Applying forecasting Revenue Management techniques to enrolment management in a university centre

Revenue Management methodology is shown to be an innovative application for fixed capacity firms or service entities which require a management system that supports pricing and inventory decision-making. The present research study takes one of the elements of this philosophy – demand forecasting and estimation - as a theoretical basis and applies it to the case study. The aim is to obtain the predicted demand of enrolments in each academic year. This will support the improvement and optimisation of the operations related to capacity management in a university centr

New forecasting methods for hotel revenue management systems

An accurate forecasting module is a key element of any revenue management system. This module includes demand forecasting, which involves tasks of forecasting complex seasonal time series. The challenge of producing accurate demand forecasts requires the application of suitable forecasting methods to address that complexity. The aim of this paper is to evaluate a new innovation state space modeling framework, based on innovations approach, developed for forecasting time series with complex seasonal patterns. This modeling framework provides an alternative to existing models of exponential smoothing, since it is capable of tackling seasonal complexities such a multiple seasonal periods and high frequency seasonality.info:eu-repo/semantics/publishedVersio

A K-means Group Division and LSTM Based Method for Hotel Demand Forecasting

The accuracy of hotel demand forecasting is affected by factors such as the completeness of historical data and the maturity of models. Most of the existing methods are based on rich data, without considering that single hotels may only obtain sparse data. Therefore, a K-means group division and Long Short-Term Memory (LSTM) based method is proposed in this paper. Guest types are introduced into the forecasting to provide reference for hotel\u27s further decision-making. Using an example of 1493 hotels in Europe, we divide hotel groups and forecast the flow of leisure and business guests. The experimental results show that, compared with the benchmark models, LSTM can improve the forecasting performance of hotel group; compared with single hotels, the forecasting of hotel groups can effectively avoid inaccuracy caused by sparse data. The results can provide necessary reference for hospitality to make decisions based on guest types

ОПТИМИЗАЦИЯ ГИБКИХ ЦЕН ГОСТИНИЧНЫХ НОМЕРОВ

An approach to solvе a problem of determining optimal dynamic prices for hotel rooms is suggested. It includes selection of input parameters for the succeeding mathematical analysis, disaggregation of the demand into several categories, demand forecasting, simulation of demand- price relations, and a mathematical programming model for price optimization.Предлагается подход к решению задачи отыскания гибких цен гостиницы, максимизирующих прибыль от сдачи номеров гостям. Подход включает определение входных параметров для последующего математического анализа, разделение спроса на несколько категорий, прогнозирование спроса, определение коэффициентов функции спроса и решение задачи математического программирования для оптимизации цен

Development of demand forecasting model for new product

Forecasting new product sales or service is a critical process in marketing strategies and product performance for an organisation. There are several methods to forecast new product sales or service and the common method used in industry nowadays is Bass Diffusion Model. Since the development of the Bass Diffusion Model in 1969, innovation of new diffusion theory has sparked considerable research among marketing science scholars, operational researchers and mathematicians. This research uses basic Bass Diffusion Model and the model is modified to analyse and forecast the vehicle demand in Malaysia. The objective of the proposed model is to represent the level of spread for the demands of new cars in the society in terms of a simple mathematical function. Since the amounts of available data are limited, a modified Bass Diffusion Model is developed to forecast the demand of new products. The selections of analogous product, parameter estimation method and different value potential market are discussed. A procedure of the proposed diffusion model is presented and the parameters of the model are estimated. The results obtained by applying the proposed model and numerical calculation show that the modified Bass Diffusion Model is robust and effective to forecast the demand of new product sales. This research concludes that the proposed modified Bass Diffusion Model has significantly contributed to forecast the level of spread for new product

Integrated computational intelligence and Japanese candlestick method for short-term financial forecasting

This research presents a study of intelligent stock price forecasting systems using interval type-2 fuzzy logic for analyzing Japanese candlestick techniques. Many intelligent financial forecasting models have been developed to predict stock prices, but many of them do not perform well under unstable market conditions. One reason for poor performance is that stock price forecasting is very complex, and many factors are involved in stock price movement. In this environment, two kinds of information exist, including quantitative data, such as actual stock prices, and qualitative data, such as stock traders\u27 opinions and expertise. Japanese candlestick techniques have been proven to be effective methods for describing the market psychology. This study is motivated by the challenges of implementing Japanese candlestick techniques to computational intelligent systems to forecast stock prices. The quantitative information, Japanese candlestick definitions, is managed by type-2 fuzzy logic systems. The qualitative data sets for the stock market are handled by a hybrid type of dynamic committee machine architecture. Inside this committee machine, generalized regression neural network-based experts handle actual stock prices for monitoring price movements. Neural network architecture is an effective tool for function approximation problems such as forecasting. Few studies have explored integrating intelligent systems and Japanese candlestick methods for stock price forecasting. The proposed model shows promising results. This research, derived from the interval type-2 fuzzy logic system, contributes to the understanding of Japanese candlestick techniques and becomes a potential resource for future financial market forecasting studies --Abstract, page iii

A call for exploratory data analysis in revenue management forecasting: A case study of a small and independent hotel in The Netherlands

Using five years of data collected from a small and independent hotel this case study explores RMS data as a means to seek new insights into occupancy forecasting. The study provides empirical evidence on the random nature of group cancellations, an important but neglected aspect in hotel revenue management modelling. The empirical study also shows that in a local market context demand differs significantly per point of time during the day, in addition to seasonal monthly and weekly demand patterns. Moreover, the study presents evidence on the nonhomogeneous Poisson nature of the probability distribution that demand follows, a crucial characteristic for forecasting modelling that is generally assumed but not reported in the hotel forecasting literature. This implies that demand is more uncertain for smaller than for larger hotels. The paper concludes by drawing attention to the critical and often overlooked role of exploratory data analysis in hotel revenue management forecasting.Coherent privaatrech

Dynamic pricing with demand disaggregation for hotel revenue management

In this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic

Big data in hotel revenue management: exploring cancellation drivers to gain insights into booking cancellation behavior

n the hospitality industry, demand forecast accuracy is highly impacted by booking cancellations, which makes demand-management decisions difficult and risky. In attempting to minimize losses, hotels tend to implement restrictive cancellation policies and employ overbooking tactics, which, in turn, reduce the number of bookings and reduce revenue. To tackle the uncertainty arising from booking cancellations, we combined the data from eight hotels’ property management systems with data from several sources (weather, holidays, events, social reputation, and online prices/inventory) and machine learning interpretable algorithms to develop booking cancellation prediction models for the hotels. In a real production environment, improvement of the forecast accuracy due to the use of these models could enable hoteliers to decrease the number of cancellations, thus, increasing confidence in demand-management decisions. Moreover, this work shows that improvement of the demand forecast would allow hoteliers to better understand their net demand, that is, current demand minus predicted cancellations. Simultaneously, by focusing not only on forecast accuracy but also on its explicability, this work illustrates one other advantage of the application of these types of techniques in forecasting: the interpretation of the predictions of the model. By exposing cancellation drivers, models help hoteliers to better understand booking cancellation patterns and enable the adjustment of a hotel’s cancellation policies and overbooking tactics according to the characteristics of its bookings.info:eu-repo/semantics/acceptedVersio

ОБЗОР МОДЕЛЕЙ УПРАВЛЕНИЯ ДОХОДНОСТЬЮ В ГОСТИНИЧНОМ БИЗНЕСЕ

The paper gives a brief scope of Hotel Revenue Management theory and discusses its basic def-initions. It also suggests a new classification of Revenue Management processes and surveys dynamic pricing, forecasting and optimization models employed in Hotel Revenue Management. Last section proposes promising directions of future research.Приводится краткое описание теории управления доходностью в гостиничном бизнесе, рас-крываются основные понятия. Предлагается новая классификация процессов управления доход-ностью, дается обзор литературы по динамическому ценообразованию, методам прогнозирования и оптимизационным моделям, применяемым в управлении доходностью в гостиничном бизнесе. Указы-ваются перспективные направления будущих исследований