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A systematic literature review for industry 4.0 maturity modeling: state-of-the-art and future challenges

Kerem Elibal (BCS Metal Co., Gaziantep, Turkey)
Eren Özceylan (Gaziantep Universitesi, Gaziantep, Turkey)

Kybernetes

ISSN: 0368-492X

Article publication date: 28 December 2020

Issue publication date: 16 November 2021

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Abstract

Purpose

The purpose of this paper is to conduct a systematic literature review for industry 4.0 maturity modeling research studies to obtain a clear view of the current state-of-the-art. Identifying characteristics of the studies; gaps, limitations and highlighted features has been aimed to guide future research studies.

Design/methodology/approach

The study includes a systematic literature review conducted on Scopus, IEEE Xplore and Web of Science databases and 90 publications have been reviewed. A novel qualitative taxonomy has been constructed which aims to reduce the cognitive load of the readers.

Findings

While industry 4.0 maturity modeling is an emerging concept and taking researchers’ attraction, review studies are still in infancy. Current review papers are inadequate in getting a clear idea about the concept, especially from the perspective of guiding future researchers. By the conducted approach of classification conducted in this paper, it has been seen that there are some challenges for improving the industry 4.0 maturity modeling.

Research limitations/implications

Findings represented in this study can serve academicians and practitioners to develop and/or improve industry 4.0 maturity models.

Originality/value

The study includes a novel classification for the reviewed papers. Constructed taxonomy is among the first and tabular representations instead of prose analogy that aims to simplify the review of papers.

Keywords

Acknowledgements

The authors thank the two anonymous reviewers for useful comments that have led to an improvement in the paper.

Citation

Elibal, K. and Özceylan, E. (2021), "A systematic literature review for industry 4.0 maturity modeling: state-of-the-art and future challenges", Kybernetes, Vol. 50 No. 11, pp. 2957-2994. https://doi.org/10.1108/K-07-2020-0472

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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