Advertisement

Demand Forecasting Method Using Artificial Neural Networks

  • Amelec ViloriaEmail author
  • Luisa Fernanda Arrieta Matos
  • Mercedes Gaitán
  • Hugo Hernández Palma
  • Yasmin Flórez Guzmán
  • Luis Cabas Vásquez
  • Carlos Vargas Mercado
  • Omar Bonerge Pineda Lezama
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1123)

Abstract

Based on a forecast, the decision maker can determine the capacity required to meet a certain forecast demand, as well as carry out in advance the balance of capacities in order to avoid underusing or bottlenecks. This article proposes a procedure for forecasting demand through Artificial Neural Networks. In order to carry out the validation, the procedure proposed was applied in a Soda Trading and Distribution Company where three types of products were selected.

Keywords

Forecast Artificial Neural Networks Big Data Demand 

References

  1. 1.
    Acosta, M.C., Villarreal, M.G., Cabrera, M.: Estudio de validación de un método para seleccionar técnicas de pronóstico de series de tiempo mediante redes neuronales artificiales. Ingeniería Investigación y Tecnología, XIV(1), 53–63, ISSN: 1405-7743 (2013), Descargado de. http://www.sciencedirect.com/science/article/pii/S140577431372225X
  2. 2.
    Fernández Enríquez, F., de la Fé Dotres, S., Miraglia Ubals, D.: Pronóstico de las pérdidas en redes de distribución mediante redes neuronales. Energética, XXVI(1), 17–21 (2005). Descargado de. http://rie.cujae.edu.cu/index.php/RIE/article/download/142/141
  3. 3.
    Lizarazo, J.M., Gómez, J.G.: Desarrollo de un modelo de redes neuronales artificiales para predecir la resistencia a la compresión y la resistividad eléctrica del concreto. Ingeniería e Investigación 27(1), 8 (2007)Google Scholar
  4. 4.
    Valarie Zeithaml, A., Parasuraman, A., Berry, L.L.: Total quality management services. Diaz de Santos, Bogota (1993)Google Scholar
  5. 5.
    Carman, J.M.: Consumer perceptions of service quality: an assessment of the SERVQUAL dimensions. J. Retail. 69(Spring), 127–139 (1990)Google Scholar
  6. 6.
    Larrea, P.: Quality of Service, the Marketing Strategy. Dfaz Santos, Madrid (1991)Google Scholar
  7. 7.
    Hair Jr., J.F., Anderson, R.E., Tatham, R.L., Black, W.C.: Multivariate Analysis, 5th edn. Prentice Hall, Iberia (1999)Google Scholar
  8. 8.
    Tsiniduo, M., et al.: Evaluation of the factors that determine quality in higher education: an empirical study. Qual. Assur. Educ. 18(8), 227–244 (2010)CrossRefGoogle Scholar
  9. 9.
    Gonzalez Espinoza, O.: Quality of higher education: concepts and models. Cal. Sup. Educ. 28, 249–296 (2008)Google Scholar
  10. 10.
    Bonerge Pineda Lezama, O., Varela Izquierdo, N., Pérez Fernández, D., Gómez Dorta, R.L., Viloria A., Romero Marín L. (2018) Models of multivariate regression for labor accidents in different production sectors: comparative study. In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, ChamGoogle Scholar
  11. 11.
    Izquierdo, N.V., et al.: Fuzzy logic applied to the performance evaluation. honduran coffee sector case. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10942, pp. 164–173. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-93818-9_16CrossRefGoogle Scholar
  12. 12.
    Pineda Lezama, O., Gómez Dorta, R.: Techniques of multivariate statistical analysis: an application for the Honduran banking sector. Innovare: J. Sci. Technol. 5(2), 61–75 (2017)Google Scholar
  13. 13.
    Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching - learning process through knowledge data discovery (Big Data). In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. Lecture Notes in Computer Science, vol. 10943. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-93803-5_63CrossRefGoogle Scholar
  14. 14.
    Chase, R.B., et al.: Administración de operaciones: producción y cadena de suministros. McGraw-Hill/Interamericana Editores, Bogota (2009)Google Scholar
  15. 15.
    Chen, T.-L., Su, C.-H., Cheng, C.-H., Chiang, H.-H.: A novel price-pattern detection method based on time series to forecast stock market. Afr. J. Bus. Manage. 5(13), 5188 (2011)Google Scholar
  16. 16.
    Conejo, A.J., Contreras, J., Espinola, R., Plazas, M.A.: Forecasting electricity prices for a day-ahead pool-based electric energy market. Int. J. Forecast. 21(3), 435–462 (2005)CrossRefGoogle Scholar
  17. 17.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995).  https://doi.org/10.1023/A:1022627411411CrossRefzbMATHGoogle Scholar
  18. 18.
    Du, X.F., Leung, S.C.H., Zhang, J.L., Lai, K.K.: Demand forecasting of perishable farm products using support vector machine. Int. J. Syst. Sci. 44(3), 556–567 (2011)CrossRefGoogle Scholar
  19. 19.
    Matich, D.J.: Redes Neuronales: Conceptos básicos y aplicaciones. In: Cátedra de Informática Aplicada a la Ingeniería de Procesos–Orientación I (2001)Google Scholar
  20. 20.
    Mercado, D., Pedraza, L., Martínez, E.: Comparación de Redes Neuronales aplicadas a la predicción de Series de Tiempo. Prospectiva 13(2), 88–95 (2015)CrossRefGoogle Scholar
  21. 21.
    Nayak, S.C., Misra, B.B., Behera, H.S.: Impact of data normalization on stock index forecasting. Int. J. Comp. Inf. Syst. Ind. Manage. Appl. 6, 357–369 (2014)Google Scholar
  22. 22.
    Obando, J.R.: Elementos de Microeconomía. EUNED (2000)Google Scholar
  23. 23.
    Ruan, D.: Fuzzy Systems and Soft Computing in Nuclear Engineering. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-7908-1866-6CrossRefGoogle Scholar
  24. 24.
    Sanclemente, J.C.: Las ventas y el mercadeo, actividades indisociables y de gran impacto social y económico: El aporte de Tosdal, Innovar, vol. 17, no. 30, pp. 160–162, July 2007Google Scholar
  25. 25.
    Sapankevych, N., Sankar, R.: Time series prediction using support vector machines: a survey. IEEE Comput. Intell. Mag. 4(2), 24–38 (2009)CrossRefGoogle Scholar
  26. 26.
    Swanson, N., White, H.: Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models. Int. J. Forecast. 13(4), 439–461 (1997)CrossRefGoogle Scholar
  27. 27.
    Toro, E.M., Mejia, D.A., Salazar, H.: Pronóstico de ventas usando redes neuronales. Scientia et technica 10(26), 25–30 (2004)Google Scholar
  28. 28.
    Villada, F., Muñoz, N., García, E.: Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores. Información tecnológica 23(4), 11–20 (2012)CrossRefGoogle Scholar
  29. 29.
    Vitez, O.: Cuáles se consideran los principales indicadores económicos. [En línea] (2017), Disponible en. https://pyme.lavoztx.com/cules-se-consideran-los-principales-indicadores-econmicos-9641.html. [Consultado: 07-dic-2017]
  30. 30.
    Wen, Q., Mu, W., Sun, L., Hua, S., Zhou, Z.: Daily sales forecasting for grapes by support vector machine. In: International Conference on Computer and Computing Technologies in Agriculture, pp. 351–360 (2013)Google Scholar
  31. 31.
    Wu, Q., Yan, H.S., Yang, H.B.: A forecasting model based support vector machine and particle swarm optimization. In: 2008 Workshop on Power Electronics and Intelligent Transportation System, pp. 218–222 (2008)Google Scholar
  32. 32.
    Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50(Supplement C), 159–175 (2003)CrossRefGoogle Scholar
  33. 33.
    Departamento Administrativo Nacional de Estadística -DANE-. Importaciones colombianas (2019). https://www.dane.gov.co/index.php/comercio-exterior/importaciones
  34. 34.
    Jain, M., Verma, C.: Adapting k-means for clustering in big data. Int. J. Comput. Appl. 101(1), 19–24 (2014)Google Scholar
  35. 35.
    Comisión Económica para América Latina y el Caribe -CEPAL-. Visión agrícola del TLC entre Colombia y Estados Unidos: preparación, negociación, implementación y aprovechamiento. Serie Estudios y Perspectivas, vol. 25, p. 87 (2013)Google Scholar
  36. 36.
    Henao-Rodríguez, C., Lis-Gutiérrez, J.-P., Gaitán-Angulo, M., Malagón, L.E., Viloria, A.: Econometric analysis of the industrial growth determinants in Colombia. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds.) ADC 2018. LNCS, vol. 10837, pp. 316–321. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-92013-9_26CrossRefGoogle Scholar
  37. 37.
    Lis-Gutiérrez, J.-P., Gaitán-Angulo, M., Henao, L.C., Viloria, A., Aguilera-Hernández, D., Portillo-Medina, R.: Measures of concentration and stability: two pedagogical tools for industrial organization courses. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10942, pp. 471–480. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-93818-9_45CrossRefGoogle Scholar
  38. 38.
    Viloria, A.: Commercial strategies providers pharmaceutical chains for logistics cost reduction. Indian J. Sci. Technol. 8(1), Q16 (2016)Google Scholar
  39. 39.
    Viloria, A., Gaitan-Angulo, M.: Statistical adjustment module advanced optimizer planner and SAP generated the case of a food production company. Indian J. Sci. Technol. 9(47) (2016).  https://doi.org/10.17485/ijst/2016/v9i47/107371

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Amelec Viloria
    • 1
    Email author
  • Luisa Fernanda Arrieta Matos
    • 2
  • Mercedes Gaitán
    • 3
  • Hugo Hernández Palma
    • 4
  • Yasmin Flórez Guzmán
    • 5
  • Luis Cabas Vásquez
    • 4
  • Carlos Vargas Mercado
    • 4
  • Omar Bonerge Pineda Lezama
    • 6
  1. 1.Universidad de La CostaBarranquillaColombia
  2. 2.Universidad Simón BolívarBarranquillaColombia
  3. 3.Corporación Universitaria Empresarial de Salamanca – CUESBarranquillaColombia
  4. 4.Corporación Universitaria LatinoamericanaBarranquillaColombia
  5. 5.Corporación Universitaria Minuto de Dios – UNIMINUTOBarranquillaColombia
  6. 6.Universidad Tecnológica Centroamericana (UNITEC)San Pedro SulaHonduras

Personalised recommendations