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

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

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

Estimación de costes y plazos en proyectos de sistema de información

La estimación de plazos en proyectos es una tarea crítica, que puede conducir al fracaso del proyecto, debido al alargamiento de la duración o a una mala estimación del esfuerzo necesario para ejecutarlo. Es necesario disponer de una herramienta, que nos ayude a tener más conocimiento sobre el proyecto para seleccionar las variables influyentes sobre las desviaciones del proyecto y que proporcione unas estimaciones más ajustadas. En este trabajo se analizan la viabilidad y las ventajas del desarrollo de un sistema basado en técnicas de inteligencia artificial, capaz de seleccionar las variables que afectan a la duración del proyecto y al esfuerzo necesario para realizarlo a partir de un conjunto de datos históricos, frente a las técnicas actuales. Se toma como conjunto de datos el procedente de cierres de proyectos recopilado por el ISBSG, sobre el cual se aplican los pasos de la metodología de análisis de datos para examinar la calidad de la información encontrando conjuntos de proyectos anómalos respecto a los demás, así como presencia de valores perdidos y gran cantidad de variables categóricas. Para ello, se propone un método para el análisis de los datos existentes y su preprocesamiento para conseguir un modelo que se ajuste a las necesidades del gestor de proyectos. Se aplican técnicas de minería de datos para solucionar estos problemas preparando los datos para el modelado utilizando mars método multivariante adaptativo de regresión por splines. Con el cual se han superado los objetivos fijados al principio del trabajo

Gross Solids Content Prediction in Urban WWTPs Using SVM

The preliminary treatment of wastewater at wastewater treatment plants (WWTPs) is of great importance for the performance and durability of these plants. One fraction that is removed at this initial stage is commonly called gross solids and can cause various operational, downstream performance, or maintenance problems. To avoid this, data from more than two operation years of the Villapérez Wastewater Treatment Plant, located in the northeast of the city of Oviedo (Asturias, Spain), were collected and used to develop a model that predicts the gross solids content that reaches the plant. The support vector machine (SVM) method was used for modelling. The achieved model precision (Radj2 = 0.7 and MSE = 0.43) allows early detection of trend changes in the arrival of gross solids and will improve plant operations by avoiding blockages and overflows. The results obtained indicate that it is possible to predict trend changes in gross solids content as a function of the selected input variables. This will prevent the plant from suffering possible operational problems or discharges of untreated wastewater as actions could be taken, such as starting up more pretreatment lines or emptying the containers

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