Yield Management in Airline Industry

Service capacity management is completely different than the manufacturing one due to different considerations as shown here. In the aviation industry for example, which has many stakeholders and players, each one has a different measure for capacity management as discussed here. For airliners in specific, capacity is measured by the number of available seats (Slack et al. 2013, p.329), i.e. Available Seat Kilometers (ASK) or Available Seat Miles (ASM) (Mack et al. 2013, p.4).

As shown before, the best approach to for capacity management in airliners is Yield Management. In order to get a comprehensive story about this subject, 5W’s+1H analysis will be used. The 5W’s+1H is structured analysis method, usually used in journalism, in order to examine the details of an event using 5 questions that start with W: Who/ What/ Why/ When/ Where, and one question starts with H: How (Hart 1996).

The questions that will be answered in are:

  • What does yield management mean?
  • Why is it important?
  • Who started it?
  • Where was it started?
  • When was it developed?
  • How is it implemented?

This article will answer the first five questions while the last one will be illustrated in the next article.

 First: What does yield management mean?

Yield management is defined as the allocation of resources to meet customers’ demands where inventories cannot be built up in advance, therefore, it is called ‘Perishable Revenue Management’ or shortly ‘Revenue Management’ (Slack et al. 2013, p.341). It is also called ‘Customer Centric Pricing’ to describe how companies adapted cost differentiation strategy by offering different services with different fairs (Huefner 2015, p.9).

Within airline context, it includes a set of techniques to deploy pricing strategies for the various segments of customers. A trade-off between allowing overbooking in one fare or waiting for a potential customer for another fare is needed in order to maximize profitability (Mack et al. 2013, p.6; Marco et al. 2015).

The definition of yield is

“A measure of the average fare paid by all passengers per kilometer (or mile) flown, in a market, on a set of routes, or a region of operation for an airline” (Belobaba et al. 2009, p.48).

Yield’s Equation is:pic 2.jpg

Second: Why is yield management important?

Managing capacity, by optimizing the available part and reducing the unused one, is a vital aspect of any business. Some of the objectives of capacity management are addressed below (Coelli et al. 2002; Haerian et al. 2006; Slack et al. 2013, p.325):

  • Improving product quality through having the right set of resources especially during demand fluctuation.
  • Reducing response time to customer requirements by ensuring enough capacity is in place.
  • Ensuring the flexibility to meet the unexpected demand.
  • Reducing the costs associated with the underutilized capacity.
  • Generating more revenues were in the airline industry, utilizing yield management approach helps in maximizing the passenger’s revenue per RPK (Revenue Per Kilometre) (Belobaba et al. 2009, p.89).

Third: Who started the yield management approach?

In the past, airliners deployed capacity- related activities by operators who were responsible for setting the maximum bookings per flight via manual cards, and depending on pre-defined prices from the government (Belobaba 1987; Huefner 2015). Due to the increasing number of bookings and the hassle in keeping the manual process, scholars started to develop different mathematical models. The first seat-inventory based system was developed by Littlewood in 1972 (Haerian et al. 2006; Lan et al. 2008).

Fourth: Where was yield management started?

During the 1950s, seat inventory control methods in Airliners such American Airlines and Western Airlines were done manually. The first revenue management implementation started after that Airline Deregulation Act of 1978 in the USA (Belobaba 1987, p.182; Huefner 2015, p.20). Later, the same deregulation happened in the European Union in the 1990s (van Ryzin & Talluri 2005).

Fifth: When was yield management developed?

The evolution of the yield management discipline passed through different stages where the technology advancement alongside with the application of mathematic, operations research and simulation techniques and the deregulation acts have shaped the yield management discipline (Mack et al. 2013, p.6).

To provide a broader overview of the timeline for developing the yield management concept, below is a summary for the main events and scholars who shaped this concept.

  • 1950s: Seat inventory control methods in Airliners such American Airlines and Western Airlines were done manually (Belobaba 1987, p.182; Huefner 2015, p.20).
  • Prior 1978: Prices are determined based on industry average and based on the mileages during the flight. Mostly, the prices are controlled by the government which limited the competition between companies (Williamson 1992, p.13).
  • 1972: The first model to calculate seat inventory based on price-based assumptions was introduced by Littlewood (1972). The main idea was to accept the bookings in low-fare seats until the revenues of these low-fare seats exceeded the expected revenues of the high-fare seats (Haerian et al. 2006; Lan et al. 2008).
  • 1978: The deregulation act where governments reduced their controls over airliners. As a result, the airliners started offering seats via dynamic and flexible cost-based systems (Williamson 1992, p.13; Haerian et al. 2006).
  • 1982: Glover et al. (1982) proposed the first Deterministic Linear Programming (DLP) model to be used in revenue management (Haerian et al. 2006).
  • 1986: Wollmer (1986) proposed different algorithms to find the optimal allocation (Haerian et al. 2006).
  • 1987: Belobaba (1987) extended Littlewood’s idea to include more than the two- fare classes (Haerian et al. 2006; Lan et al. 2008).
  • The early 1980s: the first revenue management system which extracts information from computerized systems to determine the booking limits. Some decisions were still having to be done outside the computerized system (Belobaba et al. 2009, pp.90–91).
  • Mid-1980s: additional monitoring systems were added to those revenue management systems (Belobaba et al. 2009, pp.90–91).
  • The late 1980s:  the third generation of “automated booking limit system” was developed which facilitated the extraction of reports that help in the decision- making process (Belobaba et al. 2009, pp.90–91).
  • 1990: Curry (1990) proposed different algorithms to find the optimal allocation  (Haerian et al. 2006; Lan et al. 2008).
  • 1992: Wollmer (1992) developed a model for seat inventory control based on the distribution of the demand. The model was very complicated due to the need to a large number of decision variables (Haerian et al. 2006; Lan et al. 2008).
  • 1992: Williamson (1992) proposed a model using a ranking fare classes. The ranking system is based on the incremental revenue that expected to be generated if there are enough seats available for each class (Haerian et al. 2006).
  • 1992: Smith et al., (1992) developed a new system for American Airlines which used the origin-destination concept to create nested tables. Nesting means creating a subset of seats that is available at different discount levels (Smith et al. 1992; Haerian et al. 2006)
  • 1993: Brumelle and McGill (1993) proposed different algorithms to find the optimal allocation (Haerian et al. 2006; Lan et al. 2008).
  • 1993: Hersh (1993) proposed dynamic programming formulation method to deal with stochastic demand (Lan et al. 2008).
  • 1999: Lautenbacher and Stidham (1999) discussed the multi-fare problem using Markov decision process (MDP) (Lan et al. 2008).
  • 2000: Ryzin and McGill (2000) studied the single-leg, multi-fare problem using an iterative procedure for updating booking limits (Lan et al. 2008).
  • 2002: A modified version of Wollmer’s model was introduced De Boer et al. (2002) where it depends on a lower number of decision variables (Haerian et al. 2006).
  • 2003-2005: The nesting method got more popularity and applied in a two-stage process. Firstly, the standard linear programming methods are applied to determine the best seats’ allocation. Secondly, the nesting policies were used to optimize these algorithms. Some of the scholars in this area were Bertsimas and de Boer (2005) and van Ryzin and Vulcano (2003) (Haerian et al. 2006).
  • 2006: Haerian et al. (2006) restudied the standard and the nesting approaches in detail and proposed a unified alternative that combines them together based on the concept of a nesting table.
  • 2006: Huh and Rusmevichientong (2006) suggested iterative approaches for nested booking limits (Haerian et al. 2006).
  • 2007: Kunnumkal and Topaloglu (2007) suggested iterative approaches to compute the nested booking limits as well (Lan et al. 2008).
  • 2008: Ball and Queyranne (2008) adapted online algorithms in revenue management for the first time (Lan et al. 2008).
  • 2008: Lan et al. (2008) proposed a new approach using competitive analysis to solve the classical singe-resource leg problem (Lan et al. 2008).

The next article will further illustrate how yield management is implemented alongside with an overview of the issues and the limitations incorporated with in.

References

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

Belobaba, P.P., 1987. Flight Transportation Laboratory Report R87-7 Air Travel Demand and Airline Seat Inventory Management, Available at: https: //dspace.mit.edu/handle/1721.1/68077.

Coelli, T., Grifell-Tatje, E. & Perelman, S., 2002. Capacity utilisation and profitability: A decomposition of short-run profit efficiency. International Journal of Production Economics, 79(3), pp.261–278.

Haerian, L., Homem-de-Mello, T. & Mount-Campbell, C.A., 2006. Modeling revenue yield of reservation systems that use nested capacity protection strategies. International Journal of Production Economics, 104(2), pp.340–353.

Hart, G., 1996. The Five W’s: An Old Tool for the New Task of Audience Analysis. Technical Communication, 43(2), pp.139–145.

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

Lan, Y. et al., 2008. Revenue Management with Limited Demand Information. Management Science, 54(9), pp.1594–1609.

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: //www.boeing.com/resources/boeingdotcom/commercial/about-our-market/assets/downloads/AirTransportCapacitySupplyDemandBalance.pdf. Last viewed August 2016.

Marco, A., Marcella, N. & Piga, C.A., 2015. Combined effects of capacity and time of fares: insights from the yield management of a low cost airline. The Review of Economics and Statistics, 97(4), pp.900–915.

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

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

Smith, B.C., Leimkuhler, J.F. & Darrow, R.M., 1992. Yield management at American Airlines. Interfaces, 22(1), pp.8–31.

Williamson, E.L., 1992. Airline Network Seat Inventory Control: Methodologies and Revenue Impacts, Available at: http: //hdl.handle.net/1721.1/68123.

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