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Predictive maintenance (PDM) is emerging as a strong transformative tool within Industry 4.0, enabling significant improvements in the sustainability and efficiency of manufacturing processes. This in-depth literature review, which follows the PRISMA 2020 framework, examines how PDM is being implemented in several areas of the manufacturing industry, focusing on how it is taking advantage of technological advances such as artificial intelligence (AI) and the Internet of Things (IoT). The presented in-depth evaluation of the technological principles, implementation methods, economic consequences, and operational improvements based on academic and industrial sources and new innovations is performed. According to the studies, integrating CDM can significantly increase machine uptime and reliability while reducing maintenance costs. In addition, the transition to PDM systems that use real-time data to predict faults and plan maintenance more accurately holds out promising prospects. However, there are still gaps in the overall methodologies for measuring the return on investment of PDM implementations, suggesting an essential research direction.

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To meet the growing demand of robotics applications in industry high efforts are placed on research and education for robotics fundamentals, as a more intuitive and easier access to robotics must be ensured. Based on this, the aim of this work is to make a contribution to investigate new approaches for teaching robotics fundamentals using immersive technologies. In this work an existing digital twin for a desktop robot was extended by adding a feature for the positioning of way-points for path planning in an existing Augmented Reality (AR) application as well as update the robot controller with the inverse kinematics for the corresponding motion implementation. The performance of the new functionality was evaluated by means of a pick-and-place application and validated via a test series with an associated questionnaire. The conducted experiments have been carried out in a comparative study between the AR based digital twin and an ABB industrial robot where a clear benefit of the proposed systems for entry into robotics as well as for teaching was identified. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Information Systems (IS) projects are critical for organizational development, both in the private and public sectors. The relevance and complexity inherent in this type of project require management to be fully aware of the factors that influence success. This study contributes to the literature on public-sector IS project management by providing a comprehensive set of Success Factors (SFs) for different levels of the administration. The research method comprised a literature review, six case studies of central government, local government, and other types of administration, and a questionnaire-based survey of public sector IS experts. Forty-four SFs were identified, described, and organized in nine categories: organization and environment; strategy; project; scope; project manager and project team; stakeholders; vendors; clients and users; and monitoring and control. Our results add a new perspective to the theoretical body of knowledge on the SFs for IS projects in the public sector.

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