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<dc:title>Aplicación de técnicas inteligentes a la gestión de la empresa industrial</dc:title>
<dc:creator>Manzanedo Saiz, Manuel</dc:creator>
<dc:contributor>Basurto Hornillos, Nuño</dc:contributor>
<dc:contributor>Alcalde Delgado, Roberto</dc:contributor>
<dc:contributor>Universidad de Burgos. Departamento de Digitalización</dc:contributor>
<dc:subject>Economía</dc:subject>
<dc:subject>Energía</dc:subject>
<dc:subject>Precio acero</dc:subject>
<dc:subject>Análisis de series temporales</dc:subject>
<dc:subject>Economy</dc:subject>
<dc:subject>Energy</dc:subject>
<dc:subject>Steel price</dc:subject>
<dc:subject>Analyzing time series</dc:subject>
<dcterms:abstract>La integración de “Machine Learning” en la gestión empresarial, permite analizar importantes series de datos para anticiparse o desarrollar mejoras para disminuir las ineficiencias. En este trabajo, se aplican técnicas de “Machine Learning” en la gestión de las empresas industriales, cuyos resultados están publicados en revistas cientificas. Así, se aplican por primera vez modelos neuronales no lineales a diferentes conjuntos de datos con el fin de validar su idoneidad para predecir el precio del acero laminado en España, utilizando distintos modelos y analizando diferentes conjuntos de datos, los resultados han permitido encontrar el mejor modelo de previsión de precios. También, se ha analizado el consumo energético en el sector del transporte de mercancías por carretera, observándose su desacoplamiento en la Unión Europea y utilizando el modelo SARIMA, se realiza una predicción sobre la evolución de indicadores relevantes del transporte por carretera en diferentes países</dcterms:abstract>
<dcterms:abstract>The development of new technologies such as Artificial Intelligence allows companies to make better decisions based on data that, once analyzed with these new tools, can provide information on different situatuions, future forecasts, and new scenarios, allowing them to achieve greater efficiency in resource management, optimize planning, and improve operations. Machine Learning techniques are important resources that companies can use to innovate their management. For the research developed for this thesis, a state-of-the-art analysis of the results of applying Artificial Intelligence techniques to decision-making in business management was previously conducted. No publications were found that have applied these techniques to the problem at hand. Furthermore, the techniques that best fit the initial research data were previously analyzed. As a result of this analysis, considered the basis of this research, these results have been published in high-impact scientific journals</dcterms:abstract>
<dcterms:dateAccepted>2026-05-20T12:21:56Z</dcterms:dateAccepted>
<dcterms:available>2026-05-20T12:21:56Z</dcterms:available>
<dcterms:created>2026-05-20T12:21:56Z</dcterms:created>
<dcterms:issued>2025</dcterms:issued>
<dc:type>info:eu-repo/semantics/doctoralThesis</dc:type>
<dc:identifier>https://hdl.handle.net/10259/11673</dc:identifier>
<dc:language>spa</dc:language>
<dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 Internacional</dc:rights>
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