Journal of Revenue and Pricing Management最新文献

Booking control focuses on the problem of deciding whether to accept or reject bookings to maximize revenue while considering limited capacity. For freight applications, computing the cost of fulfilling requests requires solving an operational decision-making problem which often corresponds to a mixed-integer linear program. We propose a two-phase learning-based approach that first learns to predict the objective of the operational problem, then leverages the prediction within reinforcement learning algorithms to compute the policies. The method is general and applies to different problems faced in practice. We show strong performance on two booking control problems in the literature: distributional logistics and airline cargo management.

Single and small batch production is characterized by complex value-added processes and products. The transparent calculation of new offers and change requests is therefore a particular challenge. At the same time, the rising spread of product service systems (PSS) increases the complexity of costing, as additional intangible services have to be calculated precisely. In addition to the challenges posed by such precise calculation of intangible services, companies have to master another complexity driver in the form of PSS. Innovative information and communication technologies (ICT) offer new potential for effective and efficient design of the costing process for the entire life cycle. The rising availability of data along the entire product life cycle significantly increases transparency and, thanks to intelligent analysis algorithms, allows the identification of clear cause-and-effect relationships and forecasting options. The aim of the presented paper is thus to develop a model for calculation of PPS in single and small batch production that exploits the new potential of ICT.

In the context of volatilities in the international markets in recent times, studies regarding the complexities of oil price fluctuations have focussed on analysing the special fluctuation characteristics of oil prices in different historical perspectives. This study examines the extent to which petroleum price fluctuations under the petroleum price deregulation regime impact on business cycles in Ghana. The study uses the autoregressive distributed lag (ARDL) model with a quarterly data spanning from the first quarter of 2005 to the fourth quarter of 2022. Our empirical results show that price stability impacts positively on economic growth, both in the short and the long run, while foreign direct investment also has a positive effect on economic growth in the short run. Again, we observe that increases in inflation rate and government petroleum revenue negatively affect economic growth both in the short and the long run. To the best of the authors’ belief and knowledge, the observations and recommendations made are consistent with theory and empirical studies and contribute immensely to the discussions about price asymmetry and business cycles. It also offers a nuanced perspective on how policy makers can enact policies that ensure efficient and effective deregulation and price stability.

Abstract

Currently, the hospitality industry is experiencing an increase in the adoption of subscription-based business models among restaurants. Pricing is a critical factor to consider when deploying the subscription models. However, only a few studies in the literature talk of pricing the new subscriptions and even in these studies no algorithm is given for setting the prices. Consequently, this study aims to derive an optimal pricing strategy for subscription services in the restaurant industry through a two-step implementable framework. In the first step, we try to understand the preferences of the consumers and accordingly curate different subscription packages for them. In the second step, we propose a linear programming-based optimization model to price these packages in an optimal manner. The linear programming model is solved by CPLEX 12.7 solver software. Finally, the authors discuss the theoretical and managerial implications of their findings.

This article aims to identify the measures to overcome the COVID-19 crisis proposed by revenue managers during the lockdown period. The comparison of such measures to others against previous crises and their development afterwards is valuable to future decision-making processes in the hospitality industry. A survey of 322 professionals linked to revenue management was undertaken. The holistic and innovative point of view, integrating RM implementation, operations, marketing, and communication following the Flywheel Model, led us to revenue managers’ viewpoints about the measures to overcome the lockdown phase of the COVID-19 crisis. It is, therefore, necessary to integrate them to improve the understanding of hospitality crisis management.

Abstract

This study explores how manufacturers in the competitive supply chain can set prices and secure funding effectively. We use game theory to look at how competition between domestic and foreign manufacturers affects pricing decisions. Our research investigates how a domestic manufacturer can improve its market share by addressing financial challenges through modern financing methods. In this scenario, a domestic manufacturer competes with a foreign one to attract a retailer’s market share and profits. The retailer decides what products to buy and how to price them based on bid prices and demand. We also consider that the domestic manufacturer will use online crowdfunding platforms to tackle its financial problem. Hence, our study sets up a supply chain where competition revolves around both operational and financial decisions. Mathematical models are developed to analyze how costs, finances, market potential, and price sensitivity impact various parts of the supply chain. The results reveal that decisions made on the crowdfunding platform significantly influence other supply chain decisions. Manufacturers and retailers need to pay attention to the financial decisions made on this platform to maximize profits. Also, domestic and foreign manufacturers should consider customer preferences for their products when setting prices. Finally, the results demonstrate that a domestic manufacturer can gain a competitive edge in the retail market by carefully considering both product pricing and financial decisions, including those made on the lending platform.

Traditional revenue management relies on long and stable historical data and predictable demand patterns. However, meeting those requirements is not always possible. Many industries face demand volatility on an ongoing basis, an example would be air cargo which has much shorter booking horizon with highly variable batch arrivals. Even for passenger airlines where revenue management (RM) is well-established, reacting to external shocks is a well-known challenge that requires user monitoring and manual intervention. Moreover, traditional RM comes with strict data requirements including historical bookings (or transactions) and pricing (or availability) even in the absence of any bookings, spanning multiple years. For companies that have not established a practice in RM, that type of extensive data is usually not available. We present a data-driven approach to RM which eliminates the need for demand forecasting and optimization techniques. We develop a methodology to generate bid prices using historical booking data only. Our approach is an ex-post greedy heuristic to estimate proxies for marginal opportunity costs as a function of remaining capacity and time-to-departure solely based on historical booking data. We utilize a neural network algorithm to project bid price estimations into the future. We conduct an extensive simulation study where we measure our methodology’s performance compared to that of an optimally generated bid price using dynamic programming (DP) and compare results in terms of both revenue and load factor. We also extend our simulations to measure performance of both data-driven and DP generated bid prices under the presence of demand misspecification. Our results show that our data-driven methodology stays near a theoretical optimum (< 1% revenue gap) for a wide-range of settings, whereas DP deviates more significantly from the optimal as the magnitude of misspecification is increased. This highlights the robustness of our data-driven approach.

To investigate the demand for 5G in Germany, we applied the Van Westendorp Method (VWM) to measure willingness to pay (WTP) and price sensitivity (N = 504). The results show that more than half already own a 5G smartphone and these customers are less price sensitive. The accepted price range for the monthly 5G surcharge ranges between 10.00 and 15.40 Euros. Two thirds want more transparency in 5G pricing, while price is the most crucial factor (4.2/5.0), followed by data volume (4.1/5.0) and network operator/coverage (4.0/5.0). The results are particularly interesting for providers as the target group for 5G is quite diverse.

The goal of this research is to determine whether a local brick-and-mortar (B&M) business should implement an online-to-offline (O2O) strategy, as well as the conditions under which each mode is most effective. This research examines how an O2O platform and a B&M store's (like restaurants) decision-making processes interact in a two-echelon supply chain (SC). This research employs numerical experiments and a mathematical approach to address research issues with an online food ordering service "Snappfood" that delivers from over 1,500 eateries. We first look at five case studies in which the retail price of Snappfood is determined in both a normal situation (without a marketing mechanism) and one in which the store invests in local advertising to boost demand in the self-building channel. A growth in SC profits is predicted by numerical experiments conducted under this model. The findings of decentralized decision-making reveal that in self-building mode, the store decides on both retail channels and the level of local advertising investment. In turn, the O2O platform recommended a payment delay contract as a fixed operating method for working with B&M stores. This is one of the first studies to investigate at the online sales of a local B&M store, which has the unique characteristic of offering home delivery. Second, we suggest and evaluate that a B&M store can use either the self-building technique or implementing an O2O platform for online selling.

In this paper, we analyze the dynamic-pricing decisions of differentiated products for retailers operating in a competitive environment, for a finite-time horizon, limited initial inventory, and in the presence of the reference effect. Customers learn from the past prices of retailers and form their estimate of sales prices, called the reference price effect, and use it to make a decision on choosing a retailer to make a purchase. The demand is uncertain, and the customer choice behavior is modeled based on a Multinomial Logit model, modified to incorporate the reference effect. The complexity of the problem increases under conditions of competition and demand uncertainty and cannot be analyzed using conventional methods. Therefore, we have used a neural network-based algorithm called Revenue-Based Neural Network (RBNN) to dynamically calculate the competitive price in order to increase the retailer’s revenue. We have analyzed the effect of competition and the performance of RBNN algorithm under two scenarios: a monopolistic situation in which a retailer uses the RBNN policy to maximize its revenue, and a duopolistic situation in which one retailer uses the RBNN strategy and the other uses an adaptive policy called Derivative Following (DF). The results of the experiments show that the pricing policy under duopolistic conditions highly affects the income of retailers in the presence of reference price. The RBNN policy outperforms the DF policy due to the learning process on the customers’ reference price. By charging higher prices in the RBNN strategy, the seller trades off the current revenue with the long-term revenue resulting from formation of higher levels of the reference price in customers’ minds and earns more revenue than its competitor overall.