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Book part
Publication date: 10 June 2021

Michelle (Myongjee) Yoo and Sybil Yang

Forecasting is a vital part of hospitality operations because it allows businesses to make imperative decisions, such as pricing, promotions, distribution, scheduling, and…

Abstract

Forecasting is a vital part of hospitality operations because it allows businesses to make imperative decisions, such as pricing, promotions, distribution, scheduling, and arranging facilities, based on the predicted demand and supply. This chapter covers three main concepts related to forecasting: it provides an understanding of hospitality demand and supply, it introduces several forecasting methods for practical application, and it explains yield management as a function of forecasting. In the first section, characteristics of hospitality demand and supply are described and several techniques for managing demand and supply are addressed. In the second section, several forecasting methods for practical application are explored. In the third section, yield management is covered. Additionally, examples of yield management applications from airlines, hotels, and restaurants are presented.

Details

Operations Management in the Hospitality Industry
Type: Book
ISBN: 978-1-83867-541-7

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Article
Publication date: 27 July 2012

Asli Aksoy, Nursel Ozturk and Eric Sucky

Demand forecasting in the clothing industry is very complex due to the existence of a wide range of product references and the lack of historical sales data. To the…

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2332

Abstract

Purpose

Demand forecasting in the clothing industry is very complex due to the existence of a wide range of product references and the lack of historical sales data. To the authors' knowledge, there is an inadequate number of literature studies to forecast the demand with the adaptive network based fuzzy inference system for the clothing industry. The purpose of this paper is to construct a decision support system for demand forecasting in the clothing industry.

Design/methodology/approach

The adaptive‐network‐based fuzzy inference system (ANFIS) is used for forecasting demand in the clothing industry.

Findings

The results of the proposed study showed that an ANFIS‐based demand forecasting system can help clothing manufacturers to forecast demand more accurately, effectively and simply.

Originality/value

In this study, the demand is forecast in terms of clothing manufacturers by using ANFIS. ANFIS is a new technique for demand forecasting, it combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. The input and output criteria are determined based on clothing manufacturers' requirements and via literature research, and the forecasting horizon is about one month. The study includes the real life application of the proposed system and the proposed system is tested by using real demand values for clothing manufacturers.

Details

International Journal of Clothing Science and Technology, vol. 24 no. 4
Type: Research Article
ISSN: 0955-6222

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Article
Publication date: 14 May 2018

Erik Hofmann and Emanuel Rutschmann

Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics…

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8433

Abstract

Purpose

Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances forecasts’ accuracy.

Design/methodology/approach

A conceptual structure based on the design-science paradigm is applied to create categories for BDA. Existing approaches from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the interplay between elements, and creation of a matching by conducting investigations within the retail industry.

Findings

The developed framework could serve as a guide for meaningful BDA initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments.

Originality/value

So far, no scientific work has analyzed the relation of forecasting methods to BDA; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their operational, tactical, or strategic demand plans.

Details

The International Journal of Logistics Management, vol. 29 no. 2
Type: Research Article
ISSN: 0957-4093

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Article
Publication date: 7 April 2015

Gamze Ogcu Kaya and Omer Fahrettin Demirel

Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring…

Abstract

Purpose

Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization.

Design/methodology/approach

Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines’ spare parts comprising 262 weekly periods from January 2009 to December 2013. The data set are composed of 500 stock-keeping units, so there are 131,000 data points in total.

Findings

From the results of implementation, it is shown that using the optimum parameter values yields better performance for each of the methods.

Research limitations/implications

Although it is an intensive study, this research has some limitations. Since only real data are considered, this research is limited to the aviation industry.

Practical implications

This study guides market players by explaining the features of intermittent demand. With the help of the study, decision makers dealing with intermittent demand are capable of applying specialized intermittent demand forecasting methods.

Originality/value

The study brings simplicity to intermittent demand forecasting work by using commonly used spreadsheet software. The study is valuable for giving insights to market players dealing with items having intermittent demand characteristics, and it is one of the first study which is optimizing the smoothing parameters of the forecasting methods by using spreadsheet in the area of intermittent demand forecasting.

Details

Kybernetes, vol. 44 no. 4
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 11 March 2014

Asli Aksoy, Nursel Öztürk and Eric Sucky

According to literature research and conversations with apparel manufacturers' specialists, there is not any common analytic method for demand forecasting in apparel…

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2174

Abstract

Purpose

According to literature research and conversations with apparel manufacturers' specialists, there is not any common analytic method for demand forecasting in apparel industry and to the authors' knowledge, there is not adequate number of study in literature to forecast the demand with adaptive network-based fuzzy inference system (ANFIS) for apparel manufacturers. The purpose of this paper is constructing an effective demand forecasting system for apparel manufacturers.

Design/methodology/approach

The ANFIS is used forecasting the demand for apparel manufacturers.

Findings

The results of the proposed study showed that an ANFIS-based demand forecasting system can help apparel manufacturers to forecast demand accurately, effectively and simply.

Originality/value

ANFIS is a new technique for demand forecasting, combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. In this study, the demand is forecasted in terms of apparel manufacturers by using ANFIS. The input and output criteria are determined based on apparel manufacturers' requirements and via literature research and the forecasting horizon is about one month. The study includes the real-life application of the proposed system, and the proposed system is tested by using real demand values for apparel manufacturers.

Details

Journal of Modelling in Management, vol. 9 no. 1
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 16 April 2018

Joakim Andersson and Patrik Jonsson

The purpose of this paper is to explore and propose how product-in-use data can be used in, and improve the performance of, the demand planning process for automotive…

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1667

Abstract

Purpose

The purpose of this paper is to explore and propose how product-in-use data can be used in, and improve the performance of, the demand planning process for automotive aftermarket services.

Design/methodology/approach

A literature review and a single case study investigate the underlying reasons for the demand for spare parts by conducting in-depth interviews, observing actual demand-generating activities, and studying the demand planning process.

Findings

This study identifies the relevant product-in-use data and divides them into five main categories. The authors have analysed how product-in-use data are best utilised in planning spare parts with different attributes, e.g. different life cycle phases and demand frequencies. Furthermore, the authors identify eight potentially relevant areas of application of product-in-use data in the demand planning process, and elaborate on their performance effects.

Research limitations/implications

This study details the understanding of what impact context has on the potential performance effects of using product-in-use data in aftermarket demand planning. Propositions generate several strands for future research.

Practical implications

This study shows the potential impact of using product-in-use data, using eight different types of interventions for spare parts, in the aftermarket demand planning.

Originality/value

The literature focusses on single applications of product-in-use data, but would benefit from considering the context of application. This study presents interventions and explores how these enable improved demand planning by analysing usage and effects.

Details

International Journal of Physical Distribution & Logistics Management, vol. 48 no. 5
Type: Research Article
ISSN: 0960-0035

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Article
Publication date: 11 July 2016

Shuyun Ren and Tsan-Ming Choi

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and…

Abstract

Purpose

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

Design/methodology/approach

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

Findings

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

Research limitations/implications

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

Practical implications

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

Originality/value

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

Details

Industrial Management & Data Systems, vol. 116 no. 6
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 1 April 2000

Martha A. O’Mara

Planning for future real estate and facility needs in a highly uncertain competitive environment can benefit from a four‐stage process of demand forecasting. Based upon…

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1468

Abstract

Planning for future real estate and facility needs in a highly uncertain competitive environment can benefit from a four‐stage process of demand forecasting. Based upon research conducted within the Corporate Real Estate Portfolio Alliance and a review of general business forecasting techniques, each successive stage relies on more abstract data and increased dialogue about business strategy with the lines of business as uncertainty about the future increases. Each stage requires increasing flexibility in the supply of real estate as the range of probabilities around the forecast widens.

Details

Journal of Corporate Real Estate, vol. 2 no. 2
Type: Research Article
ISSN: 1463-001X

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Article
Publication date: 25 May 2018

Ann Vereecke, Karlien Vanderheyden, Philippe Baecke and Tom Van Steendam

The purpose of this paper is to develop and empirically validate a model for assessing demand planning maturity in organisations.

Downloads
2046

Abstract

Purpose

The purpose of this paper is to develop and empirically validate a model for assessing demand planning maturity in organisations.

Design/methodology/approach

The authors developed a maturity assessment model for demand planning through iterations of theoretical and empirical work, combining insights from literature and practitioners. An online survey is developed to validate the model using data from different industries.

Findings

The authors identify six dimensions of demand planning maturity: data management, the use of forecasting methods, the forecasting system, performance management, the organisation and people management. The empirical study indicates that demand data are well managed and organisation readiness is high, yet improvements in the forecasting system and the management of forecast performance are needed. The results show a positive relationship between the size of an organisation and its demand planning maturity.

Practical implications

The contribution of this work is to propose an assessment model and survey instrument for demand planning maturity. This will help the practitioner to understand the current level of maturity of the demand planning process, reflect on the desired level and develop action plans to close the gap.

Originality/value

There is broad literature on process maturity assessment in general and on sales and operations planning (S&OP) maturity in particular. However, there is no comprehensive model for assessing the maturity of demand planning, which is a specific and critical process within the overall S&OP process. The authors fill this gap by offering a demand planning maturity model.

Details

International Journal of Operations & Production Management, vol. 38 no. 8
Type: Research Article
ISSN: 0144-3577

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Article
Publication date: 16 March 2012

Mikihisa Nakano and Nobunori Oji

The purpose of this paper is to extract some implications for managing the transition process of demand forecasting.

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1773

Abstract

Purpose

The purpose of this paper is to extract some implications for managing the transition process of demand forecasting.

Design/methodology/approach

Using case study methodology, this paper describes a case of the transition from a judgmental to an integrative method in demand forecasting at Kao Corporation in Japan and extracts useful implications from the case.

Findings

Even if the forecaster and user are not the same, it is found that firms can realize an integrative method of using judgment as input to model building through effective transition management of demand forecasting.

Research limitations/implications

The results of this paper are from a case study. To examine the validity and effectiveness, future research needs to continue case studies and search for cross‐case patterns.

Practical implications

In the transition process of demand forecasting, it is very useful for firms that the forecaster demonstrates the benefits of new forecasting methods through experiential initiatives, solves various problems with the user at the beginning of the transition process, and creates opportunities so that the user experientially acquires the technical knowledge of the forecaster.

Originality/value

Through describing a case of the transition process of demand forecasting in detail, this paper finds useful means for managing the transition process of demand forecasting.

Details

International Journal of Operations & Production Management, vol. 32 no. 4
Type: Research Article
ISSN: 0144-3577

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