A review and classification of technology readiness assessment techniques based on TRL scale
The Technology Readiness Levels (TRL) emerged in the late 1970s, proposed by the National Aeronautics and Space Administration (NASA). There are nine levels that seek to measure the maturity of a technology or product. The process that aims to assess the TRL level of a technology or product is calle...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | Brasil |
| Institución: | Exército Brasileiro (EB) |
| Repositorio: | Coleção Meira Mattos (Online) |
| Idioma: | portugués inglés español |
| OAI Identifier: | oai:meiramattos.eceme.ensino.eb.br:article/11602 |
| Acceso en línea: | https://ebrevistas.eb.mil.br/RMM/article/view/11602 |
| Access Level: | acceso abierto |
| Palabra clave: | TRL Technology Readiness Levels TRA Technology Readiness Assessment escala de madurez tecnológica evaluación de madurez tecnológica Nível de Prontidão tecnológica Avaliação de Prontidão Tecnológica |
| Sumario: | The Technology Readiness Levels (TRL) emerged in the late 1970s, proposed by the National Aeronautics and Space Administration (NASA). There are nine levels that seek to measure the maturity of a technology or product. The process that aims to assess the TRL level of a technology or product is called Technology Readiness Assessment (TRA). In the early 2000s, the TRL scale began to be used by industry and governments around the world, leading to an increase in the importance of TRA. Given this scenario, this work aimed to investigate the existing approaches in the literature for the execution of TRA based on the TRL scale. For that, a systematic review of the literature was conducted on scientific article databases and thesis and dissertation repositories. As a result of the review, three groups of approaches were identified: one based on human experts, another that uses a calculator to support the expert, and, finally, a third that uses semi-automatic or automatic tools, such as bibliometric indicators and text mining algorithms. The study identified the advantages and disadvantages of each of these approaches, as well as gaps still open in the literature. |
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