Integration of Balanced Scorecard (BSC), strategy map, and Fuzzy Analytic Hierarchy Process (FAHP) for a sustainability business framework: A case study of a Spanish software factory in the financial sector

This work has been subsidized through the Plan of Science, Technology and Innovation of the Principality of Asturias (Ref: FC-15-GRUPIN14-132). The authors also thank anonymous referees who reviewed and gave important comments to this pape

Integrating analytic hierarchy process (AHP) and balanced scorecard (BSC) framework for sustainable business in a software factory in the financial sector

This work has been subsidized through the Plan of Science, Technology and Innovation of the Principality of Asturias (Ref: FC-15-GRUPIN14-132). The authors also thank anonymous referees who reviewed and gave important comments to this paper

Exploring project complexity through project failure factors: analysis of cluster patterns using self-organizing maps

In the field of project management, complexity is closely related to project outcomes and hence project success and failure factors. Subjectivity is inherent to these concepts, which are also influenced by sectorial, cultural, and geographical differences. While theoretical frameworks to identify organizational complexity factors do exist, a thorough and multidimensional account of organizational complexity must take into account the behavior and interrelatedness of these factors. Our study is focused on analyzing the combinations of failure factors by means of self-organizing maps (SOM) and clustering techniques, thus getting different patterns about the project managers perception on influencing project failure causes and hence project complexity. The analysis is based on a survey conducted among project manager practitioners from all over the world to gather information on the degree of influence of different factors on the projects failure causes. The study is cross-sectorial. Behavioral patterns were found, concluding that in the sampled population there are five clearly differentiated groups (clusters) and at least three clear patterns of answers. The prevalent order of influence is project factors, organization related factors, project manager and team members factors, and external factors

Diseño, Implantación y Desarrollo de un Máster en Ingeniería Informática

Este artículo describe el Máster en Ingeniería Informática de la Universidad de Oviedo, impartido en la Escuela Politécnica Superior de Ingeniería de Gijón (EPI). Es uno de los primeros estudios de máster en Ingeniería Informática implantados en España conforme a las Directrices del Consejo de Universidades para estudios conducentes a la profesión de Ingeniero en Informática (Resolución de 8 de junio de 2009, BOE 187, martes 4 de agosto de 2009).This paper provides an overview of the Informatics and Computing Engineering Master Degree at the Gijon Polytechnic School of Engineering (University of Oviedo). This is one of the earliest Informatics and Computing Engineering master’s in Spain designed according the National recommendations for the Informatics Engineering professio

Gender influence in project management: Analysis of a case study based on master students

International Conference on ENTERprise Information Systems (CENTERIS) / International Conference on Project MANagement (ProjMAN) / International Conference on Health and Social Care Information Systems and Technologies (HCist) (2017. Barcelona

Competencia transversal de trabajo en equipo: Evaluación en las enseñanzas técnicas

Congreso Universitario de Innovación Educativa En las Enseñanzas Técnicas, CUIEET (26º. 2018. Gijón

Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain

Recommending the identity of bidders in public procurement auctions (tenders) has a significant impact in many areas of public procurement, but it has not yet been studied in depth. A bidders recommender would be a very beneficial tool because a supplier (company) can search appropriate tenders and, vice versa, a public procurement agency can discover automatically unknown companies which are suitable for its tender. This paper develops a pioneering algorithm to recommend potential bidders using a machine learning method, particularly a random forest classifier. The bidders recommender is described theoretically, so it can be implemented or adapted to any particular situation. It has been successfully validated with a case study: an actual Spanish tender dataset (free public information) which has 102,087 tenders from 2014 to 2020 and a company dataset (nonfree public information) which has 1,353,213 Spanish companies. Quantitative, graphical, and statistical descriptions of both datasets are presented. The results of the case study were satisfactory: the winning bidding company is within the recommended companies group, from 24% to 38% of the tenders, according to different test conditions and scenarios