![]() ![]() This section also describes the strategy used for the literature search process performance and criteria that were applied to assess the relevance of analyzed documents. Review methodology ( Section 3) explains the main methods used for the review. ![]() After the Introduction ( Section 1), the Theoretical Background ( Section 2) introduces the concept of Industry 4.0 and discusses the evolution of maintenance approaches, focusing on the Maintenance 4.0 concept. In conclusion, the article is organized into seven sections ( Figure 1). Therefore, it is imperative to examine the main trends occurring in the maintenance area in the context of Maintenance 4.0. In addition, according to the consulting group Next Move Strategy Consulting, the global predictive maintenance market is expected to register a CAGR (Compound Annual Growth Rate) of 30.47% between 20. Following the report, the global predictive maintenance market size was valued at USD 3.18 billion in 2018. According to the authors of a report that surveyed the implementation of maintenance strategies in companies in Belgium, Germany, and the Netherlands, only 11% of respondents (a total of 280 people) indicated that their companies had reached Level 4.0. On the other hand, organizations strive to improve their maturity in implementing maintenance strategies. Known solutions have evolved from Maintenance 1.0 to Maintenance 4.0. Recently, there has been a lot of research and publications in the field of maintenance models and decision-making techniques aimed at improving the efficiency of the maintenance process (for an overview, see, for example, ). The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, (4) Maintenance system architecture, and (5) Cybersecurity in maintenance. This resulted in the selection of the 214 most relevant papers in the investigated area. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. The main inclusion criteria included the publication dates (studies published from 2012–2022), studies published in English, and studies found in the selected databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. ![]() The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. ![]()
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