The paper aims to investigate the reasons why Augmented Reality (AR) has not fully broken the industrial market yet, or found a wider application in industries. The main research question the paper tries to answer is: what are the factors (and to what extent) that are limiting AR? Firstly, a reflection on the state of art of AR applications in industries is proposed, to discover the sectors more commonly chosen for deploying the technology so far. Later, based on a survey conducted after that, three AR applications have been tested on manufacturing, automotive, and railway sectors, and the paper pinpoints key aspects that are conditioning its embedding in the daily working life. In order to compare whether the perception of employees from railway, automotive, and manufacturing sectors differs significantly, a one-way analysis of variance (ANOVA) has been used. Later, suggestions are formulated in order to improve these aspects in the industry world. Finally, the paper indicates the main conclusions, highlighting possible future researches to start.

In the last few decades, several initiatives and approaches are set up to support maintenance procedures for the railway industry in adopting the principles of Industry 4.0. Contextualized maintenance technologies such as Augmented Reality (AR) overlay can integrate virtual information on physical objects to improve decision-making and action-taking processes. Operators work in a dynamic working environment requiring both high adaptive capabilities and expert knowledge. There is a need to support the operators with tailor-based information that is customized and contextualized to their expertise and experience. It calls for AR tools and approaches that combine complex methodologies with high usability requirements. The development of these AR tools could benefit from a structured approach. Therefore, the objective of this paper is to propose an adaptive architectural framework aimed at shaping and structuring the process that provides operators with tailored support when using an AR tool. Case study research is applied within a revelatory railway industry setting. It was found that the framework ensures that self-explanatory AR systems can capture the knowledge of the operator, support the operator during maintenance activities, conduct failure analysis, provide problem-solving strategies, and improve learning capabilities. This study contributes to the necessity of having a human-centered approach for the successful adaption of AR technology tools for the railway industry.

Knowledge management (KM) can facilitate collaboration and enhance innovation. To manage knowledge, organisations typically implement knowledge management systems (KMSs). It has been shown that KMS facilitates the management of explicit knowledge. However, KMSs are at times not embraced because of issues such as long file search times and information overload. Moreover, managing tacit knowledge via KMS is hardly discussed in literature. To shed light on these issues, this paper conducts a case study at a railway infrastructure manager. Three key practical problems have been identified that hinder current KMS use and sharing of tacit knowledge. To overcome these problems, a KM platform was developed, and its usability was assessed with 17 experts. A paired-samples t-test reveals that the developed platform reduces the file search time compared to the current solution. Moreover, structured interviews show that the lesson-learned site component contributes to improved learning, and the KM platform facilitates collaboration building.

In response to the industrial world becoming more complex and the recent social distancing caused by the COVID-19 outbreak, emerging technologies such as Virtual Reality (VR) are gaining importance for industrial training. The novelty, however, does not lie in the sole use of VR-based technology but rather in its adaptive and flexible nature. This paper explores the synergy between VR environments and adaptive-based learning and tests the proposed approach in the railway sector. An iterative co-design approach is adopted to develop a VR-based training system for educating train conductors. The developed system is validated with ten conductors, where their learning preference, learning outcome, and learning performance were assessed. The results show an increase in individual motivation and perceived usefulness for the developed VR-based training system, demonstrating a higher learning preference, in comparison to the currently used physical simulator. Similarly, the results outline a knowledge gain in two out of six key knowledge subjects for conductor education and indicate that time improvements per exercise are needed to meet organizational performance goals. Consequently, this research presents an attractive alternative to costly and product-specific physical simulators and offers three strategic managerial implications opening new opportunities for using VR in developing learning training programmes.

Purpose: The purpose of this paper is to create a framework to provide a scalable maintenance program for unmanned aircraft systems (UAS) in order to choose the most suitable and feasible maintenance strategy in terms of reliability. Design/methodology/approach: The paper opted for a reliability-centered maintenance-based approach to develop the framework using a UAS as the starting point of the research. A linear and user-friendly design of the methodology based on a Boolean flowchart was chosen in order to lead the analyst through the process avoiding as much as possible subjectivity decision-making issues. Finally, the framework was, on the component level, performed by a UAS company gathering feedback on its applicability. Findings: An agile and structured decision-making framework for developing scalable maintenance program of UAS is provided. The proposed solution gives the opportunity to tailor the maintenance strategy to the technical characteristics, considering not only the single component but also situations and conditions in which the machine will operate. Research limitations/implications: Because of the chosen research approach, the framework is potentially applicable to every UAS. A first trial of the method was run on a multirotor vehicle equipped with eight electric brushless motors. Further studies focused on different UAS will be mandatory in order to obtain comparable and robust findings and a reliable approach. Practical implications: This study offers a different scheme to elaborate a specific maintenance solution related to the characteristics of the system. It strives to remedy the drawbacks of the traditional approach for a manned aircraft not completely suitable for systems with such different functions, features and tasks. The authors believe that the method presented in this paper will provide a new selection tool for choosing maintenance actions based on the features of the UAS. Originality/value: This paper provides a new and usable solution to include the maintenance actions in the management of pioneering products. In spite of the maintenance program representing an essential aspect to provide reliable assets, frameworks to create programs and to help manufacturers and users are still difficult to find or to apply to different UAS. This gap enhances the misunderstanding that the maintenance is not required or essential for the unmanned aircrafts management.

NedTrain is the Netherlands Railway's subsidiary responsible for rolling stock maintenance. The life cycle is 30-40 years where the asset condition is maintained to meet it's performance requirements and is enhanced to meet the customer expectations through it's life. The life cycle costs are roughly 1/3 for the new build investment and 2/3 for subsequent maintenance and further investments. Currently the rolling stock is maintained in fixed maintenance intervals of 3 months with in between service-checks and repairs. A more dynamic and pro-active maintenance approach is required due to the increased complexity of electronics and software, planning of maintenance in short intervals when less rolling stock is required for service and increased customer expectations. NedTrain started an innovation programme that includes remote condition monitoring of the asset condition of individual trains and use of the information to prepare maintenance activities before the train comes in for maintenance. This paper describes the work in progress in three case studies for existing rolling stock, mid-life refurbishment and new build rolling stock