Virtual prototyping: Bringing out tacit knowledge in product development

Simo-Pekka Leino

    Research output: Contribution to conferenceConference AbstractScientificpeer-review


    Design is about understanding the demands of people and society, and the process of transforming them into artefacts i.e. product specifications that enable manufacture, distribution, operation, and maintenance of a product. Human factors are typical abstract characters that are difficult to be transformed as concrete specifications. Product development is a practical iterative process consisting of cycles that include the steps of synthesis (design), analysis, determining deviations and overall evaluation taking into account market opportunities. Prototyping is a phase of product development, where product characteristics and functions are evaluated using physical or virtual models. A scientific and practical problem is: How to include tacit and abstract knowledge into the design and development process?
    3D computer visualizations, virtual prototyping and virtual environments are proposed to enable improved communication and understanding between stakeholders of product development. A virtual prototype is a computer simulation model of the product prototype that is used in the virtual prototyping activity. Intermediary object is a concept that covers physical or digital artefacts produced by the participants during their work covering all kinds of externalization. Intermediary virtual prototyping (IVP) is a concept, which underscores the many layers and dimensions, from the technical advantages of virtual environments to the mediating object of product development that is easy to understand. IVP improves the transformation between tacit and explicit knowledge, thus capturing tacit knowledge that, for instance, factory and maintenance workers, as well as designers, hold. IVP can be seen as a means for communicating and discussing human factor-related demands and requirements for designers, and for transforming them to formal requirement specifications.
    The goal of engineering design science is developing abstract concepts that are valid for all kind of socio-technical systems and their development. Future research should be done in order to develop improved information models and architectures that support knowledge management and integration with Artificial Intelligence (AI) and IT systems. AI could for instance be harnessed to automate non-value adding routine design tasks. However, there is a fundamental research problem: How to convert tacit knowledge into concepts, symbols and words that can be treated with AI and learned by machine learning algorithms?
    Original languageEnglish
    Publication statusPublished - 2018
    MoE publication typeNot Eligible
    EventFiCLA Symposium on Laguage and Thought - Helsinki, Finland
    Duration: 30 Aug 2018 → …


    ConferenceFiCLA Symposium on Laguage and Thought
    Period30/08/18 → …


    • design
    • Artificial intelligence (AI)


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