DATABERG
Have you ever wondered airlines handle the rapid changes in business over the calendar year?
Anyone who has ever travelled during peak times knows that airlines process huge volumes of people during short period of time. The airline industry actually flew 17% more passengers in July relative to all other months. This requires methods for managing variable demand.
A recent trend has developed in which airline companies are managing their demand in new and innovative ways. This is called operational response. In operational response, airlines use data analysis to fine tune their capacity in accordance with historical demand. This replaces the previous method, in which airlines would rely on discounts and promotions to fill excess supply during lulls in the off-season.
Demand Management in a Nut Shell
The off-season plays an important role in airline operations. During lower travel periods, airlines can schedule their fleet for cabin work and maintenance. Airline employees often take time off and fly fewer hours per day and to fewer destinations. Especially expensive trans-Atlantic and long-haul international flights are trimmed down during the non-summer busy period. The fewer seats increases ticket prices to a stable place during the off-season. Running fewer flights also helps save money on fuel, which is fast becoming a major drain on resources. Fuel costs now make up over a third of airlines’ operating expenses, making conserving energy especially important during slower months.
This may seem like an obvious solution. You may be wondering why this is a new strategy rather than a tried and true tactic. But what has driven this development is the necessity to match flight capacity to demand to save on fuel. Increases in the price of fuel have changed the unit costs of each seat flown, putting pressure on airlines to more precisely manage their supply. Fixed costs for most airlines have actually dropped, in some instances down to half of total costs. This is a substantial break from history, when fixed costs composed a large share of total expenses. This trend can be blamed on consolidation in the sectors, with efficiencies through mergers, and the outsourcing of certain labor, including maintenance, food services, and airport operations.
As in many industries, labor flexibility plays an important role. This goes hand in hand with outsourcing operations to third party contractors. This move allows airlines to tap into labor pools as needed as a client of a third party. This makes it far easier to cut back on capacity. Remember that fixed costs are lower per unit when spread out over a larger set of units. When airlines cut their flight volume, that creates a smaller denominator, meaning higher fixed costs per unit. However, with contracted labor, the fixed become variable. Certain airport operations can be cut back during off seasons and ramped up during peaks. This means airlines can scale demand up and down at will without needing to provide big discounts to fill seats.
Demand management is a major revenue booster for the airline industry. But these tools do not exist in a vacuum. To realize these benefits, airlines must fine-tune their data analysis and forecasting resources. Analytics plays directly into the airline operations strategy. Though analytics play an important role in most industry operations, the large stakes of air demand management make it a very important investment for carriers.
Data processing an analysis is starting to change business practices for companies all over the world. Large amounts of information are increasingly able to be processed and interpreted using sophisticated tools and intelligent personnel. Though much of this information is used for marketing and promotion, operations optimization is a key benefit. The airline industry’s trajectory shows how data intersects with other practices, such as employee contracting and consolidation, to address rising fuel and fixed costs. This is an interesting case study in airline management. But it also demonstrates a less known role for date analytics in forecasting.
