At the beginning of this module we define 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 a very micro level, we are now taking more aggregate level perspective. We will again look at the U.S. airline industry. We define labor productivity as a ratio between revenue and total labor expenses. Beyond 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 the ratio between output and input often time 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, capital, 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 the labor productivity as a ratio between revenue and labor expense. Let's try this out for the U.S. airline industry. In 1995, U.S. Airways had 6.98 billion dollars 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. 16 years later, the numbers have changed, revenue had grown to 13.34, with the labor expenses being at 2.41. This has increased the labor productivity thus to 5.53. Notice that labor productivity in those 16 years at U.S. Airways more than doubled. The situation at Southwest looks as follows. In 1995, their 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 U.S. Airways had at that time. 16 years later, Southwest was able to grow its business to 13.65 billion dollars. However, later expenses also grew significantly and were now 4.81 billion dollars, creating a labor productivity of 3.26. Which is actually slightly lower compared to the current number at U.S. Airways. But what does the 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 account for the difference. Consider again out 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 costs as the revenue divided by the revenue passenger miles created by the airline, times 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 cost. 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 airlines pricing power, for that reason, often times this is what's called the yield. How much can we yield? How much do we get out of the capacity that we have available? The revenue passenger miles divided by the available seat miles really measures to what extent we are able to fill our aircraft. In many ways this is a form of utilization 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 a four different things. I cannot go to an employee at U.S. Airways and say, hey, your labor productivity is slow, just because the pricing has been done poorly or the aircraft has been flying half empty. With this is mind, breaking up the aggregate level of productivity into these smaller drivers is quite revealing because it tells you what really is going on in the operation. Let's apply our new knowledge by going back to the U.S. Airline industry and compare the labor productivity across the big carrier. We said the rate of labor productivity was driven by the ratio between the revenue and the labor cost. For the case of American Airlines, we're dividing the revenue, divided by the labor cost, and we can copy this to the cells of the other carriers. We notice quite some variation 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 then look into the drivers of this effect, we compute the yield as a 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 passenger miles. And we roll this out across all the carry outs. You notice that the companies obtaining the biggest prices in the industry are the legacy carriers such as United or U.S. 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 word, the aircraft utilization. RPM by ASM turns out to be relatively constant across the carriers, with most carriers booking the airplanes up to about 80% utilization. Now consider the ratio between the ASN, which is really the capacity that has been provided and the number of employees. So ASN divided by employees. If we 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 wage 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 15 years ago, where Southwest was paying much lower wages than their competitors. Most of the legacy carriers had gone through bankruptcy and restructuring and by now are paying 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 accurate level data can be easy, however it also can be misleading. As we saw with the data in the U.S. Airline industry, many variables drive labor productivity. Labor productivity is not just under the control of the labor, but things such as the pricing, the fleet utilization have a direct impact on labor productivity. Productivity ratios allow you to take care of these confounding effects and only measure the value of the productivity that you care about. In general in your work I 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 productivity ratio, compliment this with observational data from the front line. Look at the operational data and aggregate them using the tools such as the KPI tree that we saw in an earlier session in this module to look how they are driving financial performance. This is where you get a balanced view of your productivity in the operation.
