Industry 4.0 as a data-driven paradigm: a systematic literature review on technologies

Abstract

PurposeThe purpose of this paper is to identify current technologies related to Industry 4.0 and to develop a rationale to enhance the understanding of their functions within a data-driven paradigm.Design/methodology/approachA systematic literature review of 119 papers published in journals included in the Journal Citation Report (JCR) was conducted to identify Industry 4.0 technologies. A descriptive analysis characterizes the corpus, and a content analysis identifies the technologies.FindingsThe content analysis identified 111 technologies. These technologies perform four functions related to data: data generation and capture, data transmission, data conditioning, storage and processing and data application. The first three groups consist of enabling technologies and the fourth group of value-creating technologies. Results show that Industry 4.0 publications focus on enabling technologies that transmit and process data. Value-creating technologies, which apply data in order to develop new solutions, are still rare in the literature.Research limitations/implicationsThe proposed framework serves as a structure for analysing the focus of publications over time, and enables the classification of new technologies as the paradigm evolves.Practical implicationsBecause the technical side of the new production paradigm is complex and represents an evolving field, managers benefit from a simplified and data-driven approach. The proposed framework suggests that Industry 4.0 should be approached by looking at how data can create value and at what role each technology plays in this task.Originality/valueThe study makes a direct link between Industry 4.0 technologies and the key resource of this revolution, i.e. data. It provides a rationale that not only establishes relationships between technologies and data, but also highlights their roles as enablers or creators of value. Beyond showing the current focus of Industry 4.0 publications, this paper proposes a framework that is useful for tracking the evolution of the paradigm.