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Projeto de investigação
Industrial Data Services for Quality Control in Smart Manufacturing
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Relational network of innovation ecosystems generated by digital innovation hubs
Publication . Serrano-Ruiz, Julio C.; Ferreira, José; Jardim-Goncalves, Ricardo; Ortiz, Ángel; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; Springer Netherlands
Collaboration plays a key role in the success attained to date by networks of innovation ecosystems generated around entities known as Digital Innovation Hubs (DIHs), recently created following European Commission initiatives to boost the digitisation of the European economic fabric. This article proposes a conceptual framework that brings together, defines, structures and relates the concepts involved in the collaborative interaction processes within and between these innovation ecosystems to allow comprehensive conceptualisation. The developed framework also provides an approach that helps to tangibilise collaboration as a management process. Here the goal is to ultimately move towards not only qualitative, but also quantitative modelling to bridge the research gap in the state of the art in this respect. The data-driven business-ecosystem-skills-technology (D-BEST) model, devised to configure DIHs service portfolios in a collaborative context, provides the reference basis for the interorganisational asset transfer methodology (IOATM). This is the keystone that structures the framework and constitutes its main contribution. Through the IOATM, this conceptual framework points out collaboration quantification, and serves as a lever for its modelling to deal with collaboration accounting by: turning it into a more controllable management element; guiding practitioners' efforts to improve collaborative processes efficiency with an approach that pursues objectivity and maximises synergies.
An industry maturity model for implementing Machine Learning operations in manufacturing
Publication . Mateo-Casalí, Miguel Ángel; Gil, Francisco Fraile; Boza, Andrés; Nazarenko, Artem; DEE - Departamento de Engenharia Electrotécnica e de Computadores; Universitat Politecnica de Valencia
The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through the extensive introduction of automatons, computers and Internet of Things (IoT) components. All this seeks to streamline, improve, and increase production at the lowest possible cost and avoid any failure in the creation of the product, following a strategy called "Zero Defect Manufacturing". Machine Learning Operations (MLOps) provide a ML-based solution to this challenge, promoting the automation of all product-relevant steps, from development to deployment. When integrating different machine learning models within manufacturing operations, it is necessary to understand what functionality is needed and what is expected. This article presents a maturity model that can help companies identify and map their current level of implementation of machine learning models.
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Entidade financiadora
European Commission
Programa de financiamento
H2020
Número da atribuição
958205