Introduction
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3.1 Introduction
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o PTiMal f encing in a irline i nDusTry wiTh D eManD l e ak age
SY E D A SI F R A Z A A N D M I H A E L A T U R I AC
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and fare classes availability management. In particular, airline RM research has focused on four categories: forecasting, overbooking, quantity/inventory (booking) control and pricing; however, as noticed in Cote et al. (2003), integration of pricing and quantity controls is expected to improve the firm’s revenue. The tactics and strategies of RM are applied in general in business that has a fixed or perishable resource like the flight seats in airline business or the hotels’ rooms.
As noticed in Anon (n.d.), the general RM practices are classi- fied into quantity-based RM and price-based RM. A quantity-based RM problem is designed as a revenue optimization model in which the resource allocation can be adjusted efficiently for predetermined prices. This practice is well applied in airline industry and is usually addressed as seat inventory control problem. The price-based RM is applied to maximize the revenue by optimizing the pricing when the available resource is fixed. Such typical business frame is com- monly observed in retail industry where the simplest form of RM has been identified in the Newsvendor (Newsboy) problem, considered by Petruzzi and Dada (1999) as a building block in stochastic inven- tory control and an excellent tool for examining how operational and marketing issues interact in the decision-making process.
Pricing is one of the main cores of yield management, also known as RM practice. The fundamental concept is to segment the market into multiple market segments using a differentiation price, which will offer potentially a different price or sale condition. One real-life example of price differentiation practice can be observed in the sale tickets offered by airlines to passengers who are willing to pay in advance and accept penalties for changing or canceling tickets, while for the late-arriving passengers who are less price sensitive and more willing to pay for their tickets less restrictive, the airline reserves part of its capacity. In airline RM, this tactic is usually referred to as fare price differentiation and is among the principal strategy used to segment the demand from one fare class to multiple fare classes. As RM tactic, pricing is applied also by hotels that often set higher prices for weekdays’ room rates expected to be reserved by business customers, compared to weekend rates, which are more desirable for leisure customers. Similar to airlines, hotels do apply several penalties such as cancellation restrictions, fees for changes in reservation, or nonrefundable lower-priced room rates to achieve buy-down. Another example of how price differentiation
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which leads customers to different channels is the online versus retail store sales, where a firm may offer discounted prices for online sales with no option of touch and feel, and higher prices for retail store sale due to the option of interacting with sales staff or with products.
Integration of pricing and seat allocation in airline RM began with Weatherford (1997), who considered normally distributed customers’
demand with mean as a linear function of price. Feng and Xiao (2001) studied the integration of capacity and pricing decisions for perish- able assets in a comprehensive model with stochastic demand. Cote et al. (2003) developed a bilevel mathematical programming approach for joint determination of fare price and seat allocation. Raza and Akgunduz (2008) proposed a game theoretic model for an integrated approach of fare pricing duopoly competition with seat allocation and extended their work in Raza and Akgunduz (2010) to a cooperative game setting using bargain solution.
Many research studies (see Anon, n.d.; Philips, 2005) reported that market segmentation from price differentiation augments profitabil- ity; however, different prices for distinct market segments often cause customers’ cannibalization, referred also as demand leakage from one market segment to another. The effects of market segmentation with demand leakage on a firm’s pricing and inventory decisions were studied in Zhang and Bell (2007). To mitigate cannibalization and maintain the fences that differentiate the market segments, one com- mon practice is to improve the fences by introducing restrictions that would prevent customers from migrating between market segments.
A fence can be referred as a device designed to preserve the market segments formed after price differentiation. Among such devices com- monly observed are early purchase, prolong processing time, return penalties, channel of purchase, etc. An overview and taxonomy of price fencing in RM practice can be found in Zhang and Bell (2010).
Li (2001) investigated pricing of nonstorable perishable goods in a deterministic demand case with imperfect market segmentation and purchase restriction with an application to airline fare pricing. The interest on fencing led earlier researchers to identify that maintaining appropriate fences is very essential for an efficient RM (see Hanks et al., 2002; Kimes, 2002; Zhang et al., 2010). However, there are still many concerns on fencing as a business practice especially in the context of airline industry, such as How can an airline control demand
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leakage through investment in fencing, and what will be the optimal investment? How profitable is to integrate the pricing, seat inventory control, and fencing investment decisions for an airline?
A related study was conducted by Zhang et al. (2010) for an unca- pacitated pricing and fencing investment decision problem of a firm.
Noticeably, airline RM modeling is significantly different from a typi- cal firm; however, both problems can resemble newsvendor problem (see Philips [2005] for details on newsvendor problem and RM rela- tionship). In airline RM, the demand arrival is assumed sequential, and therefore, the lower fare price class demand is observed prior to the respective higher fare class. In response to this, the airline exercises a nested control that reserves certain seats for passengers willing to pay a higher fare price and arrive later to purchase tickets. This control is referred in airline RM literature as nested booking control (McGill and Ryzin, 1999; Chiang et al., 2007). Furthermore, in airline RM, unlike the uncapacitated firm’s problem, there is a limited capacity rep- resented by the cabin seats. Lastly, in airline RM, the costs incurred in relation to seat inventory and related flight services are often ignored in most of the airline RM models, with no exception in this study.
Thus, the focus of this study is to revisit the problem of RM with demand leakages and fencing investments in the airline context. We first present the model for an airline RM with no fencing investment to mitigate the demand leakage, and then we extend the problem with fencing improvement decision for the airline to mitigate or augment the demand leakage through additional investment. Later, the models are analyzed, and the optimal fare pricing, seat inventory control (nested booking control), and fencing decisions are determined. Finally, a numerical experimentation study is presented to highlight the impact of some significant problem-related factors such as demand variabil- ity and leakage rate onto the airline’s RM decision. Additionally, the fencing investment decision is also studied numerically to determine the airline’s decision toward demand leakage control.