Revenue management systems must be adaptable as reservations accumulate for flights. Ideally, each confirmed flight package should update the DSS model to show actual bid price information and seat availability. Unfortunately, current technology limits the reality of such real-time updates due to the large amount of data processing. There exist, however, some ways for decision-makers to maximize their yield to the benefit of the company. Although some limitations persist, flight experts can implement effective strategies to generate new seat allocations and revised bid pricings through improvements to the DSS model.
To generate new seat allocations, a number of strategies may be implemented. As booking conditions vary based on demand and flight class, the most significant problem to address lies in how to account for passengers booked in various classes in spite of the fact that different fare classes can account for the same seat (Chang, Hsieh, Yeh, and Liu 1004). In the Expected Dynamic Probability Method, Ftn + ƒs-1n-1 ≥ 0n + ƒsn-1, n represents a period, s represents a seat, Ftn represents the revenue of request i at period n, and ƒsn-1 represents the revenue of s unoccupied after n-1 (Chang et al. 1005). The result of this formula calculates the revenue of a current request as compared with the revenue generated by holding onto the seat in the next period (Chang et al. 1006). By doing so, airlines can calculate the best time to sell seats.
A second improvement possibility also exists. Chang et al. also recommend a Case-Based Seat Allocation System where computers compare current seat availability with past obtained solutions and place a higher value on more recent decisions; this system contains four systematic processes of Case representation where the current situation is assessed, Case retrieve where the three most similar situations converge, Case adaptation where a market decision occurs, and Case storage where the decision is referenced for future determinations (Chang et al. 1006). This process is represented in a formula where Qik represents the kth feature of request i, C jk represents the kth feature of case j in the case base, and k represents the index of features (Chang et al. 1006). ¬By implementing these formulas rather than a first come first served rule, airline seat allocations improved revenues by 12.4% (EDP) and 16.9% (CBSAS) with four fare classes and a booking probability of five (Chang et al. 1006). Through these formulas, the DSS model could be improved upon.
There also exists a bi-level model of fare optimization for improving upon pricing systems. Here, the leader "integrates within its optimization process the reaction of a rational 'follower' to his decisions" where the leader is the airline and the follower is the client (Côté, Marcotte, and Savard 26). By implementing the model, airline administrators can assess perceived travel dis-utilities within the context of maximum revenue fares (Côté et al. 27). The result will generate revised bid price information for DSS that profits airlines.
Other decision factors affect the DSS implementation process. Pricing and overbooking control directly affects the yield management unit as directed from the market planning departments of most airlines (Belobaba 65). Through operations management, airlines like United Airlines also manage fare pricings by setting class buckets in a maximum of five different categories; however, airlines are beginning to implement an upgraded version of DSS control that allow for full and discount fare division in up to forty buckets, thereby allowing airlines to "stop sales of extremely low-priced seats in selected markets on a connecting flight leg without closing down the entire fare class to additional bookings" (Belobaba 66). Many times, simplistic overbooking adjustments are made when thresholds within a given bucket are approached and individuals must choose whether to increase the availability of the applicable fare or close it down completely; algorithms such as those presented by Chang et al. present an alternative for achieving maximum revenue rather than overbooking a flight (Belobaba 67). By presenting more simplified algorithms to dynamic seat inventory control, the airline industry can increase yield in a volatile market (Belobaba 72). DSS models must limit discount fares and price matching competitive practices in order to succeed in a deregulated industry (Belobaba 72). In doing so, airline companies like United Airlines will ensure flight-cost profitability.
A number of other factors affect DSS systems improvement possibilities. Flight industry leaders can create sales forecasts models by considering GDP and the price of crude oil, the demand for travel based on the season as well as an intrinsic understanding of cultural and social factors (Sylla 53). Estimates for recapture probabilities can be established by constructing a distribution of demand summary for full-fare and discounted fare tickets on a given leg; by forecasting an accurate demand structure, effective decision trees can be implemented that target potential customers decisions (Sylla 55). Overbooking presents an opportunity for profits as a statistically set amount of passengers cancel their flights or elect not to show for their flight; companies must develop successful DSS models such as in order to mitigate the risk for displaced passengers (Sylla 54). Traffic management is directed by operations control and functions to negotiate delays, cancellations, and newly scheduled flights; implementation of this flight aspect will affect the DSS model as "advances in mathematical programming and computer processing speed now enable researchers to consider solving this problem in real-time" (Sylla 60). As DSS systems improve, more refined algorithms will come into place that assesses a host of possibilities and ensure airline profitability and efficiency.
Belobaba, Peter B.. "Airline Yield Management An Overview of Seat Inventory Control." Transportation Science 21.2 (1987): 63-73. Print.
Chang, Pei-Chann, Jih-Chang Hsieh, Chia-Hsuan Yeh, and Chen-Hao Liu. "A Case-Based Seat Allocation System for Effective Revenue Management." Yuan-Ze University 4113 (2006): 1003-1011. Print.
Côté, Jean-Phillipe, Patrice Marcotte, and Gilles Savard. "A Bilevel Modeling Approach to Pricing and Fare Optimisation in the Airline Industry." Journal of Revenue and Pricing Management 2.1 (2003): 23-36. Print.
Sylla, Abdoul. "Operations Research in the Airline Industry." Sabre, Inc 1 (2000): 46-62. Print.