migrate that as a negative figure above.

We can then let that equal carefully the closing backlog. Now you can see that the backlog has taken a nose dive in 2024 and in fact it's still, it's already starting to do that in 2023. And this is very interesting because this could indicate a lot of things.

New trucks are generally a short cycle project or product. They tend to get delivered within about six months historically for Kion. So a customer will place their order, they'll be on a backlog for about six months and then they will get their order. What we're seeing within these years is we are seeing a real change in that relationship where Kion is reducing the length of the cycle, the length of time it takes to clear the backlog. And what we're seeing is Kion reducing their cycle length, so their backlog length from about six months to four months. And what this does is it starts to drastically empty the backlog within these years. And so although we have a real nosedive in terms of new business order intake in 23, we still have a real growth in revenue as they start to sell more of their backlog quicker. We can also see that relationship in the book-to-bill and that formula's already been built for us. So we can just copy that to the right. You can see that in 2022, the revenue can't keep up with the orders, which means a growing backlog.

You can see that in 2023, revenue is outpacing the orders without being represented by being below 100%. And this would represent then an emptying out of the backlog, which is what we are seeing in these three columns in the final row there. If we copy everything forwards, you can see that that starts to stabilize.

The revenue growth will slow down in 2025. Now this is a function of them stopping their tightening up of the backlog length from six months to four months. It's no longer possible to get that any lower. There are just constraints on how fast you can clear the backlog and produce. And so it means that the revenue growth really slows down in 25 and then starts to settle as the new policy takes into effect. And we're no longer seeing a change in the cycle length, but we are seeing just general growth of the business or pricing or mix. You can see that we have a slightly higher value of the orders because this is values, this isn't volumes. And that translates to a book to bill of just over 100%. And that makes sense because the revenue is slightly lagged. This represents slightly older sales, and the new business is slightly newer sales. And if they're at inflated prices, you would expect a book-to-bill to be over 100%.

So in conclusion there we've had a situation where there are these historical mega orders they've eased off, which gives us a chance to tighten up our cycle length and reduce the backlog, which increases the rate of revenue growth that then slows down, and then we reach a kind of more normal state going forwards.