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Assessing the Data Management innovations within the manufacturing environment in the Industry 4.0

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Detalhes bibliográficos
Resumo:An economic growth alongside with the development of technologies are the two decisive factors for the fourth industrial revolution. This study proposes covering the gap in the literature for assessment methodologies, evaluating how the data management technological innovations contribute to value creation for manufacturing organizations. For this, a systematic critical literature review was conducted to understand the main variables that influence the adoption of technologies. Later, it was used a natural science research methodology by applying a qualitative method through interviews, with participants having relevant roles in data management departments, to then extract their perspective on data management operability, data management technologies innovations, data interoperability, and manufacturing operational performance. A further content analysis gives an overview of the main technological innovations adopted, their context, decisive factors, adoption pacing differences, and what value they are attributing to the business operations.
Autores principais:Pino, Ricardo de Sousa
Assunto:Data Management Industry 4.0 Innovation Manufacturing Industry Operational Performance SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure
Ano:2025
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso embargado
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL
Descrição
Resumo:An economic growth alongside with the development of technologies are the two decisive factors for the fourth industrial revolution. This study proposes covering the gap in the literature for assessment methodologies, evaluating how the data management technological innovations contribute to value creation for manufacturing organizations. For this, a systematic critical literature review was conducted to understand the main variables that influence the adoption of technologies. Later, it was used a natural science research methodology by applying a qualitative method through interviews, with participants having relevant roles in data management departments, to then extract their perspective on data management operability, data management technologies innovations, data interoperability, and manufacturing operational performance. A further content analysis gives an overview of the main technological innovations adopted, their context, decisive factors, adoption pacing differences, and what value they are attributing to the business operations.