Financial Performance of Airlines Through Open Data Sets
The aviation sector, after years of underperformance, has been bullish of late as evidenced by the turnaround of Spicejet’s fortunes and a strong response for the Indigo IPO. This turnaround has led to a lot more interest in the aviation sector in recent times. In this article, let us understand how operational factors affect the bottom-line of an airline and the use of open datasets to estimate these factors. An airline’s operational factors can help us understand which airline uses its fleet efficiently, which airline has an optimal route network or which airline’s fleet is optimized for fuel efficiency.
Open data sets is the collection of a set of data which are openly available to the public such as the ones released by ministries, regulatory authorities, websites for general information etc. Examples of these in aviation sector are the data released by department of transportation, aviation regulatory authority and airports authority.
Taking Cues from Operational Factors
The operational revenue of an airline comes from Passengers, Cargo and Leasing for most airlines, with the passenger segment contributing about 85% of the revenue for most airlines. In a simple sense, the passenger revenue depends on how many passengers fly and how much they pay for each seat. The standard measures to compute available capacity, capacity utilized and the amount spent by a passenger, are Available seat kilometers, Load factor and Yield respectively.
The capacity available depends on the airline’s fleet capacity and its route network. This, in turn depends on fleet size, aircraft utilization and seat density of the aircraft. The airlines have to make sure that capacity is utilized efficiently without sacrificing the airfares in order to maintain profitability.
The passenger revenue consists of two components: airfares and ancillary revenues (revenue from other services to passenger). Understanding the sensitivity of the yield to changes in the average airfares of an airline will help understand the passenger fare elasticity towards an airline. An airline that is fare inelastic will have lesser control over the fares it can charge. In addition to this, the ability of an airline to derive revenues from ancillary services will determine the airlines ability to stay ahead during fare wars.
The biggest operational expense of an airline generally is the Fuel expense. Airlines have benefitted hugely due to the subdued fuel prices in the last year. It is important to understand the sensitivity of the fuel prices on the fuel expenses of the airline. Understanding the sensitivity will help comparing how a dollar change in fuel price will affect each airline and up to what level of fuel prices can the airline sustain its profits. The composition of the fleet has a big impact on the fuel sensitivity.
Joining the dots : Case Study for Spicejet
Let us look at the turnaround of Spicejet’s fortunes during the last year from an operational perspective. A Break-even load factor is the load factor (capacity utilization) at which the passenger revenues equal the operational expenses for the company. A load factor above the break-even load factor indicates that the airline earns more passenger revenue than it spends for the operations. The break-even load factor lagged the load factor in the third and fourth quarters of 2014. The turnaround in 2015 from a loss of Rs.310.45 Crores in the third quarter of 2014 to a profit of Rs.23.77 Crores in the third quarter of 2015 can be observed through a comparison of load factor vs break-even load factor. The drop in Break-even load factor is primarily attributed to a drop in fuel prices and an increase in the Load Factor due to a revamp of the operations of Spicejet.
The break-up of Cost per available seat kilometer (CASK) in comparison with Aviation Turbine Fuel (ATF) prices reveals the extent of the impact of fuel prices on the operational expenses of an airline. In the case of Spicejet, it can be seen that the fuel costs have been the major influence in decrease of the load factor required to break-even. Every seat filled above this break-even factor contributes directly to the profits of the airline.
Recent Real-Life Example through Open Data Sets
Such operational factors can be estimated with the help of the huge number of open datasets available even before the official numbers are released. The reason why Open data sets can be a powerful tool is because the impact on the company’s financials that can be estimated on the go.
Open datasets could be used to estimate how change in route network, cancellations, frequency of flights, airfares, aircraft utilization etc. affects the profits of an airline. Let us take the real-life example of the impact of Chennai rains on different airlines this year. We would like to know how much capacity was and in turn, how much revenue was lost due to the cancellation of flights. Using airline flight route and seat density data, an estimate of the available seat kilometers can be found for a particular time period. With an estimate of the load factor by taking into account, the trend and the seasonality, we can calculate the revenue loss for each route due to the cancellations.
Spicejet makes 44 flight trips, which originate or land in Chennai in a week on an average. The total seat capacity over a week is estimated with the help of fleet capacity and route data. Our estimate shows that 5329 seats are available in a week on an average, which translates to potential revenue of Rs.14.19 Crores per week. However, when the load factor estimate (Calculated based on trend and seasonality variations) is taken into account, the potential revenue for a week comes out to Rs.10.54 Crores. The estimated expense, which is calculated based on Cost per available seat kilometer and total available seat kilometers for the time period comes out to Rs.10.01 Crores resulting in a loss of Rs.0.53 Crore in profits. A similar analysis for Jet Airways leads to a loss in revenue of Rs.16.15 Crores (Loss in revenue does not take additional maintenance and cancellation charges paid , into account). It can be seen that Spicejet derives a higher percentage of its revenues from departures from Chennai compared to Jet Airways.
The rains will just have a short-term impact on revenues of the airlines. Keeping track of operational parameters using open datasets help in estimating the business operations at a more granular level than ever before. Apart from the impact of these exceptional events, the open datasets can also help in predicting any fundamental shifts in operations of a company.
Stay tuned for more on financial cues from open data sets. Write to us at firstname.lastname@example.org for any further questions.