At the beginning of this module, we defined productivity as a ratio between

output and input. After having spent the last couple of

sessions at the front line, looking at an operation at the very micro level, we will

now take a more aggregate level of perspective.

We will, again, look at the US airline industry.

We define labor productivity as a ratio between revenue and total labor expenses.

We aren't just defining the labor productivity and comparing it across

airlines. Our goal is to really dive into

understanding the drivers of productivity. We define productivity as a ratio between

output and input. Oftentime however, it is difficult to

measure output, so it is common in productivity analysis to use revenue

numbers instead. We also often have multiple input factors

such as labor, materials, capitals, and other things.

One way to avoid adjusting for these multiple categories is simply to define

one productivity ratio for each category. For example, we can speak about a labor

productivity as the ratio between revenue and labor expense. Let's try this out for

the US in airline industry. In 1995, US Airways has $6.98 billions of

revenue. Their labor cost was $2.87 billion.

This gives us a labor productivity of 6.98 divided by 2.87 equals to 2.43.

Sixteen years later, the numbers have changed, Revenue have grown to 13.34

whereas, the labor expenses being at 2.41, this has increased the labor productivity

thus to 5.53. Notice that labor productivity in those

sixteen years at US Airways more than doubled.

The situation itself looks as follows. In 1995, the labor productivity was given

by 2.87 billion dollars in revenue divided by 0.93 billion dollars for labor expenses

equals to 3.08 as the labor productivity. You see here that this was a significantly

higher number than US Airways had at that time.

Sixteen years later, Southwest was able to grow its business to $13.65 million.

However, labor expenses also grew significantly and went up $4.18 billion,

creating a labor productivity of 3.26, which is actually slightly lower compared

to the current numbers at US Airways. But what does a higher labor productivity

actually mean? Are the workers working any harder?

Have we squeezed out the idle time? Is the process underlying the operation

smarter than before? What really accounts for the difference?

Consider again our definition of labor productivity as a ratio between revenue

and labor costs. Now, work with me through the following

equation. We can rewrite the revenue to the labor

cost as the revenue divided by revenue passenger mass created by the airline time

the revenue passenger miles times the available seat miles times the available

seat miles divided by the employees times the employees divided by the labor cost.

Now, you might be scratching your head here a little bit but at least you will

hopefully agree with me that mathematically this equation is true.

After all, this term cancels against this term, this term cancels against this term,

and this term cancels against this term and we are back to the initial expression.

Now, what is the benefit of writing the equation this way?

It reminds us that there are multiple things going on that are all driving the

labor costs, the labor productivity. We see here in the first factor, the

revenue to the number of miles that we sell, this is really driven by the

airline's pricing power. For that reason, oftentimes, this is

what's called the yield. How much can we yield? How much do we get

out of the seat capacity that we have available?

The revenue passenger mass divided by the available seat-mass really measures to

what extent we are able to fill our aircraft.

In many ways, this is a form of utilitzation of capacity.

Now, the last two of these ratios here are actually really touching the labor.

The first of these ratios is, how much capacity can we get out of each employee?

The second one looks at the cost of sourcing these employees which is

basically their wages. Notice that these four different ratios

catch up four different things. I cannot go to an employee at US Airways

and say, hey, your labor productivity is low just because the pricing has been done

poorly or the aircraft has been flying empty.

With this in mind, breaking up the aggregate level per activity into these

smaller drivers is quite revealing because it tells you what really is going on in

the operation. Let's apply the new knowledge by going

back to the US airline industry and compare the labor productivity across the

big carrier. He says, that labor productivity was

driven by the ration between revenue and the labor cost.

So, for the case of American Airlines, we divided the revenue,.divided Divided by

the labor costs, and we can copy this to the cells of the other carries.

We notice quite some variations with Frontier getting a labor productivity

ratio of six. And quite interestingly, Southwest being

at the bottom of the pack, with a productivity ratio of 3.2.

We'll then look into the drivers of this effect.

We compute the yield of this ratio between the total revenue, and the number of

passenger miles that were actually sold by the airline.

For this, again we look at the revenue divided by the revenue of passenger lines.

And you roll this out across all the carriers.

You'll notice that the companies obtaining the biggest prices in the industry are the

legacy carriers such as a United, or a US Airways.

Next, we look at RPM to ASM, which we said was the ratio between the miles that were

sold, and the miles that were available. In other words, the aircraft utilization.

Rpm by ASM turns out to be relatively constant across the carriers with most

carriers booking the airplanes up to about a 90%,, 80% utilization.

Now, consider the ratio between the ASM, which is really the capacity that has been

provided, and the number of employees. So, ASM divided by employees.

If you compare this across the carriers, We see that the leader in productivity on

that side is clearly Virgin America. Southwest though, does relatively well

compared to its immediate competitors, the Legacy Airlines.

As a final ratio, we'll look at the FTEs relative to the labor cost.

That measures how many people I can hire for a given amount of money.

Technically, this is simply one over the weight rate of the employees.

Well, when we divide FTE divided by labor cost,

We see surprisingly high differences in salaries across airlines.

For example, you notice that Southwest by now pays its employees really, really

well. Remember, you have to take one over this

number to get to the actual wages. This is in sharp contrast to how it was

some fifteen years ago when Southwest was paying much lower wages than their

competitors. Most of the Legacy carriers have gone

through bankruptcy and restructuring and by now are paying their employees

significantly less. All these four variables together explain

variation and labor productivity. So, when we say that one company has a

higher labor productivity than the other, we really need to be careful in

distinguishing between these four forces. Measuring productivity using aggregate

level data can be easy. However, it also can be misleading.

As we saw with the data in the US airline industry,

Many variables drive labor productivity. Labor productivity is not just under the

control of the labor, but things such as the pricing of the fleet utilization have

a direct impact on labor productivity. Productivity ratios allow you to take care

of these confounding effects, and really only measure the value of a productivity

that you care about. In general, in your work, I would always

encourage you to take two approaches. First, look top down.

Start from the financial and work yourself down into the operations using tools such

as a productive inter ratios. Complement this with observational data

from the front line. Look at the operational data and aggregate

them using the tools such as a KPI Tree that we saw in an earlier session in this

module to look how they're driving financial performance.

This way, you get a balanced view of your productivity in the operation.

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