Scope

The study of digital transformation in the context of industry and infrastructure is highly topical and of great interest. One of the business areas expected to experience the greatest impact is maintenance. It is therefore essential to understand how maintenance can benefit from this transformation and how it can be effectively implemented.

Key questions include:

• What new technologies and tools have the greatest potential impact on maintenance, and why?
• How can the digital transformation process be effectively managed and implemented?
• How will emerging asset management platforms, intelligent maintenance applications, and digital twin technologies transform organizations?

The emergence of new technologies has made the maintenance landscape increasingly complex, requiring the efficient management of large volumes of information, predictive alarms, and dynamic maintenance schedules. However, the complexity of optimizing maintenance decision-making often limits the practical impact of these technologies. As a result, conventional maintenance practices remain predominant, delaying digitalization and reducing the expected return on investment.

Overcoming this resistance to change is particularly important with the emergence of advanced technologies such as Digital Twins, which introduce a new level of complexity through System of Systems (SoS) approaches.

The human dimension is equally critical. Digital transformation offers an opportunity to combine employees’ expertise and experience with new digital capabilities, creating a significant competitive advantage for innovation and technological progress.

These challenges are even greater for assets and systems with limited levels of digitalization. Such assets often represent a large proportion of existing industrial infrastructure, while valuable knowledge about their degradation and maintenance remains primarily in the hands of experienced personnel. Capturing and transferring this knowledge into Digital Twin environments and intelligent maintenance applications is essential to fully realize the benefits of digital transformation.

This seminar aims to bring together researchers, practitioners, and industry experts to exchange ideas and recent advances in digital maintenance, digital twins, complex systems, and human-centered digital transformation.

The seminar seeks to provide a forward-looking vision of digital maintenance in a context where enabling technologies—including Artificial Intelligence, Predictive Analytics, Digital Twins, IoT, and Cloud/Edge/Fog Computing—have reached sufficient maturity. The objective is to identify the approaches needed to overcome the current barriers that still limit the full deployment of digital maintenance, particularly for assets with low levels of digitalization.

Participants will have the opportunity to:

• Share practical experiences in applying advanced technologies to maintenance.
• Review the current state of the art.
• Evaluate the real impact of these technologies on organizations.
• Discuss the evolution of maintenance models in the digital era.

We invite researchers and professionals to submit original, high-quality papers related to the following topics (including, but not limited to):

1. Current Barriers to the Implementation of Digital Maintenance

• Identification and analysis of the challenges hindering the adoption of digital maintenance technologies.
• Barriers to integrating advanced maintenance practices into traditional industrial environments.
• Industrial case studies presenting real-world experiences and lessons learned.
• Maintenance Modelling, including:
  • Predictive maintenance models.
  • Failure analysis.
  • Decision-support models.
  • Comprehensive asset maintenance modelling.

2. Advances in Digital Twins for Complex Systems

• Digital Twins and Asset Administration Shell (AAS) as key components of Industry 4.0.
• Challenges in modelling and managing assets within Digital Twin environments.
• Applications of Digital Twins for predictive and prescriptive maintenance.
• Integration of IoT, Artificial Intelligence, and data analytics.
• Digital Twins and Model-Based Systems Engineering (MBSE).
• Opportunities and current limitations of Digital Twin technologies.
• Standardization approaches and interoperability.

3. Human Resources in Digital Maintenance and Digital Twins

• Skills gaps and workforce training requirements.
• Human–computer interaction and collaboration in Digital Twin environments.
• Strategies for upskilling and reskilling maintenance professionals for the digital era.