This paper fills the gap in the financial perspective of supply chain performance measurement, related to the lack of a bankruptcy probability indicator, and proposes a predictor which is the eighth-model of the Altman Z-Score Logistic Regression. Furthermore, a bankruptcy probability ranking is established for the companies’ supply chains, according to the industry to which they belong. Moreover, the values are set to establish three categories of companies according to predictor. The probability of bankruptcy is analysed and studied for the supply chain of different industries. The building industry is revealed to have the highest probability of bankruptcy.

Firms face an increasingly complex economic and financial environment in which the access to international networks and markets is crucial. To be successful, companies need to understand the role of internationalization determinants such as bilateral psychic distance, experience, etc. Cutting-edge feature selection methods are applied in the present paper and compared to previous results to gain deep knowledge about strategies for Foreign Direct Investment. More precisely, evolutionary feature selection, addressed from the wrapper approach, is applied with two different classifiers as the fitness function: Bagged Trees and Extreme Learning Machines. The proposed intelligent system is validated when applied to real-life data from Spanish Multinational Enterprises (MNEs). These data were extracted from databases belonging to the Spanish Ministry of Industry, Tourism, and Trade. As a result, interesting conclusions are derived about the key features driving to the internationalization of the companies under study. This is the first time that such outcomes are obtained by an intelligent system on internationalization data.

Road transport is an integral part of economic activity and is therefore essential for its development. On the downside, it accounts for 30% of the world’s GHG emissions, almost a third of which correspond to the transport of freight in heavy goods vehicles by road. Additionally, means of transport are still evolving technically and are subject to ever more demanding regulations, which aim to reduce their emissions. In order to analyse the sustainability of this activity, this study proposes the application of novel Artificial Intelligence techniques (more specifically, Machine Learning). In this research, the use of Hybrid Unsupervised Exploratory Plots is broadened with new Exploratory Projection Pursuit techniques. These, together with clustering techniques, form an intelligent visualisation tool that allows knowledge to be obtained from a previously unknown dataset. The proposal is tested with a large dataset from the official survey for road transport in Spain, which was conducted over a period of 7 years. The results obtained are interesting and provide encouraging evidence for the use of this tool as a means of intelligent analysis on the subject of developments in the sustainability of road transportation.

El objetivo de este trabajo es diseñar y aplicar un modelo robusto de Gestión del Conocimiento para las empresas energéticas que permita identificar el conjunto de conocimientos y capacidades clave del sector, comprender y aplicar estos conocimientos, realizar el inventario de expertos, retener y capitalizar la experiencia y el saber hacer, compartir y transmitir el conocimiento entre el personal y detectar las necesidades de formación y actualización derivadas de las exigencias del trabajo. El «Modelo PAR de Gestión del Conocimiento» que resulta se basa en tres estrategias de conocimiento, con sus respectivos objetivos, acciones, recursos e indicadores.

<p class="TtuloAbstract">Four consecutive years of more than a thousand Spanish companies from different economic sectors are analyzed to determine the influence of intellectual capital on the business growth strategy. One of the purposes of this work is to establish a classification criterion of the strategic behaviour of a company linked to the growth of three factors: the demand of the sector, the sales of the company and the financial sustainability of the company. Another purpose is to develop and validate an appropriate classification of where the value added by human intellectual capital is structurally concentrated and used according to the strategic behaviour, growth and sector of the company. Interesting conclusions are drawn about the strategic behaviour of the company and its intangible capital, as well as a different method for classifying companies according to their growth, which helps predict business profitability.</p>