Alessandro  Bertoni

Alessandro Bertoni

Professor (Biträdande) in Mechanical Engineering

Alessandro's research focuses on the development of methods and tools to support the early design stages of product-service systems and systems engineering projects. His major research contributions are in the field of Value-Driven Design, Data-Driven Design, and Value Visualization.

Alessandro Bertoni holds an M.Sc. degree in Management Engineering from the University of Bergamo and a Ph.D. in Product Innovation from the Luleå University of Technology. From early 2014 he works s lecturer and researcher at the Department of Mechanical Engineering of Blekinge Institute of Technology


During my Ph.D. studies, my research focused on the area Value Driven Design and Product Service Systems development. I researched how to improve the decision-making activities in the preliminary design of complex systems by developing models considering the potential value of new concepts from a supply chain and lifecycle perspective. Concurrently, I focused on the design practices in the conceptual design stage of a new product, where available information is limited. In such a context, I paid major attention to the System Engineering processes and tools and I did research within the Value-Driven Design area, focusing on the integration of a wider lifecycle perspective into current decision-making practices. My most relevant contributions in the engineering design field were the development of a Value-Driven Design Methodology and a visualization prototype to visualize the value of product-service systems in preliminary design using color-coded CAD models.

After my Ph.D. studies, my research interest has developed into generalizing the Value-Driven Design findings in industrial contexts other than aerospace (article 5 in the attached publications list). I was initially employed as a post-doctoral research fellow, and later as a senior lecturer, at Blekinge Institute of Technology doing research in the frame of the Model-Driven Development and Decision Support (MD3S) research profile funded by the Swedish KK Foundation. Here a dedicated research track has allowed me to get in contact with a larger set of companies from different sectors. My research interest has evolved into the monetary quantification of value and its integration in the product simulation platforms, and in the development of Value Driven Design methods for lean product and service development (article 3, 7 and 9 in the attached publications list). This research effort is framed into the overall research group objective to develop of a Decision-Making Arena in which interactive visualization, and management of different data through different media technologies, allow decision-makers to “play” with multidisciplinary information eventually leading to more value-aware decisions.

In the last four years, I have also been involved in a research project targeting the extension of Value-Driven Design into Set-Based Concurrent Engineering for platform-based design. This project, named VITUM (Virtual Turbine Demonstrator) was funded by the Swedish agency VINNOVA, and involved 2 academic institutions and 2 industrial partners and it was led by an aerospace sub-system manufacturer. The project concerned the development of a demonstrator for the virtual modeling of an aerospace modular component in early design and was concluded on February 2018.

In the last three years, I have increasingly expanded my research into the use of data-intense technologies (e.g. data mining and machine learning), to generate predictive models to be used in early engineering design decision making. I am currently focusing on integrating the use of data mining on mechanical performance and environmental data prediction, to assess the value of alternative machine design configurations, in respect to the system-level impact of a design change (article 4, 9, and 10 in the attached publications list).

In synthesis, my research interest in Value-Driven Design and Product Service Systems Design has expanded toward researching the integration of data science as an enabler of better design decisions. Due to its novelty, the research field still lacks consistent methodologies and approaches generalizable in different industrial areas and implemented into the established design processes (article 4 in the attached publications list). While a number of cases studies and conceptual frameworks are available dealing with the very early planning of new products, very limited contributions are available integrating data-driven models in “traditional” engineering-oriented product or service development projects, encompassing idea generation and sub-system embodiment.


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