Alessandro Bertoni

Alessandro Bertoni serves as Professor of Mechanical Engineering at Blekinge Institute of Technology (BTH) since April 2025, affiliated with the Department of Mechanical Engineering at the Karlskrona campus. He leads research in value-driven design methodologies and product-service systems development, collaborating extensively with aerospace and civil engineering industry partners.

His research interests span value-driven design, product-service systems (PSS), and data-intensive technologies for engineering decision support. Bertoni's work focuses on developing methods and tools to support early phases of product and system development, particularly through value visualization techniques and changeability assessment frameworks. He actively investigates how data mining and machine learning can create predictive models that address reliability issues in deterministic engineering models, with applications in aerospace, civil engineering, and construction machinery sectors.

His publication record demonstrates consistent growth in impact, with recent work emphasizing digital twin applications, changeability frameworks for circular systems, and data-driven approaches to value assessment. The research trajectory shows increasing integration of artificial intelligence techniques with traditional systems engineering approaches, particularly in the context of sustainable product development and resilient system design.

Bertoni teaches across all academic levels, including systems engineering, research methodology in engineering design, and innovative sustainable product development courses. His educational approach incorporates value-driven design principles into engineering education, preparing students to address complex design challenges through quantitative and qualitative assessment methods.

As part of the Product Development Research Lab at BTH, he collaborates with researchers like Marco Bertoni, Tobias Larsson, and other international partners on projects addressing challenges in high-variety, low-volume manufacturing contexts, sustainable design, and digital transformation of engineering processes.