Previsión de demanda intermitente con métodos de series de tiempo y redes neuronales artificiales: Estudio de caso

Publicado

2017-10-01

Previsión de demanda intermitente con métodos de series de tiempo y redes neuronales artificiales: Estudio de caso

Intermittent demand forecasting with time series methods and artificial neural networks: A case study

Palabras clave:

previsión de la demanda, demanda intermitente, redes neuronales artificiales (es)
demand forecasting, intermittent demand, artificial neural networks (en)

Descargas

Autores/as

Adolfo Rene Santa Cruz Rodriguez Universidade do Estado de Santa Catarina https://orcid.org/0000-0003-4175-7299
Camila Corrêa Universidad del Estado de Santa Catarina https://orcid.org/0000-0001-6339-8245

Resumen (es)
Resumen (en)

Este artículo tiene como objetivo estudiar la previsión de la demanda intermitente de un tipo específico de pieza de reposición en una industria brasilera de sistemas de refrigeración que comercializa sus productos en el mercado latinoamericano. La demanda es caracterizada en términos de intermitencia y variabilidad. Los resultados son calculados usando métodos clásicos de previsión intermitente fuera de la muestra: Croston, Aproximación Syntetos-Boylan (SBA), Corrección Shale-Boylan-Johnston (SBJ), Algoritmo de Previsión de Agregación Múltiple (MAPA) y modelos basados en Redes Neuronales Artificiales (RNA). El Error Cuadrático Medio (RMSE) y Desvío Medio Absoluto (MAE) son utilizados para efectos de comparación y selección del modelo de previsión. El análisis comparativo de los resultados muestra que las previsiones basadas en modelos RNA simple de tres capas y entrenadas con el algoritmo Resilient Backpropagation presentan mejor desempeño. Los cálculos fueron realizados con el software R, RStudio, bibliotecas “forecast”, “tsintermittent” y “neuralnet”.

This article aims to study the intermittent demand forecasting for a specific type of spare part of a Brazilian refrigeration industry that commercialize its products in the Latin American market. Demand characterization is performed in terms of their intermittency and variability. Results are obtained with classical intermittent forecasting methods outside the sample: Croston, Syntetos-Boylan Approximation (SBA), Shale-Boylan-Johnston Correction (SBJ), Multiple Aggregation Prediction Algorithm (MAPA) and with Artificial Neural Networks (ANN) based models. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are used for comparison and selection of forecast model. The comparative analysis results shows that the predictions based on a simple three-layer ANN model trained with the Resilient Backpropagation algorithm present better performance. The calculations were performed using R software with RStudio, "forecast", "tsintermittent" and "neuralnet" libraries.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Kumar, P., Herbert, M. and Rao, S., Artificial Neural Network approach to industrial demand forecast. Proceeding of 23rd The IIER International Conference Singapure, 25 th April. ISBN: 978-93-82702-99-3. 2015.

Sahin, M., Kizilaslan, R. and Demirel, O.F., Forecasting aviation spare parts demand using croston based methods and artificial neural networks. Journal of Economic and Social Research, 15(2), pp. 1-21, 2013. ISSN: 13021060.

Willemain, T.R., Smart, C.N., Shockor, J.H. and DeSautels, P.A., Forecasting intermittent demand in manufacturing: A comparative evaluation of Croston’s method. International Journal of Forecasting, 10(4), pp. 529-538, 1994. DOI: 10.1016/0169-2070(94)90021-3.

Ghobbar, A. and Friend, H.C., Sources of intermittent demand for aircraft spare parts within airline operations. Journal of Air Transport Management, 8(4), pp. 221-231, 2002. DOI: 10.1016/S0969-6997(01)00054-0.

Babiloni, E., Cardós, M., Albarracín, J.M. and Palmer, M.E., Demand categorisarion, forecasting, and inventory control for intermittent demand items. South African Journal of Industrial Engineering, 21(2), pp. 115-130, 2010. DOI: 10.1128/JCM.00418-10.

Billah, B., King, M.L., Snyder, R.D. and Koehler, A.B., Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22, pp. 239-247, 2006. DOI: 10.1016/j.ijforecast. 2005.08.002.

Syntetos, A.A. and Boylan, J.E., On the bias of information estimates. International Journal of Production Economics, 71(2), pp. 457-466, 2001. DOI: 10.1037/h0026857.

Croston, J.D., Forecasting and stock control for intermittent demands. Journal of Operational Research Society, 23(3), pp. 289-303, 1972. DOI: 10.1057/jors.1972.50.

Xu, Q., Wang, N. and Shi, H., A review of Croston’s method for intermittent demand forecasting. Proceedings 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery FSKD, pp. 1456-1460, 2012. DOI: 10.1109/FSKD.2012.6234258.

Syntetos, A.A. and Boylan, J.E., The accuracy of intermittent demand estimates. Internatoinal Journal of Forecasting, 21(2), pp. 303-314, 2005. DOI: 10.1016/j.ijpe.2010.07.005.

Teunter, R.H., Syntetos, A.A. and Babai, M.Z., Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Reseasrch, 214(3), pp. 606-615, 2011. DOI: 10.1016/j.ejor.2011.05.018.

Kourentzes, N., On intermittent demand model optimisation and selection. International Journal of Production Economics, 156, pp. 180-190, 2014. DOI: 10.1016/j.ijpe.2014.06.007.

Shale, E.A., Boylan, J.E. and Johnston, F.R., Forecasting for intermittent demand: the estimation of an unbiased average. Journal of the Operational Research Society, 57(5), pp.588-592, 2006. DOI: 10.1057/palgrave.jors.2602031

Babai, M.Z., Syntetos, A. and Teunter, R., Intermittent demand forecasting: An empirical study on accuracy and the risk of obsolescence. International Journal of Production Economics, 157(1), pp. 212-219, 2014. DOI: 10.1016/j.ijpe.2014.08.019.

Nikolopoulos, K., Syntetos, A., Boylan, J., Petropoulos, F. and Assimakopoulos, V., An Aggregate-Disaggregate Intermittent Demand Approach (ADIDA) to forecasting: An empirical proposition and analysis. Journal of the Operational Research Society, 62(3), pp. 544-554, 2011. DOI: 10.1057/jors.2010.32.

Basheer, I.A. and Hajmeer, M., Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43, pp. 3-31, 2000. DOI: 10.1016/S0167-7012(00)00201-3.

Warner, B. and Misra M., Understanding neural networks as statistical tools. The American Statisticians, [online]. 50(4), pp. 284-293, 1996. [date of reference, August 31th of 2017]. Available at http://www.jstor.org/stable/2684922.

Khashei, M. and Bijari, M., An artificial neural network (p,d,q) model for timeseries forecasting. Expert Systems with Applications, 37, pp. 479-489, 2010. DOI: 10.1016/j.eswa.2009.05.044.

Hornik, K., Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2), pp. 251-257, 1991. DOI: http://doi.org/10,1016/0893-6080(91)90009-T

Guang-Bin H., Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Transactions on Neural Networks, 14(2), pp.274-281. 2003. DOI: 10.1109/TNN.2003. 809401.

Peralta, J., Li, X., Gutierrez, G. and Sanchis de Miguel, A., Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm. Neural Comput. & Applic., 22(1), pp. 11-20, 2013. DOI: 10.1007/s00521-011-0741-0.

Mitrea, C.A., Lee, C.K. M. and Wu, Z., A comparison between neural networks and traditional forecasting methods: A case study. International Journal of Engineering Business Management, 1, pp. 19-24, 2009. DOI: 10.5772/6777.

De Gooijer, J. and Hyndman, R.J., 25 years of time series forecasting. International Journal of Forecasting, 22(3), pp. 443-473, 2006. DOI: 10.1016/j.ijforecast.2006.01.001.

Yadav, R.N., Kalra, P.K. and John, J., Time series prediction with single multiplicative neuron model. Applied Soft Computing Journal, 7, pp. 1157-1163, 2007. DOI: 10.1016/j.asoc.2006.01.003.

Zhang, G.P., Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, pp.159-175, 2003. DOI: 10.1016/S0925-2312(01)00702-0.

Wang, W., A neural network model on the forecasting of inventory risk management of spare parts. 2012 International Conference on Information, Technology and Management Science (Icitms 2012), [online]. pp. 649-660, 2012. [date of reference: September 15th of 2016]. Available at: http://link.springer.com/10.1007/978-3-642-34910-2_34nhttp://link.springer.com/10.1007/978-3-642-34910-2.

Nasiri-Pour, A., Rostami-Tabar, B. and Rahimzadeh, A., A hybrid neural network and traditional approach for forecasting lumpy demand. World Academy of Science. Engineering and Technology, [online]. 40(4), pp. 384-390, 2009. [date of reference: September 20th of 2016]. Available at: http://www.scopus.com/inward/ record.url?eid=2-s2.0-78651581595&partnerID=tZOtx3y1

Yolcu, U., Egrioglu, E. and Aladag, C.H., A new linear & nonlinear artificial neural network model for time series forecasting. Decis Support Systems, 54(3), pp. 1340-1347, 2013. DOI: 10.1016/j.dss.2012.12.006.

Hakan-Aladag, C., Yolcu, U. and Egrioglu, E., A new multiplicative seasonal neural network model based on particle swarm optimization. Neural Process Lett, 37, pp. 251-262, 2013. DOI: 10.1007/s11063-012-9244-y.

Gheyas, I.A. and Smith, L.S., A neural network approach to time series forecasting. Engineering II(1), [online]. pp. 1-5, 2009. [date of reference, september 22th of 2016]. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0196890407003032.

Zhang, G. and Berardi, V., Time series forecasting with neural network ensembles: An application for exchange rate prediction. Journal of the Operational Researche Society, 52, pp. 652-664, 2001. DOI: 10.1057/palgrave.jors.2601133.

Gutierrez, R.S., Solis, A.O. and Mukhopadhyay, S., Lumpy demand forecasting using neural networks. Int. J. Production Economics, 111(2), pp. 409-420, 2008. DOI: 10.1016/j.ijpe.2007.01.007

Sipper, D. y Bulfin, R., Planeación y control de la producción. Mexico: McGrawHill, 1998.

Igel, C. and Husken, M., Empirical evaluation of the improver Rprop learning algorithms. Neurocomputing, 50, pp. 105-123, 2003. DOI: DOI: 10.1016/S0925-2312(01)00700-7.

Licencia

Derechos de autor 2017 DYNA

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.

El autor o autores de un artículo aceptado para publicación en cualquiera de las revistas editadas por la facultad de Minas cederán la totalidad de los derechos patrimoniales a la Universidad Nacional de Colombia de manera gratuita, dentro de los cuáles se incluyen: el derecho a editar, publicar, reproducir y distribuir tanto en medios impresos como digitales, además de incluir en artículo en índices internacionales y/o bases de datos, de igual manera, se faculta a la editorial para utilizar las imágenes, tablas y/o cualquier material gráfico presentado en el artículo para el diseño de carátulas o posters de la misma revista.