Revenue Management Methods Research Articles

Revenue management methods for the liner shipping industry

Due to changes in the industry which have led to opportunities for new market segmentations, revenue management (RM) methods have gained in importance for liner shipping companies in the recent past. Therefore, possible market segmentations of the liner shipping market and suitable RM methods are discussed in this work. Based on a slot allocation model for container bookings, three different booking acceptance strategies are studied and compared with respect to their applicability in liner shipping and their performance regarding profit and capacity utilization. Especially, a new bid-price (BP) strategy is developed and compared to previously presented booking limit strategies. A simulation approach is used to evaluate the strategies for different scenarios. The simulation study reveals large differences in the impact the strategies can have, e.g. depending on the booking forecast quality. As the new BP strategy shows very promising results for many different realistic scenarios, it can be highly recommended for application in liner shipping.

Alliance revenue management in practice: Impacts of bid price sharing and dynamic valuation

The control of shared seat inventory on codeshare flights operated by partner airlines in an alliance produces complexities for revenue management (RM). Typically, the carriers have limited information about their partner’s codeshare flight legs and must make RM seat availability decisions based on the information available with respect to their own network. Such decisions can be sub-optimal in terms of revenues for the carrier, for the partner, as well as the combined alliance. This article describes the practical RM challenges related to alliance RM and examines information exchange schemes designed to increase revenues for the combined alliance. The sharing of bid prices or network displacement costs between partners allows the carriers to determine seat availability based on the opportunity costs and revenue benefits of accepting codeshare passengers on connecting itineraries involving flights operated by both partners. These partner bid prices can be further used as inputs to the RM optimizer. We call this technique ‘dynamic valuation’ of codeshare paths. We use simulations to demonstrate the revenue benefits of both bid price sharing (BPS) for seat availability and dynamic valuation for RM optimization, when applied in typical network RM methods: displacement adjusted virtual nesting and probabilistic network bid price control. Our simulations suggest that BPS and dynamic valuation can lead to total combined system revenue gains for an alliance partnership of two airlines in the range of 0.30 per cent over other techniques. This can translate into total incremental revenue gains in the range of US$50–$100 million per year for larger airlines in alliance partnerships.

Dynamic Pricing Strategy of Hotel Revenue Management

With the increasingly fierce competition in hotel industry, hotel revenue management theory and methods emerges, as the times require, so it is very important to study the pricing strategy hotel room. This paper analyses the traditional hotel pricing strategy and its problems, and thus leads to the dynamic pricing method as the center of market and analysis of how to implement the dynamic pricing effectively. Based on modifying Littlewood law and EMSR-a model of revenue management capacity control applying to space industry often, it establishes a dynamic pricing model from the point of price. Hotel business revenue management could not only guide the hotel to realize scientific operation and management, but also make the hotel to maximize gains, so to survive and develop in the competition. This paper can enrich the theory of revenue management in hotel industry; at the same time for hotel room price has certain practical effect.

Cochlear implant programs: Balancing clinical and financial sustainability

In 2006, a tertiary academic medical center's adult and pediatric cochlear implant program was closed due to financial losses. Using business practices known as supply chain and revenue management, the objective was to establish a new cochlear implant program that was financially viable. Retrospective cohort study using a nonequivalent historical comparison group design. Using available financial data from the period of 1999 to 2006, cost and revenue figures were estimated, and a business plan developed using supply chain and revenue management principles to re-establish the cochlear implant program in 2007. Actual cost and revenue data from 2007 to 2011, the current program, were assessed for current financial performance and compared to the historical data. In comparing the period of 1999 to 2006 to the period of 2007 to 2011, the net loss per implanted patient went from $22,365 to $976. Profitable gross and net margins were achieved for all payers except Medicaid, for which the loss per case remained unchanged. This per case loss may change with receipt of pending Medicaid Upper Payment Limit supplemental payments. Use of supply chain and revenue management principles markedly improved the financial performance of the re-established cochlear implant program. With improved cost and revenue outcomes, the overall negative net margin was reduced. Physicians who learn and use supply chain and revenue management methods can work to ensure that their patients will have continued access to cochlear implant surgery, and are applicable to any clinical services or procedures that must meet the challenge of achieving financial sustainability.

A revenue management slot allocation model for liner shipping networks

The use of revenue management methods is still an up and coming topic in the liner shipping industry. In many liner shipping companies, decisions on container bookings are made by skilled employees without, or with little use of, decision support systems. Also in the literature, only a few publications on the topic of revenue management in the liner shipping industry can be found. Most of the models that have been suggested so far consider only one service and one ship cycle on this service. However, in liner shipping, it is important to consider the possibility of transhipment between services and of different demand situations at different times. Moreover, drawing inferences from similar developments in other industries and the literature, it seems promising to create a segmentation that divides container bookings into urgent and non-urgent cargo. This segmentation gives the customers more control over their cargo, and the carrier can gain additional revenue through extra charges. To achieve that aim, the carrier needs to keep some slots available until closing time, so he can offer slots on the next ship to customers with urgent cargo. On the basis of these facts, a new quantitative slot allocation model is developed that takes into account priority service segmentation, the network structure of liner shipping with the possibility of transhipment, and the existence of different ship cycles on the services. In contrast to the existing models, this approach leads to a more realistic representation of the situation in liner shipping. The booking limits resulting from the model can be used to decide whether a booking should be accepted or rejected in favour of a possible later and potentially more beneficial booking. A simulation study is done to test the model for different demand scenarios, which leads to promising results.

A two-stage bid-price control for make-to-order revenue management

Capacity control implementations in make-to-order (MTO) revenue management typically are based on bid-prices, which are used to approximate the opportunity cost of accepting a customer request. However, in the face of stochastic demand, this approximation becomes less accurate and the performance of bid-prices may deteriorate. To address this problem, we examine the informational dynamics inherent in MTO capacity control problems and propose a two-stage capacity control approach based on bid-price updates. Updating is realized with neural networks, which are applied to adjust the selection criteria during the booking period with respect to online demand information. Not only is the resulting contribution margin positively influenced by the update, but also the downside risk of performing worse than a naive first-come-first-served policy. Results from computational experiments show that the proposed approach dominates traditional revenue management methods like randomized linear programming with and without resolving in expected contribution margin as well as in risk.

Management Insights

Management Insights

Review of revenue management methods with dependent demands

Owing to the global proliferation of simplified, lightly restricted airfares since the year 2000, the older Revenue Management (RM) technologies that are based on unadjusted Expected Marginal Seat Revenue and independent demands have become less relevant. Airlines around the world are looking for new tools to manage the more realistic situation of dependent demand RM. In fact, new RM tools designed for dependent demands are showing 5+ per cent revenue improvements, which is very significant. As of Spring 2008, surveys results showed slow adoption of these new RM technologies (only 22 per cent of carriers had implemented ‘Low Fare’ or lightly restricted pricing solution modules). Although initial versions had limitations, both the science and tools for dependent demand RM have matured over the past 5 years. The latest versions of dependent demand RM systems should outperform rule-based heuristics that many carriers moved to after deciding that their traditional RM systems were not working as needed in their new lightly restricted fare environment. This article is designed to promote awareness among carriers and researchers that these newer RM technologies for managing dependent demands are now available and perform effectively.

Refined experimental design and regression splines method for network revenue management

We present a refinement of a network revenue management method that employs design of experiments and multivariate adaptive regression splines to approximate upper and lower bounds for the Markov decision process (MDP) value function. This approach involves an offline statistical modelling module that approximates the value function to provide a policy for accepting/rejecting customer booking requests, and an online availability processor module that conducts the actual decisions as the booking requests arrive. In the statistical modelling module, the data for the value function upper and lower bound functions are obtained by solving deterministic and stochastic linear programming problems, respectively. The refinement in this paper identifies realistic ranges of remaining seat capacity at different reading periods by adding a state space simulation module preceding the statistical modelling module. This effectively combines the advantages of a design of experiments approach and a simulation-based approach. Simulation results using a real airline network and based on actual demand data demonstrate up to 2.7 per cent improvement over the original method before refinement, which corresponds to a 5.8 per cent improvement over the bid price approach using the deterministic linear programming model to determine bid prices. The state space simulation module could also be applied to improve approximate dynamic programming methods that conduct value function approximation.

Research Papers: Modelling and simulation of impact of revenue management on Japan's domestic market

Revenue management has been shown to be an effective tool for increasing revenues in a variety of domestic and international airline markets. Owing to the significant volume of group passengers and differences in characteristics between the Japanese and other markets outside Japan, however, it has been difficult for Japanese airlines to determine whether revenue management methods will bring them similar revenue increases if applied to their domestic operations. An investigation of the impact of revenue management on airlines in Japan's domestic market is presented in this paper, taking into account the important role of group bookings in this environment. This paper first introduces the unique characteristics of the Japanese domestic market and describes the business and revenue management processes typically used by Japanese airlines. In preparation for the modelling of group bookings, parameters to describe the behaviour of group bookings are estimated, using data collected from Japanese airlines. Based on the parameters of the group booking data analysed, a model of the group passenger booking process is then developed and the model's framework is used for simulation. The Passenger Origin—Destination Simulator (PODS), originally designed to simulate revenue management for individual passenger bookings, is modified to incorporate the group booking process and used for the investigation. Leg-based seat inventory control, specifically fare class yield management (FCYM) with the expected marginal seat revenue (EMSRb) algorithm, is applied as the revenue management method, and it is shown to result in increased revenue for the implementing airline(s) in a representative Japanese domestic market, with group bookings incorporated. The result of simulations of scenarios reflecting the recent integration of two large Japanese domestic carriers and direct competition with a third existing carrier suggest that the revenue gains from using FCYM are as high as 5 per cent when one competitor implements systematic RM forecasting and optimisation. The simulated revenue gains are substantially less under scenarios in which both competitors implement FCYM, at about 1 per cent for each carrier.

A revenue management approach to demand management and order booking in assemble‐to‐order manufacturing

AbstractRevenue management is an order acceptance and refusal process that employs differential pricing strategies and stop sales tactics to reallocate capacity, enhance delivery reliability and speed, and realize revenue from change order responsiveness in order to maximize the revenue from pre‐existing capacity. While previously considered primarily as a tool of service operations, revenue management has considerable potential for assemble to order (ATO) manufacturing environments. Increasing demand for customer responsiveness has created service‐oriented manufacturing environments suitable for the application of revenue management methods. This paper applies revenue management concepts and techniques to ATO manufacturing environments and presents models for optimal pricing and capacity decisions.