Early Warning Systems in Industry 4.0: A Bibliometric and Topic Analysis

Early Warning Systems in Industry 4.0: A Bibliometric and Topic Analysis

Tine Bertoncel (University of Primorska, Koper, Slovenia) and Maja Meško (University of Primorska, Koper, Slovenia)
Copyright: © 2019 |Pages: 15
DOI: 10.4018/IJESMA.2019040104

Abstract

This article is based on a literature search, using the keywords: Industry 4.0, smart manufacturing, smart factory, early warning systems and decision making in the Web-of-Science (WoS) database. The authors conducted a bibliometric and topic analysis that covered the year of publication, country of publication and the frequency with which authors, institutions/organizations, journals and phrases appeared in the literature. Based on the research, it was found that research on EWS in Industry 4.0 mainly focused on predictive maintenance. Security threats, workplace safety and managerial early warning systems (MEWS) were also present in the literature on EWS, however to a much lesser degree. The article concludes by discussing the importance of this bibliometric study within the context of MEWS in Industry 4.0 and Industry 4.0 in general.
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Introduction

Bibliometric research began gaining popularity from the 1970s onward as a method of quantitative analysis of literature (Patra et al., 2005; Bilas and Moutusi, 2013; Mallig, 2010; Belter, 2015; Ellegaard and Wallin, 2015). Bibliometric analysis can be conducted by searching online databases, such as Web of Science and Scopus, after which the found literature is analyzed with the help of software specifically designed for bibliometric analysis (Ellegaard and Wallin, 2015). Bibliometric studies initially began as a result of the advent of communication technologies and the internet, which in turn contributed to the development of bibliographic databases. In bibliographic databases different kinds of information can be found, such as information on the authors of a study, country of origin, institution involved in the publication of certain articles, impact factors and other relevant information (Patra et al., 2005; Belter, 2015; Ellegaard and Wallin, 2015).

Bibliometric studies present knowledge-based organizations with a useful tool, whereby knowledge-workers, who use their skills, experience and abilities to gather information, can look at what a bibliometric analysis has to say about the current situation of research being done, in regard to their specific industry (Leon, 2013; Patra et al., 2005; Belter, 2015; Ellegaard and Wallin, 2015). Bibliometric analysis can also be complemented with database tomography (DT), which was initially proposed by Kostoff (2000). DT uses data from scientific articles to look for phrase frequencies, which gives information on how often phrases occur, as well as information on the pervasiveness of themes. Secondly, DT also helps determine how close together these phrases occur, giving information on the relationship between various phrases and words (Kostoff, 2000).

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