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第 3 单元班次 8 视频课程
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运营管理概论（中文版）
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从本节课中
第 3 单元 - 生产率
您将在本模块中学习生产力的主要构成。首先，课程将介绍生产力的学术定义，然后介绍常见阻碍生产力的因素、生产力的测量方法、效益指标、改进生产力技巧和经济价值。本模块教学结束后，您将掌握如何识别、描述和测量输入和输出之间的关系，您还将能够设计策略，减少输入和提高输出，从而提高生产率。
与讲师见面
Christian TerwieschAndrew M. Heller Professor at the Wharton School, Senior Fellow Leonard Davis Institute for Health Economics Co-Director, Mack Institute of Innovation Management
The Wharton School
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.
