Workshop organized by

Moris Behnam, Mälardalen University, Sweden, Mats Jackson, Mälardalen University, Sweden.

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Workshop type

Half-day workshop and a poster session.

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Focus

The rapid digital transformation of industrial systems is reshaping how modern production environments are designed, monitored, and optimized. Emerging technologies such as artificial intelligence (AI), digital twins, industrial data analytics, and smart maintenance solutions are enabling more adaptive, intelligent, and resilient industrial operations. This workshop brings together researchers and industrial practitioners to present recent advances and real-world applications that support the digitalization of industrial systems and the transition toward data-driven decision-making in manufacturing and related sectors. The presentations highlight practical implementations and lessons learned from leading industrial organizations and research institutes, covering a wide range of domains including manufacturing, off-road equipment, and railway systems.
A central theme of the workshop is the integration of digital twins and data-driven technologies into industrial development and operational processes. Digital twins are increasingly used to support the design, validation, and optimization of production systems and complex industrial assets. Presentations from industrial partners explore how digital twin technologies can support production development and improve the performance of off-road machinery applications by enabling simulation-based analysis, predictive insights, and real-time system monitoring. These contributions demonstrate how digital representations of physical assets can enhance engineering decision-making and accelerate innovation in complex industrial environments.
Another important focus of the workshop is the role of artificial intelligence and advanced analytics in industrial operations. Several presentations discuss how AI-driven solutions can support optimization and automation in industrial contexts. Topics include the application of AI for improving operational processes, the use of intelligent algorithms to enhance requirements engineering in safety-critical railway systems, and data-driven approaches to improve troubleshooting in Industry 4.0 environments. These studies highlight how machine learning and AI-based methods can assist engineers and operators in identifying system inefficiencies, diagnosing faults, and improving decision-making processes in complex socio-technical systems.
The workshop also addresses the increasing importance of smart maintenance and reliability engineering in digitalized industries. Smart maintenance technologies leverage sensor data, predictive analytics, and intelligent diagnostics to improve asset availability and reduce unplanned downtime. Presentations in this area discuss approaches for enabling predictive and condition-based maintenance in manufacturing environments, as well as systematic investigations into failure mechanisms in critical railway subsystems such as electro-dynamic braking systems. These contributions emphasize the importance of reliability analysis, failure investigation, and data-driven maintenance strategies in ensuring the safe and efficient operation of industrial systems.
Another key topic explored in the workshop is the digitalization of industrial development and production processes. Presentations cover the implementation of digitalized tools and engineering methods in product development and industrialization projects, as well as broader perspectives on the digitalization of industrial production systems. These discussions highlight how organizations are adopting integrated digital tools to support collaboration across engineering disciplines, improve traceability, and enhance efficiency in complex development processes.
Finally, the workshop explores the transition toward data-driven production planning and decision support. As manufacturing systems generate increasing amounts of operational data, organizations are moving from traditional manual planning approaches toward intelligent, data-driven production management. One of the presentations discusses practical experiences and challenges in transitioning from manual planning processes to smart production planning supported by advanced analytics and digital technologies.
Overall, the workshop provides a platform for discussing practical experiences, emerging research, and industrial challenges related to the digital transformation of industrial systems. By bringing together contributions from academia, research institutes, and industry, the workshop aims to foster knowledge exchange and stimulate discussion on how digital technologies, AI, and data-driven methods can support the next generation of intelligent and resilient industrial systems.

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Contact for more details

If you would like to know more about the workshop, please contact Moris Behnam (moris.behnam@mdu.se).