Adopting revenue management strategies and data sharing to cope with crises

2021 ◽  
Vol 137 ◽  
pp. 336-344
Author(s):  
Giampaolo Viglia ◽  
Francesca De Canio ◽  
Anna Stoppani ◽  
Anna Chiara Invernizzi ◽  
Stefania Cerutti
Keyword(s):  
Revenue Management ◽  
Data Sharing ◽  
Management Strategies

scholarly journals Small Queuing Restaurant Sustainable Revenue Management

2020 ◽  
Vol 12 (8) ◽  
pp. 3477
Author(s):  
Kwangji Kim ◽  
Mi-Jung Kim ◽  
Jae-Kyoon Jun
Keyword(s):  
South Korea ◽  
Revenue Management ◽  
Waiting Time ◽  
Equilibrium Points ◽  
Management Strategies ◽  
Queuing System ◽  
Utilization Rate ◽  
Trade Off ◽  
Average Waiting Time ◽  
Price Increases

When competitive small restaurants have queues in peak periods, they lack strategies to cope. However, few studies have examined small restaurants’ revenue management strategies at peak times. This research examines how such small restaurants in South Korea can improve their profitability by adapting their price increases, table mix, and the equilibrium points of the utilization rates, and reports the following findings based on the analysis of two studies. In Study 1, improving profitability by increasing prices should carefully consider the magnitude and timing. In Study 2, when implementing the table mix strategy, seat occupancy and profit also increase, and we further find the equilibrium points of the utilization rates. Under a queuing system, the utilization rate and average waiting time are also identified as having a trade-off relationship. The results provide insights into how managers of small restaurants with queues can develop efficient revenue management strategies to manage peak hours.

scholarly journals Revenue Management In Manufacturing: A Research Landscape

2010 ◽  
Vol 8 (2) ◽  
Author(s):  
S. Hossein Cheraghi ◽  
Mohammad Dadashzadeh ◽  
Prakash Venkitachalam
Keyword(s):  
Revenue Management ◽  
Airline Industry ◽  
Past History ◽  
Management Strategies ◽  
Demand Forecasting ◽  
Customer Relationship ◽  
Management Systems ◽  
Manufacturing Companies ◽  
Product Availability ◽  
Sales Channels

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt; mso-pagination: none;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">Revenue management is the science of using past history and current levels of order activity to forecast demand as accurately as possible in order to set and update pricing and product availability decisions across various sales channels to maximize profitability. In much the same way that revenue management has transformed the airline industry in selling tickets for the same flight at markedly different rates based upon product restrictions, time to departure, and the number of unsold seats, many manufacturing companies have started exploring innovative revenue management strategies in an effort to improve their operations and profitability. These strategies employ sophisticated demand forecasting and optimization models that are based on research from many areas, including management science and economics, and that can take advantage of the vast amount of data available through customer relationship management systems in order to calibrate the models. In this paper, we present an overview of revenue management systems and provide an extensive survey of published research along a landscape delineated by three fundamental dimensions of capacity management, pricing, and market segmentation.</span></span></p>

Modern Revenue Management Strategies for Railway Slots

2000 ◽  
Vol 33 (9) ◽  
pp. 141-146
Author(s):  
Μ. Schroeder ◽  
I. Braun ◽  
E. Schnieder
Keyword(s):  
Revenue Management ◽  
Management Strategies

The Use of Differential Pricing in Tourism and Hospitality

2019 ◽  
pp. 113-142 ◽  
Author(s):  
Aurelio G. Mauri ◽  
Ruggero Sainaghi ◽  
Giampaolo Viglia
Keyword(s):  
Revenue Management ◽  
Management Strategies ◽  
Hedonic Pricing ◽  
Business Practices ◽  
Academic Literature ◽  
Price Differentiation ◽  
Special Events ◽  
Perceptions Of Fairness ◽  
Differential Pricing ◽  
Tourism And Hospitality

Due to the widespread adoption of revenue management strategies within the hospitality business, pricing has become more and more a central topic both for academics and practitioners. In particular, pricing has evolved towards value-based approaches, dynamic and customized through the use of price differentiation. “Rate fences” are the criteria that hotels adopt to separate customer segments whose service values may differ. The purpose of this chapter is to analyze the academic literature as well as the business practices relating to this subject. The authors propose a logical link between rate fences and the hedonic pricing approach. Main topics are 1) rate fence classifications and 2) the effectiveness of rate fences and their impacts on perceptions of fairness. Overall, this contribution suggests that time-based rate fences are fundamental at the destination level, and they are strictly connected to seasonality. Destinations' policymakers and firms can consider strategies and tools for overcoming seasonality, including special events that may take place in a destination.

Hotel daily occupancy forecasting with competitive sets: a recursive algorithm

2016 ◽  
Vol 28 (2) ◽  
pp. 267-285 ◽  
Author(s):  
Zvi Schwartz ◽  
Muzaffer Uysal ◽  
Timothy Webb ◽  
Mehmet Altin
Keyword(s):  
Revenue Management ◽  
Data Sharing ◽  
Strong Support ◽  
Empirical Support ◽  
Essential Element ◽  
Unique Contribution ◽  
Individual Property ◽  
Content Type ◽  
Novel Approach ◽  
The Absolute

Purpose – This paper aims to improve the accuracy of hotel daily occupancy forecasts – an essential element in the revenue management cycle – by proposing and testing a novel approach. The authors add the hotel competitive-set’s predicted occupancy as an input of the individual property forecast and, using a recursive approach, demonstrate that there is a potential for significant reduction in the forecasting error. Design/methodology/approach – The paper outlines the theoretical justification and the mechanism for this new approach. It applies a simulation for exploring the potential to improve the accuracy of the hotel’s daily occupancy forecasts, as well as analysis of data from a field study of two hotel clusters’ daily forecasts to provide empirical support to the procedure’s viability. Findings – The results provide strong support to the notion that the accuracy could be enhanced. Incorporating the competitive set prediction by using either a genetic algorithm or the simple linear regression model improves the accuracy of the forecast using either the absolute or the absolute percentage as the error measure. Research limitations/implications – The proliferation of data sharing practices in the hotel industry reveals that the timely data sharing-aggregation-dissemination mechanism required for implementing this forecasting paradigm is feasible. Originality/value – Given the crucial role of accurate forecasts in revenue management and recent changes in the hotels’ operating environment which made it harder to achieve or maintain high levels of accuracy, this study’s proposed novel approach has the potential to make a unique contribution in the realm of forecasting daily occupancies.

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