This study examines the role of digital capabilities in facilitating organizational transformation within the maintenance domain, focusing on the need for transitioning from Industry 4.0 to Industry 5.0. The evolving concept of Industry 5.0 complements Industry 4.0 by shifting the focus from automation to human-machine collaboration. Industry 5.0 aims to create a more cooperative relationship between humans and machines to maximize productivity and innovation. To facilitate digital transformation, companies need to develop suitable digital capabilities. Through interviews with maintenance specialists and practitioners, we identified key challenges and facilitators to implementing emerging technologies. Key findings highlight the importance of providing high-quality education, aligning digital tools with work tasks, and offering user-friendly digital solutions. Furthermore, the findings emphasize the need for robust information sharing, cooperation, and the formalization of responsibilities between maintenance and production functions. Lastly, establishing a clear digital strategy, commitment to investments, and testing new technologies are crucial for facilitating digital transformation and promoting a culture of innovation.

This article underscores the importance of integrating machine learning analytics to enhance preventive maintenance methodologies, particularly emphasizing condition-based maintenance (CBM) within the wind energy domain. Through empirical evidence derived from wind turbine data, the paper outlines the efficacy and applicability of Machine Learning in Operations (MLOps) forpredicting the residual operational life of wind turbine bearings. While the study's principal domain is renewable energy, especially wind power, it employs a specific wind turbine dataset for exhaustivemodel testing, leading to the proposition of an innovative ensemble model tailored for high-speedwind turbine bearing prognosis. The introduced model, "The Data Interpretation Algorithm for Forecasting Time Series" (DIAFS), crafted for assessing wind turbine bearing conditions, is predicated on an adaptive polynomial model approximation. It emerges as an indispensable asset for maintenance professionals implementing CBM methodologies

Gravel roads form a significant share of the global road network, usually in sparsely populated rural areas. They are important, especially in agriculture, tourism, and forestry, connecting rural to urban areas. This systematic literature study comprises 105 reviewed publications on gravel road maintenance. Review articles on maintenance management practices, especially concerning objective condition assessment and data-driven methods (DDMs), are lacking. Therefore, this review provides a concise overview of current gravel road maintenance practices and ongoing research on objective condition assessment and DDMs for gravel road maintenance. It offers researchers in gravel road maintenance and other related fields a clear indication of where to focus their research efforts, as it suggests the direction for future research. Visual assessment methods are predominant for monitoring the condition of gravel roads, while objective methods and DDMs are not common. Research on gravel roads and their maintenance has increased in the last two decades, especially in North America and Northern Europe. Condition assessment is shifting from subjective to objective methods, utilizing knowledge from technological advancements in image processing, vibration and acoustics analysis, and so forth. There are some excellent research initiatives for objectively assessing the condition of gravel roads and DDMs, but the practical implementation is limited. Implementing objective assessment methods and DDMs generally improves the management of gravel roads with regard to decision-making, maintenance costs, safety, and the stability and comfort of the ride. Objective condition assessment and DMs have the potential to enhance maintenance practices in the maintenance of gravel roads.

The efficient maintenance of gravel roads depends on digital tools and data as well as information systems adapted for specific needs while conforming with current standards for better interconnectivity. Within gravel road maintenance, cloud-based solutions are a key alternative. This because necessary information, such as road condition data, could be shared throughout the network and provide the opportunity to coordinate maintenance activities between stakeholders. Effective standards are essential since they permit various services with the ability to work together while at the same time supporting differences that ease competition and innovation. Among several maintenance standards, the OSA-CBM and MIMOSA CRIS database schema are intended for handling information exchange and communication within and between systems supporting condition-based maintenance. This paper tests the usability of these standards for translating context-dependent information into a standardized data set. The domain studied is gravel road maintenance. Critical data was gathered and elicited from different stakeholders and represented in the form of a conceptual information model. This paper exemplifies how the conceptual model could be mapped into a logical model with respect to the CRIS information model. The main results are in the form of a conceptual and logical information model for purposes of gravel road maintenance, as well as a procedure suggested for mapping the information model following specific steps. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The gravel road network is an important function for rural residents and entrepreneurs. Traditional maintenance of gravel roads is well-functioning but provides a relatively high maintenance cost per unit length of the road, and every maintenance action as well as extraction and transport of new gravel contributes to increased climate impact and resource depletion. Today, maintenance planning is carried out periodically based on the maintenance history, which also is reflected in the economic models and procurement methods. Current maintenance plans may be enhanced and will not be a reliable basis in the future, e.g. due to climate change. Instead, real needs and conditions must be given greater consideration. Today, appropriate maintenance management systems are lacking, e.g. in order to be able to evaluate maintenance deficiencies, prioritize objects and choose the appropriate maintenance action. Moreover, the knowledge available at specific stakeholders is not shared with other actors. In this paper, an Information and Communication Technology (ICT) system for gravel road maintenance is proposed in the form of a cloud-based system covering the information needs of stakeholders in the gravel road maintenance ecosystem. Requirement specifications are given for the sub-systems intended for the maintenance executioner and the maintenance planner. The specifications are based on workshops and interviews conducted with stakeholders, where requirements were acquired e.g. in the form of User stories.

The current article highlights the specific challenges and issues of the healthcare system in Europe. In addition, the particular factors towards the digitalization of the domain are highlighted, and the emerging technologies contribute to this process because many things are becoming more feasible. Thus, information and communication technologies (ICTs), such as new sensors, machine learning, big data, and analytics, provide new opportunities and challenges in their implementation and use. Therefore, it has become crucial to understand the different kinds of ICTs, such as artificial intelligence (A.I) techniques, especially machine learning algorithms and their use in the domain of interest. Thus, the paper aims to understand the mentioned technologies and their implementation in the area of interest to comprehend their current status, their suitability, and what needs to be considered for their successful development and implementation. While at the same time taking into account several key aspects that need to be well-thought-out in the domain. Consequently, the author performs a conceptual literature review of relevant scientific articles where sensors, machine learning, data mining, statistical learning, etc., have been tested and utilized in the eHealth area, especially for fall prevention and fall risk assessment. Finally, the literature findings are discussed, and the factors to consider when applying machine learning for fall prevention and fall risk assessment are underscored. © 2022 Copyright for this paper by its authors.

The gravel road network is an essential function for rural residents and entrepreneurs. Traditional maintenance of gravel roads is well-functioning but provides a relatively high maintenance cost per unit length of the road. In addition, every maintenance action and extraction and transport of new gravel contribute to increased climate impact and resource depletion. Today, maintenance planning is carried out periodically based on the maintenance history, which also is reflected in the economic models and procurement methods. Current maintenance plans may be enhanced and will not be a reliable basis in the future, for instance due to climate change. Instead, real needs and conditions must be given greater consideration. Today, appropriate maintenance management systems are lacking, e.g., to be able to evaluate maintenance deficiencies, prioritize objects and choose the appropriate maintenance action. Moreover, the knowledge available at specific stakeholders is not shared with other actors. In this paper, an Information and Communication Technology (ICT) system for gravel road maintenance is proposed in the form of a cloud-based system covering the information needs of stakeholders in the gravel road maintenance ecosystem. Requirement specifications are given for the subsystems intended for the maintenance executioner and the maintenance planner. The specifications are based on workshops and interviews conducted with stakeholders, where requirements were acquired using techniques such as User stories, Use case scenarios, and mock-up prototypes. System examples corresponding to the requirements specifications are also given.

Gravel roads are important assets forgeographically sparse countries, but the maintenance of theseroads is costly and inefficient. In addition, as failure developmentis highly affected by environmental factors, the planning shouldbe dynamic for reaching efficiency and effectiveness, which isachieved by data-driven maintenance approaches. This paperproposes applying a data-driven approach in gravel roadmaintenance following the steps of the OSA-CBM specifications.The conceptual approach is developed and illustrated based on thefindings of an extensive literature review. The approach thuscontextualises OSA-CBM in gravel road maintenance and pointsout further development and research areas. It was found that theresearch has mainly focused on data acquisition techniques, roadcondition classification, diagnostics, and deterioration models,while data manipulation methods and prognostic models forgravel roads are rather unresearched areas. In addition, a holisticapproach towards data-driven maintenance of gravel roads iscurrently lacking. In this perspective, the approach presented inthis paper could serve as a base for the further development ofdata-driven methods to reach efficient and effective gravel roadmaintenance practices.

This paper reports on an empirical investigationof functionality and information requirements for gravel roadmaintenance following a stakeholder-based requirementelicitation process. Results in the form of use case diagrams,prototypes, as well as a database model describing informationneeds for gravel road maintenance are presented. The noveltyof the database is highlighted by describing new features thatare enabled by the database, such as predictive maintenancemanagement using road condition information or thecoordination of maintenance tasks between the contractor andthe customer.

The efficient project management of information systems (IS) and information technology (IT) projects is a crucial factor since, in many cases, they fail to meet the expected demands and requirements of the different stakeholders’. It is well known that poor project management, like inadequate risk management and insufficient project planning, is one of the main reasons for ICT project failure. An understanding of the digitalisation and the ICTs digital capabilities, i.e. adoption of disruptive technologies are crucial as well for its successful implementation. Thus, with the emergence of the new ICTs such as the Internet of Things, cloud computing, and big data, the digitalisation of the maintenance domain has become even more complicated. It is, therefore, essential to revisit standard processes when carrying out software development projects for the area of interest. A broad selection of software development methodologies exists today. However, these are general-purpose development approaches, and it is, therefore, crucial to have an understanding of the best options and best practices of the existent methodologies for context-specific development projects. Thus, the authors present a literature review of ICT project methodologies and later discuss these findings taking into consideration the characteristics of the domain of interest. Based on this, an ICT project management methodology suitable for the Industrial maintenance domain is suggested.