In this workout, we're going to analyze Boeing's backlog.

We're gonna do so to explore the ideas, terminology and calculations that you might be looking to do in your models.

You can see that the year that we're most interested in is FY 24, which has gaps and we've got FY 20 to 23 as historical.

We have an assertion here that says that 16% of the backlog is going to be converted to revenue in FY 24, and that's being mirrored by this figure here.

First thing we could do is go ahead and forecast the revenue in the next year.

If we have a backlog of about 520,000 and about 16% of that is going to convert, then we have conversion there into revenue of 83, 2 3, 1.

What's happening is this represents the backlog being fulfilled and turning into revenue perhaps in the next year.

If this is a long cycle business, which you would expect it to be given its Boeing and it's delivering aircraft, we've also been asked to work on FY 23 and the order intake and its growth rate, and then give a qualitative assertion about what that's going to do down the line.

To do this, we should analyze the backlog and create a kind of base calculation.

We know that the ending backlog in every year is as follows, and we can copy that forwards where we have available information.

We also know that in the years where we have revenue, that revenue would be being deducted from the backlog as it represents the conversion that we calculated earlier in the question.

We also know the beginning backlog because it represents last year's ending backlog.

What we can do now is fill in the blank, if not given by the company, we can say they started with 3 6 3, they ended with 3, 7 7, and along the way some of that backlog was converted into revenue, which means that the difference between all of these figures must represent the orders that they took for new planes in 21.

To add to the backlog, it stands to reason the orders must have been higher than the revenue recognized, given the backlog grew over the year.

Filling in the blanks, you can see the FY 23 was a very strong year for order intake and that without the analysis that we're currently doing, that would be difficult to analyze or see.

You might reasonably be asking whether this is an error, but what we're seeing is Boeing, uh, benefiting from a spike of demand post COVID.

As travel picked up again and it, it became clear, the markets would open up again and airlines sought to refresh their somewhat aging fleets to help us answer the qualitative question.

We could calculate the revenue growth rate and the order intake growth rate.

We can only calculate the revenue growth rate from FY 22, and that's because we need a prior year.

We'll need to say what is the difference and we've got a 6.9 difference there.

We can then do the same with the order intake.

You can see that there is a mismatch between the revenue growth rate, which is good and accelerating, and the growth rate in the order intake, which is higher and accelerating more.

It'll be useful to have, uh, a view further back for this because what we're probably seeing is a long delay in orders to revenue.

It makes sense for a very long cycle business like Boeing that when it produces good results in its order book like it is here, that we might reasonably see the result of that one or several years down the line.

And this is because we're looking in a very long cycle business.

And so being able to analyze the backlog, the order intake, or the order book as it's sometimes called, and its relationship with revenue and the kind of delays that you might see will really help us to understand and analyze companies in the industrial space.