The above revenuemanagement object of our studies is the perishable products. And the opposite of perishable products is durable products. Durable products means the goods can be used for a long time after one purchase, such as automobile, housing, etc. One product is durable goods for consumers, but it may be perishable product for manufacturers. For instance, to the most real estate developers, because of its cost of funds and the loan re- payment pressure, developers sell the housing as a per- ishable product while customers consider it durable goods. Another example is automobile, due to the rapid devel- opment of new technology and new car introduced con- tinuously, each type of car has the perishable product’'s attributes in selling process. Durable products have per- ishable product’s properties in sales management, we consider that we can use revenuemanagement methods to study durable products, and in the current studies there are only small literatures study the problems of durables revenuemanagement.
In attempting to integrate pricing research with capacity allocations, one of the main challenges faced is that OR-based revenuemanagement often assumed demand to be stochastic or probabilistic, whilst pricing research tend to model demand as deterministic. Marketing researchers are keen to understand if a theoretical structure exists that could explain how demand is shaped or why it would follow a particular pattern across time. Some research studies have attempted to shed some light on the behavior of the advanced buyer. The literature is scant, dominated by marketing literature, and not commonly brought into traditional revenuemanagement research. For example, Desiraju and Shugan  evaluated strategic pricing in advanced selling and found that yield management strategies such as discounting, overbooking and limiting early sales work best when price-insensitive customers buy later than price-sensitive customers. Shugan and Xie  demonstrated that the state dependency of service utility caused buyers to be uncertain in advance and certain at consumption time, while sellers remain uncertain of the buyer’s state at consumption time due to information asymmetry. They suggest that this informational disadvantage can be overcome by advance selling, which then becomes a strategy to increase profit. Xie and Shugan  went on to study when advanced selling improves profits and how advanced prices should be set. Their areas of investigation include the optimality of advanced selling, investigating selling in a variety of situations, buyer risk aversion, second period arrivals, limited capacity, yield management and other advanced selling issues.
 proves that when the seller knows only the upper and lower bounds of the demand, the network revenuemanagement problem (multiple products use multiple resources) using maximin and the minimax regret criteria are both NP-hard. Then they discuss several special but tractable revenue network: single resource revenuemanagement, bundle revenuemanagement. Finally, a heuristic algorithm is given for general cases.  respectively discusses robust version of static and dynamic single resource capacity allocation, and designs polynomial time algorithm.  introduces competitive ratio criteria to robust revenuemanagement from the perspective of online algorithm in computing science. For two fare classes, multiple fare classes and bid-price controls problem, they obtain the lower bounds of competitive ration and corresponding protection level or biding price. Notice that  assume that seller knows no information about uncertain demand.
A strategic RM system should begin with an analy- sis of the organization’s internal and external operating environment. The external environment encompasses sociocultural trends, economic influences, technological influences, environmental, legal, competitive, and partner and vendor influences (illustrated in the outermost por- tions of Exhibit 8). For example, advances in data analyt- ics and cloud-based technologies on the vendor side will allow managers and operators to devise more customized value-centric offerings. In terms of the internal operat- ing environment, the formulation and implementation of specific RM strategies requires designing an organiza- tional structure, controlling organizational processes, and managing resources and relationships with stakeholders, so as to develop competitive advantage. Our interviewees have identified the effort of building systems to support company-wide total revenuemanagement initiatives as a major challenge. An organization may discover, for instance, that a proposed RM strategy cannot be imple- mented feasibly within the existing management systems and with the existing levels of training and employee
We study the revenuemanagement (RM) problem encountered in airport carparks, with the primary ob- jective to maximize revenues under a continuous-time framework. The implementation of pre-booking systems for airport carparks has spread rapidly around the world and pre-booking is now available in most major airports. Currently, most RM practices in carparks are simple adjustments of those devel- oped for hotels, exploiting the similarities between the two industries. However, airport carparks have a distinct setting where the price-per-day of a parking space is heavily discounted by the length-of-stay of the booking. This is because the customer decision tends to be made after the length of the trip is already set, and it becomes a choice between parking or alternative modes of transport. Consequently, the length-of-stay becomes a critical variable for revenue optimization. Since customers are able to book the parking by the minute, the resulting state space is very large, making a conventional network solu- tion intractable. Instead, decomposed single-resource problems need to be considered. Here we develop a bid-price control strategy to manage the bookings and propose novel approaches to define such bid- prices depending on the length of stay, which could be utilized in real-time RM algorithms. Managing stochastic carpark bookings by length-of-stay in the decomposed single-resource approximation allowed us to achieve within 5% of the expected revenues for a multi-resource approximation, with a fraction of the computational effort. When expected demand exceeds the available parking capacity, the method increases the revenues by up to 45% relative to the first-come-first-serve acceptance policy.
Enterprise Solution to our ever growing market, helping Micros remain #1 in the UK & Ireland. Richard’s introduction to hotels was purely accidental as a barman at a city centre hotel while at University, followed by what was supposed to be a part time job at a hotel which lasted 4 years, during which time they installed Fidelio v6 and as they say the rest is history. Our key focus at the moment is the development of the long awaited OPERA9 which will provide a new user experience to the system and enable new tools to be integrated with the system to improve revenuemanagement reporting and opportunities.
So, once the decision has been made that a change is needed for current revenuemanagement processes, a series of steps should be taken. But first, in order to implement the right systems and processes for revenuemanagement within the company, all stakeholders must be considered and in agreement.
In future, railway capacity management systems are required to achieve an efficient marketing of line, cargo and passenger capacity. An appropriate system architecture for such a system has been presented in previous publications (see for example (10)). RevenueManagement algorithms for the pricing of services already exist (airline seats, hotel rooms). One main research field is the implementation of appropriate algorithms for the pricing of railway services (in this context the assignment and use of a slot/seat/cargo capacity is understood as a main service).
Crucially a common issue seems to be one of the managers perceiving themselves to have a greater degree of judgment over that of the automated system. One manager expressed this directly stating, “you still trust yourself more than the system…it is a natural process…you just do it, don’t you?”. Another agreed, “you can feed all that data in but sometimes it’s you know…it’s a gut feeling…I think you should have a better feel for your business…that you should be able to go no…it needs to be this…not just the computer does it”. However, there may be some suggestion that revenue managers, who are more statistically driven are happier to trust the systems rather than general managers who seem to be more instinctive. When hotel managers and revenue managers need to agree on prices together this could cause an interesting tension between the two parties and debates between the hotel and off-site revenue could serve to slow down rather than speed up the process as both parties focus on different sets of data. One General Manager commented on the different viewpoints of general managers and central revenue team using the automated systems, “for me, it's gut feeling…for them its spreadsheets". The danger here is, of course, that vital data that would, in fact, support both those roles are ignored and that revenuemanagement gets broken down into its component parts rather than being viewed as an organisation wide strategic decision. Revenue managers, especially those working at specialist revenue hubs away from properties appear to be viewed as more statistically and technologically driven and may be happier to trust in systems as they are more aware of the working behind the algorithms than general managers. Finally, the last step in the process is action, where again the use of mobile and tablet technologies is increasing the number of revenue actions and price changes that are made but with that remains the lack of detailed analysis prior to the decisions being implemented. One deputy general manager commented of herself and the General Manager that, “we’ve both had our system put onto our I- pads, so we can sit at home at night and check rates”. Another said, “it’s like if I’m abroad…I can actually manage the hotel”. Taking decisions using mobile technology, at home and out of context may mean that not all the data is utilised fully and only a quick scan of the data is conducted. It may be based on visual scanning of trends rather than analysis of the more detailed statistics available. It seems almost that with more data and the ability to change prices more quickly and easily there has become actually less accurate decision-making in revenuemanagement in hotels even if it is more frequent.
The list of products preferred by customers is analyzed as preference orders . An ecient sets concept for single leg revenuemanagement model is introduced by Talluri & van Ryzin . Deterministic linear programming model is introduced for solving choice-based revenuemanagement models . Pre- vious work was extended by allowing market segmen- tation to the choice model by disjoint consideration sets of products . They extended the ecient sets concept to network and showed that as demand and capacity are scaled up asymptotically, only ecient sets are used in an optimal policy. Bront et al.  extended work of Liu & Van Ryzin  by applying market segmentation in a way that products could belong to overlapping segments. The heuristics for improving initial guess of bid price vector is consid- ered by Meissner and Strauss  in network revenuemanagement.
Abstract: Many airline reservation systems oﬀer the commitment option to their potential passengers. This option allows passengers to reserve a seat for a fixed duration before making a final purchase decision. In this study, we develop single-leg revenuemanagement models that consider such contingent commitment decisions. We start with a dynamic programming model of this problem. This model is computationally intractable as it requires storing a multi-dimensional state space due to book-keeping of the committed seats. To alleviate this diﬃculty, we propose an alternate dynamic programming formulation that uses an approximate model of how the contingent commitments behave and we show how to extract a capacity allocation policy from the approximate dynamic programming formulation. In addition, we present a deterministic linear programming model that gives an upper bound on the optimal expected revenue from the intractable dynamic programming model. As the problem size becomes large in terms of flight capacity and the expected number of arrivals, we demonstrate an asymptotic lower bound for the deterministic linear programming model. Our extensive numerical study indicates that oﬀering commitment options can noticeably increase the expected revenue even though oﬀering a contingent commitment option may not always be in the best interest of the airline. Also, our results show that the proposed approximate dynamic programming model coordinates capacity allocation and commitment decisions quite well.
in the early days of revenuemanagement, in practice as well as in theory, the focus was usually on such single-leg problems only, before there was a shift towards more sophisticated network-based methods in the 1990s. In their paper, Talluri and Van Ryzin (2004) develop a whole new theory of availability control, thereby adapting techniques from portfolio theory. In particular, they show that an optimal offer set S is always from an ordered family of subsets of J that are efficient with regard to the trade-off between expected revenue and purchase probability, while inefficient subsets of J can be ignored. The efficient subsets can be visualized two-dimensionally to form a so-called efficient frontier in their order, and it can be shown that the more capacity is available or the less time remains, the further an optimal set lies on this frontier, i.e., the lower the quotient of revenue and purchase probability of the currently optimal offer set is. Moreover, in the case that the efficient subsets are nested with regard to the contained products along the frontier, a nested allocation policy is optimal. For the case of MNL as the underlying choice model (as well as for independent demand), the authors show that the optimal policy is nested by the order of the product’s revenues (so called “nesting by fare-class order,” see Section 4).
The only thing worse than no data is stale data. PROS O&D III provides seamless integration with other products for real- time availability and group revenuemanagement, as well as other systems and processes within your airline, allowing for greater efficiency and data accuracy.
So far, revenuemanagement systems (RMS) are available only for traditional RM industries, but in the future it is expected there will be new technologies and RM systems for non-traditional RM industries. While most current RMS for airlines and hotels are system-oriented, service-oriented RMS are to be introduced for non-traditional RM. Initial investment costs for service-oriented RMS should be lower than system-oriented ones and thus small business owners can readily utilize RMS to improve their revenue. The increasing number of smartphone users and the prevalence of Web 2.0 technologies may help non-traditional RM industries to control demand and to enhance revenue better than ever before. For example, mobile platforms and social commerce can be used for restaurant RM as distribution channels in the same way that hotels and airlines use online travel agencies. Also, location service technology will allow organizations to find customers within a defined area. Considerable additional research on non-traditional RM industries, particularly using new technology, may help organizations to maximize the benefits of RM.
Abstract. New challenges in the business environment, such as increased competition and the inuence of the Internet on main distribution channels has led to fundamental changes in traditional revenuemanagement models. Under these conditions, modeling individual decisions more accurately is becoming a key factor. Nearly all research studies about choice- based revenuemanagement models use the well-known multinomial logit model. This model has one important restriction, that is, the independence of irrelevant alternatives; a property which states that the ratio of choice probabilities for two distinct alternatives is independent of the attributes of any other alternatives. In this paper, a nested logit model is proposed for removing this limitation and incorporating a correlation between alternatives in each nest. The new subproblem of column generation is introduced and a combination of heuristic and metaheuristic algorithms for solving this problem is provided. Interesting outcomes are obtained during analysis of the results of experimental computations, such as oer sets and iteration trends, with respect to the correlation measure inside each nest. Simulation results show that, although changing the choice model might lead to signicant improvement in revenue under some conditions, during all scenarios, observing the correlation should not cause the choice model to change immediately.
Another aspect that is relevant regarding the integration of a long-term perspective into revenuemanagement decisions, is the customers’ perceived fairness of revenuemanagement techniques. Empirical research investigates how far customer satisfaction and thus loyalty is affected by applying revenuemanagement (i.e., in particular by denying customer re- quests). Customer loyalty has proved to be a key driver for profitable long-term customer relationships and consequently constitutes an important objective in customer relationship management. In general, results indicate that short-term allocation decisions might affect (due to being perceived as unfair) customer satisfaction and loyalty and therefore also cus- tomer relationships in the long run (see, e.g., Suzuki, 2004; Wangenheim and Bay´ on, 2007). These findings imply that customer demand should be considered interdependent over time. Future customer demand thus also depends on current allocation decisions. Both revenuemanagement and customer relationship management therefore interact. In the field of in- ventory management, several authors have already studied models where the effects of poor customer service on customer demand in subsequent periods are incorporated (e.g., Adelman and Mersereau, 2013; Olsen and Parker, 2008). These papers are based on the findings of em- pirical research regarding customer reactions towards physical stock-outs. Stock-outs cause customers (amongst others) to switch brand or store (see, e.g., Campo et al., 2000). Ac- cordingly, stock-outs might have severe impacts in the long-run (e.g., Anderson et al., 2006). In general, there are few analytical contributions, including intertemporal demand effects caused by customer service. The papers particularly focus on inventory-related problems and not on issues related to revenuemanagement.
As our businesses and economies take on a more global focus everyday, so does revenuemanagement. The IJRM provides a platform for both academic and business professionals to share technical theory and practical solutions aimed at making revenue managers and the businesses they support more successful at home and abroad.”
Revenuemanagement is the application of disciplined analytics that predict the consumer behavior at the micro level and optimize product availability and price to maximize revenue growth. The essence of this discipline is under stood in this discipline is in understanding customers’ perception of the product value and accurately aligning product prices, placement and availability with customers’ segment. Businesses have taken important decisions such as what to sell, when to sell, whom to sell and how much to sell relating to the marketability of the product. RevenueManagement uses a data driven tactics and strategy to increase the revenue. This article focuses on revenuemanagement in general and BSNL in particular, which is a public sector company. This process includes data collection, segmentation, forecasting, optimization, dynamic revaluation and estimating the expected revenue. It includes revenue generated from services and other incomes, expenditure incurred and revenue leakage in BSNL during 2011-2015. Revenue income was gradually decreasing in spite of an increase in its expenditure was increased year by year. BSNL needs to improve its income in future, by utilizing its efficiently and effectively so as to improve its operational efficiency of the organization and in addition by launching better plans which suits customers of all categories.
typically referred to as optimizing by length- of-stay (length-of-rent in the car rental indus- try). It is far more profitable to earn $149 per night for four nights than $229 for a one- night stay. Recommendations to close the lower rate tiers for a peak night to all guests, regardless of whether they wanted to stay for one, four or seven nights, could easily discour- age those guests who wanted to stay several nights or longer. Unfortunately, the early hotel revenuemanagement systems were not designed to analyze demand patterns across dates and address this issue. Nor were many hotel reservation systems designed with the controls needed to implement such decisions. The systems were not designed to control room inventory in ways that would stimu- late or accept demand from guests wanting longer-stays, while simultaneously limiting the number of shorter-stays on peak nights.