Competitive Intelligence Text Mining: Words Speak

Competitive intelligence (CI) has become one of the major subjects for researchers in recent years. The present research is aimed to achieve a part of the CI by investigating the scientific articles on this field through text mining in three interrelated steps. In the first step, a total of 1143 articles released between 1987 and 2016 were selected by searching the phrase "competitive intelligence" in the valid databases and search engines; then, through reviewing the topic, abstract, and main text of the articles as well as screening the articles in several steps, the authors eventually selected 135 relevant articles in order to perform the text mining process. In the second step, pre-processing of the data was carried out. In the third step, using non-hierarchical cluster analysis (k-means), 5 optimum clusters were obtained based on the Davies–Bouldin index, for each of which a word cloud was drawn; then, the association rules of each cluster was extracted and analyzed using the indices of support, confidence, and lift. The results indicated the increased interest in researches on CI in recent years and tangibility of the strong and weak presence of the developed and developing countries in formation of the scientific products; further, the results showed that information, marketing, and strategy are the main elements of the CI that, along with other prerequisites, can lead to the CI and, consequently, the economic development, competitive advantage, and sustainability in market

Hybrid rough set and data envelopment analysis approach to technology prioritisation

The complexity and speed of change in technological systems pose new challenges to technology management. Particular attention should be given to the issue of modelling the uncertainty of assessments and creating rules for determining the weights of the technology assessment criteria. The article aims to present a comprehensive hybrid technology prioritisation model based on the Data Envelopment Analysis and the concept of Rough Sets. The technology prioritisation process that uses the proposed model includes three consecutive stages: (i) the formulation of technology assessment matrix, (ii) the removal of the criteria redundancy based on indiscernibility relation defined in the Rough Set Theory, (iii) the development of rough variables and prioritisation using the DEA super-efficiency model. The combination of DEA and RS is a unique proposal to classify and rank objects based on the tabular representation of their conditional attributes under circumstances of uncertainty. Application of the developed hybrid model to the real data of the technology foresight project “NT FOR Podlaskie 2020” positively verified the assumed effects of its use. The obtained results allow a more objective and rational justification of the chosen technology, simplification of interpretation and better authentication of results from the perspective of decision-makers. First published online 8 May 202

Gestión basada en procesos: diseño e implantación en la PYME Seguridad Avanzada Proyectos, S.L.U.

El presente trabajo tiene como objetivo principal aplicar de manera práctica la gestión por procesos en una PYME andaluza. Para cumplir con el objetivo, en primer lugar, se ha analizado la organización vertical (estructura), para posteriormente aplicar las cuatro fases de la gestión por procesos: (1) identificación de los procesos para la elaboración del mapa de procesos; (2) priorización y formalización de los procesos (en este caso se han procedimentado solo los clave); (3) diseño de un sistema de seguimiento mediante la definición de indicadores; y (4) el diseño de una sistemática para la mejora continua de los procesos. Las principales ventajas de la implantación de este sistema se resumen en: aumento de la competitividad, optimización de costes a través de la estandarización de los procesos, incremento de la satisfacción de los grupos de interés y eficiencia en los servicios prestados.Universidad de Sevilla. Grado en Finanzas y Contabilida

Enhancing product quality of a process

Purpose - Data mining (DM) is used to improve the performance of manufacturing quality control activity, and reduces productivity loss. The purpose of this paper is to discover useful hidden patterns from fabric data to reduce the amount of defective goods and increase overall quality