How is Yield Management Implemented in Airline Industry?

Capacity management is a key contributor to the success of any company. The tools and techniques that are used for capacity management within a service provider are completely different than a manufacturing-based environment as discussed here. In airline companies in specific, the relationship between supply and demand in addition to all the characteristics that shape this industry resulted in using Yield Management as the capacity management approach as shown here. Previously, the concept was illustrated thoroughly in this article, and this one will be the last step in understanding the yield management in the airline industry.

Before getting into the details of capacity management in the airline industry, it is important to emphasize that the timescale of capacity-related decisions is usually split over three stages: long term, medium term and short term capacity planning. The differences between them and examples from airliners are shown below.pic 2.jpg

Whether it is long, medium or short term capacity planning, the purpose of the decisions is to achieve a balance between supply and demand using solo or usually a Mixed Plan between three capacity management strategies; level, chase and demand management. The three strategies and examples from airliners are shown below.pic 3.jpg

It is worthy to mention that capacity management in an alliance partnership, where a group of airline companies agreed to share their capacities under a certain contract, is different than single airline capacity management (Graf & Kimms 2013).

Alliance partnership is defined as:

An agreement between multiple independent partners to collaborate in various activities to streamline costs (e.g., by sharing sales offices, maintenance facilities, ground handling personnel, check-in and boarding staff, etc.) while expanding global reach and market penetration” (Hu et al. 2013).

It is also defined as:

Combine their flights through code sharing. Code-sharing agreements allow partner airlines within the alliance to offer a flight operated by one of the partners as a product of another partner airline (Graf & Kimms 2013).

The benefits from such an alliance agreement from a capacity stand point is that improving the ‘load factor’ where the partner company represents an extended network that can be managed via a combined flight schedule (Graf & Kimms 2013).  In order to distribute the seats between the partner companies, Graf and Kimms (2013) suggested using a method called “option- based method with booking limit improvement through stochastic approximation and transfer price optimization”.

The next discussion will be dedicated to the yield management for a single airline company.

Yield Management in a Single Airline Company

As illustrated previously, the purpose yield management is to get the maximum revenue and load factor for each flight (Belobaba et al. 2009, p.89). Load factor is a measure of the average percentage of the filled capacity (seats) in the flight (Mack et al. 2013, p.12).

This goal is well-understood by addressing the cost differentiation strategy that airliners implement where they manage their inventory of seats by offering different fares for the different customer groups (Slack et al. 2013, p.342). Yield management then works by setting different booking limits to control the maximum bookings on low fare seats and providing a larger room for high fare seats (Belobaba et al. 2009, p.88). Earlier, this process was conducted using linear programming models (Mack et al. 2013, p.6), but later, different computerized systems that include data base supported by mathematical modeling are being developed (Belobaba et al. 2009, p.89; Lemke et al. 2012). The development of these systems was commenced in three generations as shown below.pic 4.jpg

A schematic diagram for the third generation of the airline revenue management system with the different types of input data is shown below. Please note that RM stands for Revenue Management.pic 5.jpg

According to the figure above, the computerized system will perform the forecasting process based on historical bookings, no-show data and actual bookings till date, then it will facilitate the two pillars of yield management:

  • Booking limit optimization which is the point of time that determines when to close the booking limit of a class and is calculated based on historical data and forecasting models (Belobaba et al. 2009, p.91).
  • Overbooking management which means accepting bookings that exceed the available capacity of seats on a flight because there are expected no-show passengers (Belobaba et al. 2009, p.91). When the actual number of passengers is larger than the available seats, the airline company will ask volunteers to swap their tickets for another trip and get additional credit for another flight. If no enough volunteers are available, the passenger will leave the flights with a pre-determined compensation (Huefner 2015, p.84).

The uncertainty associated with the overbooking and booking limits are calculated using mathematical models such as Expected Marginal Seat Revenue (EMSR) approach (Belobaba et al. 2009, p.95). The two pillars of yield management are needed in order to ensure the maximum revenues out of the fixed plane’s capacity (Slack et al. 2013, p.341).

Yield management: issues and limitations

The applications of yield management resulted in generating huge revenues for airliners, yet the approach has been criticized due to different issues such as:

  • “Ethical dilemma” in terms of customer dissatisfaction and loyalty: using different prices for the same seat may create customer dissatisfaction and may reduce their perception in getting fair prices. In yield management, the actual parameters that determine the final selling prices are not well-known by the customer. As a result, customer’s loyalty might be affected negatively (Huefner 2015, pp.101–103; van Ryzin & Talluri 2005; Yousef 2007; Krajewski et al. 2013, p.563).
  • Transparency: the calculation of yield is very important to airliners, however, there is a lack of clarity on how the data actually compiled which might cause a lack of confidence in the final numbers either at customers’ side or the practitioners’ side (Mack et al. 2013, p.12).
  • Internal resistance to adapt: the internal management of airliners might resist adopting the yield management practices due to the lack of skills and experience needed to deploy it and get the maximum benefits of it (Yousef 2007).
  • Complex infrastructure: effective yield management require complex and expensive computer systems, so management might be reluctant to implement it (Yousef 2007).
  • Employees’ conflict and dissatisfaction: the implementation of yield management requires changing in the current organization’s culture and structure. Moreover, extensive training is needed to all employees so they can understand their role in this new system. Finally, yield management may create stress and conflict between departments when dealing with issues such as overbooking, price differentiations…etc (Wirtz et al. 2001).

In order to put the previous discussion in a contemporary context, a case study about three airliners is available here. The companies are Royal Jordanian Airline, American Airliners and Easy Jet. The first two companies were selected as examples for ‘full-service carrier’, while the last one was selected as an example for ‘low-cost carrier’. Accordingly and with reference to the previous articles about capacity management in the airline industry, four set of critical success factors can be identified as shown below.

Critical Success Factors for Capacity Management in Airline Industry

With reference to the three case studies, fours critical factors are needed for a successful capacity management in airliners, which are:

  1. Yield management: yield management plays a key role in capacity management for industries that have a perishable inventory, utilize a reservation-based demand system, operating with a high fixed cost and a market that is divided into segments. The above conditions are available in the airline industry; therefore, yield management is a critical aspect in that industry.
  2. Managing stochastic demand: The demand in the airline industry is affected by many external conditions that lead to a stochastic demand. Analysing these external factors- such as politics, economy and diseases – is crucial so companies can put the right response and right capacity in place.
  3. Long term capacity planning: the capacity in the airline industry is measured by ‘seats’ which reflects the size of the aircraft at a wider context. Given that airliners cannot change the number of seats in each aircraft easily, optimizing the load factor per each flight is very important. Additionally, the lead time that is needed to produce an aircraft by the OEM (Original Equipment Manufacturer) could be several months, therefore, long term capacity planning and determining the needed capacity in terms of addition/ deletion of the aircraft is critical.
  4. Alliance agreement and airline subsidiaries: airliners can improve their load factor by getting into alliance agreements and subsidiaries partnerships where shared capacities can be managed using Code-Sharing. These approaches reduce the fixed cost needed for capacity expansion and the risk that is associated with demand fluctuation. As a result, alliances and such partnerships are important for a successful capacity management.


Belobaba, P., Odoni, A.R. & Barnhart, C., 2009. The Global Airline Industry 1st ed., Sussex: John Wiley & Sons, L td.

Graf, M. & Kimms, A., 2013. Transfer price optimization for option-based airline alliance revenue management. International Journal of Production Economics, 145(1), pp.281–293.

Hu, X., Caldentey, R. & Vulcano, G., 2013. Revenue Sharing in Airline Alliances. Management Science, 59(5), pp.1177–1195.

Huefner, R.J., 2015. Revenue management: a path to increased profits [e-book] 1st ed., Business Expert Press.

Johnston, R., Clark, G. & Shulver, M., 2012. Service operations management [e-book] 4th ed., Harlow: Pearson Education.

Krajewski, L.J., Ritzman, L.P. & Malhotra, M.K., 2013. Operations Management: Processes and Supply Chains 10th ed., Harlow: Pearson Education Limited.

Lemke, C., Riedel, S. & Gabrys, B., 2012. Evolving forecast combination structures for airline revenue management. Journal of Revenue and Pricing Management, 12(3), pp.221–234.

Mack, R., Jiang, H. & Peterson, R.B.M., 2013. A Discussion of the capacity supply – demand balance within the global commercial within the global commercial air transport industry, Available at: http: // Last viewed August 2016.

van Ryzin, G.J. & Talluri, K.., 2005. An introduction to Revenue management. Tutorials in Operations research in INFORMS, pp.142–194.

Sasser, W.E., 1976. Match Supply and Demand in Service Industries. Harvard Business Review, 54(6), pp.133–140.

Slack, N., Jones, A. & Johnston, R., 2013. Operations Management 7th ed., Harlow, England: Pearson.

Wirtz, J., Ho Pheng Theng, J. & Patterson, P., 2001. Yield Management: Resolving Potential Customer and Employee Conflicts,

Yousef, D.A., 2007. The Status of Yield Management in Service Organizations in the United Arab Emirates: Results of a Survey. Journal of Business and Public Affairs, 1(2), pp.1–9. Available at: http: //

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