Selection and industrial applications of panel data based demand forecasting models

Shuyun Ren, Tsan Ming Choi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1131-1159
Number of pages29
JournalIndustrial Management and Data Systems
Volume116
Issue number6
DOIs
Publication statusPublished - 11 Jul 2016

Keywords

  • Data systems
  • Demand forecasting
  • Model selection
  • Panel data forecasting
  • Technical review
  • Use of information

ASJC Scopus subject areas

  • Management Information Systems
  • Industrial relations
  • Computer Science Applications
  • Strategy and Management
  • Industrial and Manufacturing Engineering

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