Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61776
Title: Selection and industrial applications of panel data based demand forecasting models
Authors: Ren, S
Choi, TM 
Keywords: Data systems
Demand forecasting
Model selection
Panel data forecasting
Technical review
Use of information
Issue Date: 2016
Publisher: Emerald Group Publishing Limited
Source: Industrial management and data systems, 2016, v. 116, no. 6, p. 1131-1159 How to cite?
Journal: Industrial management and data systems 
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 variousmain streampanel data-based demand forecastingmodels. 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.
URI: http://hdl.handle.net/10397/61776
ISSN: 0263-5577
EISSN: 1758-5783
DOI: 10.1108/IMDS-08-2015-0345
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