Asset standard classification using artificial intelligence
By adopting a case study methodology, the research leverages real-world data sourced from CBRE, a global leader in Commercial Real Estate (CRE) services, to evaluate the performance and potential of AI for classification systems in streamlining and standardizing asset management processes through an...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/456931 |
| Acceso en línea: | https://hdl.handle.net/2117/456931 |
| Access Level: | acceso embargado |
| Palabra clave: | Artificial intelligence -- Industrial applications Case study Prompt engineering Asset and facility management Artificial intelligence Data management. Intel·ligència artificial -- Aplicacions industrials Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | By adopting a case study methodology, the research leverages real-world data sourced from CBRE, a global leader in Commercial Real Estate (CRE) services, to evaluate the performance and potential of AI for classification systems in streamlining and standardizing asset management processes through analysis of AI driven methodologies to optimize the efficiency and accuracy of asset standard classification by processing extensive datasets from CBRE clients worldwide, ultimately improving the effectiveness of Preventive Maintenance Plans (PPM). The focus is mainly on the usage of the given data to analyse the effectiveness of Ellis AI and other AI tools with corresponding performance indicators and self-designed process methodology. |
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