Predicción De Fuga De Clientes en Una Empresa De Distribución De Gas Natural Mediante El Uso De Minería De Datos
Abstract: La fuga de clientes es un problema relevante al que enfrentan las empresas de servicios y que les puede generar pérdidas económicas significativas. Identificar los elementos que llevan a un cliente a dejar de consumir un servicio es una tarea compleja, sin embargo, mediante su comportamiento es posible estimar una probabilidad de fuga asociada a cada uno de ellos. Esta investigación aplica minería de datos para la predicción de la fuga de clientes en una empresa de distribución de gas natural, mediante dos téc… Show more
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Cellulose is the main raw material for the production of paper. Companies that produce it present in their production line the cutting of the cellulose sheet. This failure is sporadic and has a high economic impact since it paralyzes the production line for several hours, incurring unproductive hours and a large deployment of human and financial resources. In this research, the use of Data Mining is proposed to define a machine learning algorithm that allows predicting the cutting of the cellulose sheet in a production line of a cellulose plant in Chile. The results show that by applying this technique it is possible to predict the cutting of the cellulose sheet well in advance to take corrective actions to avoid cutting and thus minimize the economic impact associated with the failure. Keywords: Data Mining, machine learning, cellulose, productivity. References [1]B. Ranaganth y G. Viswanath, «Application of artificial neural network for optimizing cutting variables in laser cutting of 304 grade stainless steel,» International Journal of Applied Engineering and Technology, vol. 1, nº 1, pp. 106-112, 2011. [2]M. Durica, J. Frnda y L. Svabova, «Decision tree based model of business failure prediction for Polish companies,» Oeconomia Copernicana, vol. 10, nº 3, pp. 453-469, 2019. [3]G. Köksal, İ. Batmaz y M. C. Testik, «A review of data mining applications for quality improvement in manufacturing industry,» Expert systems with Applications, vol. 38, nº 10, pp. 13448-13467, 2011. [4]H. Poblete y R. Vargas, «Relacion entre densidad y propiedades de tableros HDF producidos por un proceso seco,» Maderas. Ciencia y tecnología, vol. 8, nº 3, pp. 169-182, 2006. [5]B. Kovalerchuk y E. Vityaev, «Data mining for financial applications,» Data Mining and Knowledge Discovery Handbook, pp. 1203-1224, 2005. [6]U. Fayyad, G. Piatetsky-Shapiro, P. Smyth y R. Uthurusamy, «Advances in knowledge discovery and data mining,» American Association for Artificial Intelligence, 1996. [7]A. K. Pandey y A. K. Dubey, «Neuro fuzzy modeling of laser beam cutting process,» Applied Mechanics and Materials, vol. 110, pp. 4109-4117, 2012. [8]M. Németh y G. Michaľčonok, «Preparation and cluster analysis of data from the industrial production process for failure prediction,» Research Papers Faculty of Materials Science and Technology Slovak University of Technology, vol. 24, nº 39, pp. 111-116, 2016. [9]S. Ballı, «A data mining approach to the diagnosis of failure modes for two serial fastened sandwich composite plates,» Journal of Composite Materials, vol. 51, nº 20, pp. 2853-2862, 2017. [10]S. Dindarloo y E. Siami-Irdemoosa, «Data mining in mining engineering: results of classification and clustering of shovels failures data,» International Journal of Mining, Reclamation and Environment, vol. 31, nº 2, pp. 105-118, 2017. [11]E. e Oliveira, V. Miguéis, L. Guimarães y J. L. Borges, «Power Transformer Failure Prediction: Classification in Imbalanced Time Series,» U. Porto Journal of Engineering, vol. 3, nº 2, pp. 34-48, 2017. [12]A. Taghizadeh y N. Demirel, «Application of Machine Learning for Dragline Failure Prediction,» E3S Web of Conferences, vol. 15, p. 03002, 2017. [13]W. Chang, Z. Xu, M. You, S. Zhou, Y. Xiao y Y. Cheng, «A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering,» Entropy, vol. 20, nº 12, p. 923, 2018. [14]K. Halteh, K. Kumar y A. Gepp, «Financial distress prediction of Islamic banks using tree-based stochastic techniques,» Managerial Finance, vol. 44, nº 6, pp. 759-773, 2018. [15]C.-H. Liu, C.-J. Lin, Y.-H. Hu y Z.-H. You, «Predicting the failure of dental implants using supervised learning techniques,» Applied Sciences, vol. 8, nº 5, p. 698, 2018. [16]B. Mohammed, I. Awan, H. Ugail y M. Younas, «Failure prediction using machine learning in a virtualised HPC system and application,» Cluster Computing, vol. 22, nº 2, pp. 471-485, 2019. [17]O. Sukhbaatar, T. Usagawa y L. Choimaa, «An artificial neural network based early prediction of failure-prone students in blended learning course,» International Journal of Emerging Technologies in Learning (iJET)}, vol. 14, nº 19, pp. 77-92, 2019. [18]Z. Wang, W. Zhao y X. Hu, «Analysis of prediction model of failure depth of mine floor based on fuzzy neural network,» Geotechnical and Geological Engineering, vol. 37, nº 1, pp. 71-76, 2019. [19]V. S. Gujre y R. Anand, «Machine learning algorithms for failure prediction and yield improvement during electric resistance welded tube manufacturing,» Journal of Experimental \& Theoretical Artificial Intelligence, vol. 32, nº 4, pp. 601-622, 2020. [20]P. du Jardin, «Forecasting corporate failure using ensemble of self-organizing neural networks,» European Journal of Operational Research, vol. 288, nº 3, pp. 869-885, 2021. [21]R. Brachman y T. Anand, «The process of knowledge discovery in databases,» Advances in knowledge discovery and data mining, pp. 37-57, 1996. [22]W. Frawley, G. Piatetsky-Shapiro y C. Matheus, «Knowledge discovery in databases: An overview,» AI magazine, vol. 13, nº 3, p. 57, 1992. [23]F. H. Troncoso Espinosa y J. V. Ruiz Tapia, «Predicción de fuga de clientes en una empresa de distribución de gas natural mediante el uso de minería de datos,» Universidad Ciencia y Tecnología, vol. 24, nº 106, pp. 79-87, 2020. [24]F. H. Troncoso, «Prediction of Recidivism in Thefts and Burglaries Using Machine Learning,» Indian Journal of Science and Technology, vol. 13, nº 6, pp. 696-711, March 2020. [25]M. Kantardzic, Data mining: concepts, models, methods, and algorithms, John Wiley & Sons, 2011. [26]F. H. Troncoso Espinosa, P. G. Fuentes Figueroa y I. R. Belmar Arriagada, «Predicción de fraudes en el consumo de agua potable mediante el uso de Minería de Datos,» Universidad Ciencia y Tecnología, vol. 24, nº 104, pp. 58-66, 2020. [27]C. Romero y S. Ventura, «Data mining in education,» Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 3, nº 1, pp. 12-27, 2013. [28]D. Larose y C. Larose, Discovering knowledge in data: an introduction to data mining, John Wiley & Sons, 2014.
Cellulose is the main raw material for the production of paper. Companies that produce it present in their production line the cutting of the cellulose sheet. This failure is sporadic and has a high economic impact since it paralyzes the production line for several hours, incurring unproductive hours and a large deployment of human and financial resources. In this research, the use of Data Mining is proposed to define a machine learning algorithm that allows predicting the cutting of the cellulose sheet in a production line of a cellulose plant in Chile. The results show that by applying this technique it is possible to predict the cutting of the cellulose sheet well in advance to take corrective actions to avoid cutting and thus minimize the economic impact associated with the failure. Keywords: Data Mining, machine learning, cellulose, productivity. References [1]B. Ranaganth y G. Viswanath, «Application of artificial neural network for optimizing cutting variables in laser cutting of 304 grade stainless steel,» International Journal of Applied Engineering and Technology, vol. 1, nº 1, pp. 106-112, 2011. [2]M. Durica, J. Frnda y L. Svabova, «Decision tree based model of business failure prediction for Polish companies,» Oeconomia Copernicana, vol. 10, nº 3, pp. 453-469, 2019. [3]G. Köksal, İ. Batmaz y M. C. Testik, «A review of data mining applications for quality improvement in manufacturing industry,» Expert systems with Applications, vol. 38, nº 10, pp. 13448-13467, 2011. [4]H. Poblete y R. Vargas, «Relacion entre densidad y propiedades de tableros HDF producidos por un proceso seco,» Maderas. Ciencia y tecnología, vol. 8, nº 3, pp. 169-182, 2006. [5]B. Kovalerchuk y E. Vityaev, «Data mining for financial applications,» Data Mining and Knowledge Discovery Handbook, pp. 1203-1224, 2005. [6]U. Fayyad, G. Piatetsky-Shapiro, P. Smyth y R. Uthurusamy, «Advances in knowledge discovery and data mining,» American Association for Artificial Intelligence, 1996. [7]A. K. Pandey y A. K. Dubey, «Neuro fuzzy modeling of laser beam cutting process,» Applied Mechanics and Materials, vol. 110, pp. 4109-4117, 2012. [8]M. Németh y G. Michaľčonok, «Preparation and cluster analysis of data from the industrial production process for failure prediction,» Research Papers Faculty of Materials Science and Technology Slovak University of Technology, vol. 24, nº 39, pp. 111-116, 2016. [9]S. Ballı, «A data mining approach to the diagnosis of failure modes for two serial fastened sandwich composite plates,» Journal of Composite Materials, vol. 51, nº 20, pp. 2853-2862, 2017. [10]S. Dindarloo y E. Siami-Irdemoosa, «Data mining in mining engineering: results of classification and clustering of shovels failures data,» International Journal of Mining, Reclamation and Environment, vol. 31, nº 2, pp. 105-118, 2017. [11]E. e Oliveira, V. Miguéis, L. Guimarães y J. L. Borges, «Power Transformer Failure Prediction: Classification in Imbalanced Time Series,» U. Porto Journal of Engineering, vol. 3, nº 2, pp. 34-48, 2017. [12]A. Taghizadeh y N. Demirel, «Application of Machine Learning for Dragline Failure Prediction,» E3S Web of Conferences, vol. 15, p. 03002, 2017. [13]W. Chang, Z. Xu, M. You, S. Zhou, Y. Xiao y Y. Cheng, «A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering,» Entropy, vol. 20, nº 12, p. 923, 2018. [14]K. Halteh, K. Kumar y A. Gepp, «Financial distress prediction of Islamic banks using tree-based stochastic techniques,» Managerial Finance, vol. 44, nº 6, pp. 759-773, 2018. [15]C.-H. Liu, C.-J. Lin, Y.-H. Hu y Z.-H. You, «Predicting the failure of dental implants using supervised learning techniques,» Applied Sciences, vol. 8, nº 5, p. 698, 2018. [16]B. Mohammed, I. Awan, H. Ugail y M. Younas, «Failure prediction using machine learning in a virtualised HPC system and application,» Cluster Computing, vol. 22, nº 2, pp. 471-485, 2019. [17]O. Sukhbaatar, T. Usagawa y L. Choimaa, «An artificial neural network based early prediction of failure-prone students in blended learning course,» International Journal of Emerging Technologies in Learning (iJET)}, vol. 14, nº 19, pp. 77-92, 2019. [18]Z. Wang, W. Zhao y X. Hu, «Analysis of prediction model of failure depth of mine floor based on fuzzy neural network,» Geotechnical and Geological Engineering, vol. 37, nº 1, pp. 71-76, 2019. [19]V. S. Gujre y R. Anand, «Machine learning algorithms for failure prediction and yield improvement during electric resistance welded tube manufacturing,» Journal of Experimental \& Theoretical Artificial Intelligence, vol. 32, nº 4, pp. 601-622, 2020. [20]P. du Jardin, «Forecasting corporate failure using ensemble of self-organizing neural networks,» European Journal of Operational Research, vol. 288, nº 3, pp. 869-885, 2021. [21]R. Brachman y T. Anand, «The process of knowledge discovery in databases,» Advances in knowledge discovery and data mining, pp. 37-57, 1996. [22]W. Frawley, G. Piatetsky-Shapiro y C. Matheus, «Knowledge discovery in databases: An overview,» AI magazine, vol. 13, nº 3, p. 57, 1992. [23]F. H. Troncoso Espinosa y J. V. Ruiz Tapia, «Predicción de fuga de clientes en una empresa de distribución de gas natural mediante el uso de minería de datos,» Universidad Ciencia y Tecnología, vol. 24, nº 106, pp. 79-87, 2020. [24]F. H. Troncoso, «Prediction of Recidivism in Thefts and Burglaries Using Machine Learning,» Indian Journal of Science and Technology, vol. 13, nº 6, pp. 696-711, March 2020. [25]M. Kantardzic, Data mining: concepts, models, methods, and algorithms, John Wiley & Sons, 2011. [26]F. H. Troncoso Espinosa, P. G. Fuentes Figueroa y I. R. Belmar Arriagada, «Predicción de fraudes en el consumo de agua potable mediante el uso de Minería de Datos,» Universidad Ciencia y Tecnología, vol. 24, nº 104, pp. 58-66, 2020. [27]C. Romero y S. Ventura, «Data mining in education,» Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 3, nº 1, pp. 12-27, 2013. [28]D. Larose y C. Larose, Discovering knowledge in data: an introduction to data mining, John Wiley & Sons, 2014.
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