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The content of fats, oils, and greases (FOG) in wastewater, as a result of food preparation, both in homes and in different commercial and industrial activities, is a growing problem. In addition to the blockages generated in the sanitary networks, it also represents a difficulty for the performance of wastewater treatment plants (WWTP), increasing energy and maintenance costs and worsening the performance of downstream treatment processes. The pretreatment stage of these facilities is responsible for removing most of the FOG to avoid these problems. However, so far, optimization has been limited to the correct design and initial installation dimensioning. Proper management of this initial stage is left to the experience of the operators to adjust the process when changes occur in the characteristics of the wastewater inlet. The main difficulty is the large number of factors influencing these changes. In this work, a prediction model of the FOG content in the inlet water is presented. The model is capable of correctly predicting 98.45% of the cases in training and 72.73% in testing, with a relative error of 10%. It was developed using random forest (RF) and the good results obtained (R 2 = 0.9348 and RMSE = 0.089 in test) will make it possible to improve operations in this initial stage. The good features of this machine learning algorithm had not been used, so far, in the modeling of pretreatment parameters. This novel approach will result in a global improvement in the performance of this type of facility allowing early adoption of adjustments to the pretreatment process to remove the maximum amount of FOG.
Vanesa Mateo Pérez; José Mesa Fernández; Joaquín Villanueva Balsera; Cristina Alonso Álvarez. A Random Forest Model for the Prediction of FOG Content in Inlet Wastewater from Urban WWTPs. Water 2021, 13, 1237 .
AMA StyleVanesa Mateo Pérez, José Mesa Fernández, Joaquín Villanueva Balsera, Cristina Alonso Álvarez. A Random Forest Model for the Prediction of FOG Content in Inlet Wastewater from Urban WWTPs. Water. 2021; 13 (9):1237.
Chicago/Turabian StyleVanesa Mateo Pérez; José Mesa Fernández; Joaquín Villanueva Balsera; Cristina Alonso Álvarez. 2021. "A Random Forest Model for the Prediction of FOG Content in Inlet Wastewater from Urban WWTPs." Water 13, no. 9: 1237.
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 ( = 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.
Vanesa Mateo Pérez; José Mesa Fernández; Francisco Ortega Fernández; Joaquín Villanueva Balsera. Gross Solids Content Prediction in Urban WWTPs Using SVM. Water 2021, 13, 442 .
AMA StyleVanesa Mateo Pérez, José Mesa Fernández, Francisco Ortega Fernández, Joaquín Villanueva Balsera. Gross Solids Content Prediction in Urban WWTPs Using SVM. Water. 2021; 13 (4):442.
Chicago/Turabian StyleVanesa Mateo Pérez; José Mesa Fernández; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 2021. "Gross Solids Content Prediction in Urban WWTPs Using SVM." Water 13, no. 4: 442.
The energy production of concentrated solar power (CSP) plants not only depends on their design, but also of the weather conditions and the way they are operated. A performance model (PM) of a CSP plant is an essential tool to determine production costs, to optimize design and also to supervise the operation of the plant. The challenge is developing a PM that is both easy enough to be useful during the earlier stages of the project, and also useful for supervision of plant operation. This requires one to be able to describe the step between the different modes of operation and to fit the response to transient meteorological phenomena, not so relevant in terms of aggregate values, but crucial for the supervision. The quasi-dynamic performance model (QD-PM) can predict the net energy exported to the grid, as well as all the key operational variables. The QD-PM was implemented using Matlab-Simulink of Mathwoks (MA, USA) with a modular structure. Each module is developed using specific software and a state machine is used to simulate the sequence between the operation modes. The validation of the PM is made using one complete year of commercial operation of a 50 MWe CSP plant situated in Spain. The comparison between the actual data and the results of the model shows an excellent fit, being especially noteworthy as follows the transients between the different CSP operation modes. Then, QD-PM provides an accuracy better than the usual PM, and, almost, as good as that of a fully dynamic model but with a shorter simulation time. But, the main advantage of the QD-PM is that it can be use not only in the feasibility and design stages, but it can be used to supervise the operation of the plant.
Adrian Gonzalez Gonzalez; J. Valeriano Alvarez Cabal; Vicente Rodríguez Montequin; Joaquín Villanueva Balsera; Rogelio Peón Menéndez. CSP Quasi-Dynamic Performance Model Development for All Project Life Cycle Stages and Considering Operation Modes. Validation Using One Year Data. Energies 2020, 14, 14 .
AMA StyleAdrian Gonzalez Gonzalez, J. Valeriano Alvarez Cabal, Vicente Rodríguez Montequin, Joaquín Villanueva Balsera, Rogelio Peón Menéndez. CSP Quasi-Dynamic Performance Model Development for All Project Life Cycle Stages and Considering Operation Modes. Validation Using One Year Data. Energies. 2020; 14 (1):14.
Chicago/Turabian StyleAdrian Gonzalez Gonzalez; J. Valeriano Alvarez Cabal; Vicente Rodríguez Montequin; Joaquín Villanueva Balsera; Rogelio Peón Menéndez. 2020. "CSP Quasi-Dynamic Performance Model Development for All Project Life Cycle Stages and Considering Operation Modes. Validation Using One Year Data." Energies 14, no. 1: 14.
Corrosion is the main mechanism of the degradation of steel structures buried in the soil. Due to its aggressiveness, the material gradually loses thickness until the structure fails, which may cause serious environmental problems. The lack of a clearly established method in the design leads to the need for conservative excess thicknesses to ensure their useful life. This implies inefficient use of steel and an increase in the cost of the structure. In this paper, four quantitative and multivariate models were created to predict the loss of buried steel as a function of time. We developed a basic model, as well as a physical and an electrochemical one, based on multivariate adaptive regression spline (MARS), and a simpler model for comparative purposes based on clusters with Euclidean distance. The modeling was synthesized in a computer tool where the inputs were the characteristics of the soil and the time and the outputs were the loss of thickness of each predictive model and the description of the most similar real tests. The results showed that in all models, for relative errors of 10%, over 90% of predictions were correct. In addition, a real example of the operation of the tool was defined, where it was found that the estimates of the models allow the necessary optimization of steel to fulfill its useful life.
Lorena-De Arriba-Rodríguez; Vicente Rodríguez-Montequín; Joaquín Villanueva-Balsera; Francisco Ortega-Fernández. Design of Predictive Models to Estimate Corrosion in Buried Steel Structures. Sustainability 2020, 12, 9879 .
AMA StyleLorena-De Arriba-Rodríguez, Vicente Rodríguez-Montequín, Joaquín Villanueva-Balsera, Francisco Ortega-Fernández. Design of Predictive Models to Estimate Corrosion in Buried Steel Structures. Sustainability. 2020; 12 (23):9879.
Chicago/Turabian StyleLorena-De Arriba-Rodríguez; Vicente Rodríguez-Montequín; Joaquín Villanueva-Balsera; Francisco Ortega-Fernández. 2020. "Design of Predictive Models to Estimate Corrosion in Buried Steel Structures." Sustainability 12, no. 23: 9879.
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.
Manuel J. García Rodríguez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín M. Villanueva Balsera. Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain. Complexity 2020, 2020, 1 -20.
AMA StyleManuel J. García Rodríguez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín M. Villanueva Balsera. Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain. Complexity. 2020; 2020 ():1-20.
Chicago/Turabian StyleManuel J. García Rodríguez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín M. Villanueva Balsera. 2020. "Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain." Complexity 2020, no. : 1-20.
In the process of converting pig iron into steel, some co-products are generated—among which, basic oxygen furnace (BOF) slag is highlighted due to the great amount generated (about 126 kg of BOF slag per ton of steel grade). Great efforts have been made throughout the years toward finding an application to minimize the environmental impact and to increase sustainability while generating added value. Finding BOF slag valorization is difficult due to its heterogeneity, strength, and overall swallowing, which prevents its use in civil engineering projects. This work is focused on trying to resolve the heterogeneity issue. If many different types of steel are manufactured, then different types of slag could also be generated, and for each type of BOF slag, there is an adequate valorization option. Not all of the slag can be valorized, but it can be a tool for reducing the amount that must go to landfill and to minimize the environmental impact. An analysis by means of data mining techniques allows a classification of BOF slag to be obtained, and each one of these types has a better adjustment to certain valorization alternatives. In the plant used as an example of the application of these studies, eight different slag clusters were obtained, which were then linked to their different potential applications with the aim of increasing the amount valorized.
Sara M. Andrés-Vizán; Joaquín M. Villanueva-Balsera; J. Valeriano Álvarez-Cabal; Gema Martinez-Huerta. Classification of BOF Slag by Data Mining Techniques According to Chemical Composition. Sustainability 2020, 12, 3301 .
AMA StyleSara M. Andrés-Vizán, Joaquín M. Villanueva-Balsera, J. Valeriano Álvarez-Cabal, Gema Martinez-Huerta. Classification of BOF Slag by Data Mining Techniques According to Chemical Composition. Sustainability. 2020; 12 (8):3301.
Chicago/Turabian StyleSara M. Andrés-Vizán; Joaquín M. Villanueva-Balsera; J. Valeriano Álvarez-Cabal; Gema Martinez-Huerta. 2020. "Classification of BOF Slag by Data Mining Techniques According to Chemical Composition." Sustainability 12, no. 8: 3301.
The prioritization of factors has been widely studied applying different methods from the domain of the multiple-criteria decision-making, such as for example the Analytic Hierarchy Process method (AHP) based on decision-makers’ pairwise comparisons. Most of these methods are subjected to a complex analysis. The Bradley-Terry model is a probability model for paired evaluations. Although this model is usually known for its application to calculating probabilities, it can be also extended for ranking factors based on pairwise comparison. This application is much less used; however, this work shows that it can provide advantages, such as greater simplicity than traditional multiple-criteria decision methods in some contexts. This work presents a method for ranking the perspectives and indicators of a balance scorecard when the opinion of several decision-makers needs to be combined. The data come from an elicitation process, accounting for the number of times a factor is preferred to others by the decision-makers in a pairwise comparisons. No preference scale is used; the process just indicates the winner of the comparison. Then, the priority weights are derived from the Bradley-Terry model. The method is applied in a Financial Software Factory for demonstration and validation. The results are compared against the application of the AHP method for the same data, concluding that despite the simplifications made with the new approach, the results are very similar. The study contributes to the multiple-criteria decision-making domain by building an integrated framework, which can be used as a tool for scorecard prioritization.
Vicente Rodríguez Montequín; Joaquín Manuel Villanueva Villanueva Balsera; Marina Díaz Piloñeta; César Álvarez Pérez. A Bradley-Terry Model-Based Approach to Prioritize the Balance Scorecard Driving Factors: The Case Study of a Financial Software Factory. Mathematics 2020, 8, 276 .
AMA StyleVicente Rodríguez Montequín, Joaquín Manuel Villanueva Villanueva Balsera, Marina Díaz Piloñeta, César Álvarez Pérez. A Bradley-Terry Model-Based Approach to Prioritize the Balance Scorecard Driving Factors: The Case Study of a Financial Software Factory. Mathematics. 2020; 8 (2):276.
Chicago/Turabian StyleVicente Rodríguez Montequín; Joaquín Manuel Villanueva Villanueva Balsera; Marina Díaz Piloñeta; César Álvarez Pérez. 2020. "A Bradley-Terry Model-Based Approach to Prioritize the Balance Scorecard Driving Factors: The Case Study of a Financial Software Factory." Mathematics 8, no. 2: 276.
Crowdfunding is a response to the financing problem of innovative projects in an environment of severe economic crisis. Its competitive advantage lies in its independence from banking institutions and the distribution of risk among a certain number of funders. Since its inception, the number of successfully completed projects has grown to a point where it has started to suffer a downturn that puts its sustainability at risk. This study concerns this particular period of downturn, in order to identify attributes that characterize it, and to define behavioral stereotypes that may be associated with new projects. On a wide data set from sufficiently contrasted projects, and through the use data mining techniques, we extracted the most influential factors in determining the success or failure of the projects, that will subsequently be grouped together using clustering techniques. Six groups of projects have been identified, each with their own characteristics that define them, two of them clearly guide the projects to success and another one allows the modification its characteristics to move away from failure. This achieved strategy allows us to estimate which potential group would be the result of a new project.
Aladino Fernandez-Blanco; Joaquin Villanueva-Balsera; Vicente Rodriguez-Montequin; Henar Moran-Palacios. Key Factors for Project Crowdfunding Success: An Empirical Study. Sustainability 2020, 12, 599 .
AMA StyleAladino Fernandez-Blanco, Joaquin Villanueva-Balsera, Vicente Rodriguez-Montequin, Henar Moran-Palacios. Key Factors for Project Crowdfunding Success: An Empirical Study. Sustainability. 2020; 12 (2):599.
Chicago/Turabian StyleAladino Fernandez-Blanco; Joaquin Villanueva-Balsera; Vicente Rodriguez-Montequin; Henar Moran-Palacios. 2020. "Key Factors for Project Crowdfunding Success: An Empirical Study." Sustainability 12, no. 2: 599.
Smart Manufacturing is a goal to be achieved, and the most advanced manufacturing approaches are being used to pursue this objective. Within this context, industry development aims to attain an intelligent manufacturing using, for example, virtual models that simulate production lines. This paper presents the architecture of a Digital Twin for emulating the rolls replacement process within a wire rod rolling mill. The model is developed in Python, using a backtracking algorithm to select the suitable set of rolls as a first basic approach for the validation of the system. It may be used in the future to improve the production system automating the decision for the replacement of rolls as alternative to the current human-decision process.
Ana María Valdeón Junquera; Javier García González; Joaquín Manuel Villanueva Balsera; Vicente Rodríguez Montequín. A Wire Rod Rolling Mill Digital Twin for the Simulation of the Rolls Replacement Process. Proceedings 2020, 63, 13 .
AMA StyleAna María Valdeón Junquera, Javier García González, Joaquín Manuel Villanueva Balsera, Vicente Rodríguez Montequín. A Wire Rod Rolling Mill Digital Twin for the Simulation of the Rolls Replacement Process. Proceedings. 2020; 63 (1):13.
Chicago/Turabian StyleAna María Valdeón Junquera; Javier García González; Joaquín Manuel Villanueva Balsera; Vicente Rodríguez Montequín. 2020. "A Wire Rod Rolling Mill Digital Twin for the Simulation of the Rolls Replacement Process." Proceedings 63, no. 1: 13.
The largest project managers and adjudicators of a country, both by number of projects and by cost, are public procurement agencies. Therefore, knowing and characterising public procurement announcements (tenders) is fundamental for managing public resources well. This article presents the case of public procurement in Spain, analysing a dataset from 2012 to 2018: 58,337 tenders with a cost of 31,426 million euros. Many studies of public procurement have been conducted globally or theoretically, but there is a dearth of data analysis, especially regarding Spain. A quantitative, graphical, and statistical description of the dataset is presented. Mainly, the analysis is of the relation between the award price and the bidding price. An award price estimator is proposed that uses the random forest regression method. A good estimator would be very useful and valuable for companies and public procurement agencies. It would be a key tool in their project management decision making. Finally, a similar analysis, employing a dataset from European countries, is presented to compare and generalise the results and conclusions. Hence, this is a novel study which fills a gap in the literature.
Manuel J. García Rodríguez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín M. Villanueva Balsera. Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning. Complexity 2019, 2019, 1 -20.
AMA StyleManuel J. García Rodríguez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín M. Villanueva Balsera. Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning. Complexity. 2019; 2019 ():1-20.
Chicago/Turabian StyleManuel J. García Rodríguez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín M. Villanueva Balsera. 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning." Complexity 2019, no. : 1-20.
The following paper proposes a study about the existing solutions for dealing with uncertainty while solving the planning and scheduling problem at steel industry manufacturing processes. The different techniques designed to cope with uncertainty in manufacturing scheduling are discussed, along with the main uncertainty factors affecting the scheduling. The paper proposes a classification for the main uncertainties affecting the steelmaking process and analyzes the existing literature about solutions for the scheduling with uncertainty in the steel sector in terms of approaches followed and uncertainty types considered. Finally, the main remarks and future challenges within this field are presented.
Miguel Iglesias-Escudero; Joaquín Villanueva-Balsera; Francisco Ortega-Fernandez; Vicente Rodriguez-Montequín. Planning and Scheduling with Uncertainty in the Steel Sector: A Review. Applied Sciences 2019, 9, 2692 .
AMA StyleMiguel Iglesias-Escudero, Joaquín Villanueva-Balsera, Francisco Ortega-Fernandez, Vicente Rodriguez-Montequín. Planning and Scheduling with Uncertainty in the Steel Sector: A Review. Applied Sciences. 2019; 9 (13):2692.
Chicago/Turabian StyleMiguel Iglesias-Escudero; Joaquín Villanueva-Balsera; Francisco Ortega-Fernandez; Vicente Rodriguez-Montequín. 2019. "Planning and Scheduling with Uncertainty in the Steel Sector: A Review." Applied Sciences 9, no. 13: 2692.
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.
Vicente Rodríguez Montequín; Joaquín Villanueva Balsera; Sonia María Cousillas Fernández; Francisco Ortega Fernández. Exploring Project Complexity through Project Failure Factors: Analysis of Cluster Patterns Using Self-Organizing Maps. Complexity 2018, 2018, 1 -17.
AMA StyleVicente Rodríguez Montequín, Joaquín Villanueva Balsera, Sonia María Cousillas Fernández, Francisco Ortega Fernández. Exploring Project Complexity through Project Failure Factors: Analysis of Cluster Patterns Using Self-Organizing Maps. Complexity. 2018; 2018 ():1-17.
Chicago/Turabian StyleVicente Rodríguez Montequín; Joaquín Villanueva Balsera; Sonia María Cousillas Fernández; Francisco Ortega Fernández. 2018. "Exploring Project Complexity through Project Failure Factors: Analysis of Cluster Patterns Using Self-Organizing Maps." Complexity 2018, no. : 1-17.
Around the world, there are thousands of metal structures completely or partially buried in the soil. The main concern in their design is corrosion. Corrosion is a mechanism that degrades materials and causes structural failures in infrastructures, which can lead to severe effects on the environment and have direct impact on the population health. In addition, corrosion is extremely complex in the underground environment due to the variability of the local conditions. The problem is that there are many methods to its evaluation but none have been clearly established. In order to ensure the useful life of such structures, engineers usually consider an excess thickness that increases the economic cost of manufacturing and does not satisfy the principles of efficiency in the use of resources. In this paper, an extended revision of the existing methods to evaluate corrosion is carried out to optimize the design of buried steel structures according to their service life. Thus, they are classified into two categories depending on the information they provide: qualitative and quantitative methods. As a result, it is concluded that the most exhaustive methodologies for estimating soil corrosion are quantitative methods fed by non-electrochemical data based on experimental studies that measure the mass loss of structures.
Lorena-De Arriba-Rodriguez; Joaquin Villanueva-Balsera; Francisco Ortega-Fernandez; Fernando Rodriguez-Perez. Methods to Evaluate Corrosion in Buried Steel Structures: A Review. Metals 2018, 8, 334 .
AMA StyleLorena-De Arriba-Rodriguez, Joaquin Villanueva-Balsera, Francisco Ortega-Fernandez, Fernando Rodriguez-Perez. Methods to Evaluate Corrosion in Buried Steel Structures: A Review. Metals. 2018; 8 (5):334.
Chicago/Turabian StyleLorena-De Arriba-Rodriguez; Joaquin Villanueva-Balsera; Francisco Ortega-Fernandez; Fernando Rodriguez-Perez. 2018. "Methods to Evaluate Corrosion in Buried Steel Structures: A Review." Metals 8, no. 5: 334.
This paper presents a case study of how a Spanish financial software factory (FSF) has determined the weights of the indicators and objectives included in their strategy map with the aim of ensuring its business sustainability. A strategy map is a graphical representation of the cause-effect relationships between strategic objectives and indicators of a balanced scorecard (BSC). The detailed description of the strategy map development and deployment is not part of the aim of this work as it was described in a former paper. In this study, FAHP, a multicriteria decision-making (MCDM) method using the concepts of fuzzy set theory and hierarchical structure analysis, was used to calculate the weights. The analysis was carried out considering the points of view of different groups of stakeholders (shareholders, top management, middle managers, other employees, customers and some experts in the field of software factories) and the results are presented grouped by role to get a better understanding of the preferences of each kind of stakeholder. The conclusions of this study give a better insight of the corporative sustainability strategies of this kind of firms as well as the different vision of each stakeholder, what could be very valuable to the software factory managers for the decision-making and the strategic management of their organizations.
César Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 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. Sustainability 2017, 9, 527 .
AMA StyleCésar Álvarez Pérez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín Villanueva Balsera. 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. Sustainability. 2017; 9 (4):527.
Chicago/Turabian StyleCésar Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 2017. "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." Sustainability 9, no. 4: 527.
A balanced scorecard (BSC) framework for a factory that develops software for banking was proposed by us at the end of 2015 to ensure its sustainability, and was focused on improving its productivity and cost. Based on this framework, the aim of this study is to construct an approach using the analytic hierarchy process (AHP) and BSC for evaluating a factory’s performance in order for it to become a sustainable business. In this study, AHP is proposed to prioritise and determine weights for the perspectives and indicators included in the BSC for a financial software factory (FSF). The combination of these weights with different indicator measures produces a model that provides an effective assessment tool for FSF managers. The results of the study, which are shown both globally and disaggregated according to the different roles of FSF stakeholders, show that user satisfaction is the main pillar for making decisions. In addition, the result considering roles shows differences according to the relationship of each stakeholder with the software factory. The current study has been validated in a Spanish factory that develops software for several financial entities.
César Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. Integrating Analytic Hierarchy Process (AHP) and Balanced Scorecard (BSC) Framework for Sustainable Business in a Software Factory in the Financial Sector. Sustainability 2017, 9, 486 .
AMA StyleCésar Álvarez Pérez, Vicente Rodríguez Montequín, Francisco Ortega Fernández, Joaquín Villanueva Balsera. Integrating Analytic Hierarchy Process (AHP) and Balanced Scorecard (BSC) Framework for Sustainable Business in a Software Factory in the Financial Sector. Sustainability. 2017; 9 (4):486.
Chicago/Turabian StyleCésar Álvarez Pérez; Vicente Rodríguez Montequín; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 2017. "Integrating Analytic Hierarchy Process (AHP) and Balanced Scorecard (BSC) Framework for Sustainable Business in a Software Factory in the Financial Sector." Sustainability 9, no. 4: 486.
José Manuel Mesa Fernández; César Pacios González; Valeriano Álvarez Cabal; Joaquín Villanueva Balsera. Analysis of the quality control planning in residential construction projects in Spain. Revista de la construcción 2016, 15, 106 -114.
AMA StyleJosé Manuel Mesa Fernández, César Pacios González, Valeriano Álvarez Cabal, Joaquín Villanueva Balsera. Analysis of the quality control planning in residential construction projects in Spain. Revista de la construcción. 2016; 15 (2):106-114.
Chicago/Turabian StyleJosé Manuel Mesa Fernández; César Pacios González; Valeriano Álvarez Cabal; Joaquín Villanueva Balsera. 2016. "Analysis of the quality control planning in residential construction projects in Spain." Revista de la construcción 15, no. 2: 106-114.
Projects are complex works subjected to significant time, budget and quality constraints. One or the greatest challenges in project management still remaining unsolved is determining what is necessary to do in order to achieve success or failure. According to the specialized literature, both concepts of success factors and failure causes in projects are largely subjective and therefore difficult to quantify, depending on the point of view of the stakeholders involved. This paper compares which are the most frequent failure causes and the most important success factors among three different scenarios: for any type of project, for ICT projects and for ICT projects carried out in Spain only, by means of a worldwide empirical survey carried out among project managers intended to gather their personal perceptions on the matter. The survey is based on a questionnaire anonymously distributed through a professional internet network.
Vicente Rodríguez Montequín; Sonia Cousillas Fernández; Francisco Ortega Fernández; Joaquín Villanueva Balsera. Analysis of the Success Factors and Failure Causes in Projects. International Journal of Information Technology Project Management 2016, 7, 18 -31.
AMA StyleVicente Rodríguez Montequín, Sonia Cousillas Fernández, Francisco Ortega Fernández, Joaquín Villanueva Balsera. Analysis of the Success Factors and Failure Causes in Projects. International Journal of Information Technology Project Management. 2016; 7 (1):18-31.
Chicago/Turabian StyleVicente Rodríguez Montequín; Sonia Cousillas Fernández; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 2016. "Analysis of the Success Factors and Failure Causes in Projects." International Journal of Information Technology Project Management 7, no. 1: 18-31.
Projects are complex works subjected to significant time, budget and quality constraints. One or the greatest challenges in project management still remaining unsolved is determining what is necessary to do in order to achieve success or failure. According to the specialized literature, both concepts of success factors and failure causes in projects are largely subjective and therefore difficult to quantify, depending on the point of view of the stakeholders involved. This paper compares which are the most frequent failure causes and the most important success factors among three different scenarios: for any type of project, for ICT projects and for ICT projects carried out in Spain only, by means of a worldwide empirical survey carried out among project managers intended to gather their personal perceptions on the matter. The survey is based on a questionnaire anonymously distributed through a professional internet network.
Vicente Rodríguez Montequín; Sonia Cousillas Fernández; Francisco Ortega Fernández; Joaquín Villanueva Balsera. Analysis of the Success Factors and Failure Causes in Projects. Project Management 2016, 1365 -1379.
AMA StyleVicente Rodríguez Montequín, Sonia Cousillas Fernández, Francisco Ortega Fernández, Joaquín Villanueva Balsera. Analysis of the Success Factors and Failure Causes in Projects. Project Management. 2016; ():1365-1379.
Chicago/Turabian StyleVicente Rodríguez Montequín; Sonia Cousillas Fernández; Francisco Ortega Fernández; Joaquín Villanueva Balsera. 2016. "Analysis of the Success Factors and Failure Causes in Projects." Project Management , no. : 1365-1379.
Financial institutions and especially banks have always been at the forefront of innovation in management policies in order to improve their performance, and banking is probably one of the sectors that more effectively measures productivity and efficiency in virtually all aspects of its business. However, there is one area that still fails: the productivity of its software development projects. For years banking institutions have chosen to outsource their software projects using software firms created by them for this purpose, but up until a few years ago, the deadline for the delivery of the projects was more important than the efficiency with which they were developed. The last economic crisis has forced financial institutions to review and improve the software development efficiency related to their software factories to achieve a sustainable and feasible model. The sustainability of these software factories can be achieved by improving their strategic management, and the Balanced Scorecard (BSC) framework can be very useful in order to obtain this. Based on the concepts and practices of the BSC, this paper proposes a specific model to establish this kind of software factory as a way of improving their sustainability and applies it to a large Spanish firm specializing in financial sector software. We have included a preliminary validation plan as well as the first monitoring results. The adoption is still very recent and more data are needed to measure all the perspectives so no definitive conclusions can be drawn.
César Álvarez; Vicente Rodríguez; Francisco Ortega; Joaquín Villanueva. A Scorecard Framework Proposal for Improving Software Factories’ Sustainability: A Case Study of a Spanish Firm in the Financial Sector. Sustainability 2015, 7, 15999 -16021.
AMA StyleCésar Álvarez, Vicente Rodríguez, Francisco Ortega, Joaquín Villanueva. A Scorecard Framework Proposal for Improving Software Factories’ Sustainability: A Case Study of a Spanish Firm in the Financial Sector. Sustainability. 2015; 7 (12):15999-16021.
Chicago/Turabian StyleCésar Álvarez; Vicente Rodríguez; Francisco Ortega; Joaquín Villanueva. 2015. "A Scorecard Framework Proposal for Improving Software Factories’ Sustainability: A Case Study of a Spanish Firm in the Financial Sector." Sustainability 7, no. 12: 15999-16021.
During the last years, data mining models have proven to be a promising approach to improve hot rolling processes. In the present research we propose a model for prediction of lateral flow. In hot rolling mills this will lead to exact predictions of the strip width after rolling, which reduces cut-offs and scrapped material. Any reduction of the cut-offs implies important economical and environmental benefits. Physically based models were developed some years ago, but they require simplifications, need data that is difficult to achieve online or include experimental parameters that have to be optimized. Adaptive techniques can contribute widely to the improvement of the diagnostics. For this work, production data was gathered from a Hot Strip Mill (HSM) and a nonlinear model was built using a data-mining methodology based on multivariate adaptive regression splines (MARS). The agreement of the MARS model with observed data confirmed its good performance.
Vicente Rodriguez-Montequin; Fernando Rodriguez Perez; Francisco Ortega Fernandez; Joaquin Villanueva-Balsera. Modelling of lateral flow in a Hot Strip Mill (HSM) using adaptive techniques. 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) 2015, 44 -49.
AMA StyleVicente Rodriguez-Montequin, Fernando Rodriguez Perez, Francisco Ortega Fernandez, Joaquin Villanueva-Balsera. Modelling of lateral flow in a Hot Strip Mill (HSM) using adaptive techniques. 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS). 2015; ():44-49.
Chicago/Turabian StyleVicente Rodriguez-Montequin; Fernando Rodriguez Perez; Francisco Ortega Fernandez; Joaquin Villanueva-Balsera. 2015. "Modelling of lateral flow in a Hot Strip Mill (HSM) using adaptive techniques." 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) , no. : 44-49.