Abstract
A proper product demand projection is an aspect that can be decisive for the competitiveness and survival of companies. However, in most of cases, this process is carried out based on empirical knowledge of the marketing personnel generating a high level of error in the results. To solve this problem, this paper presents a production planning architecture based on demand analysis by using business intelligence architecture and analytical algorithms. The proposed architecture has been validated by means of a case study which results indicate that the effectiveness increases from 25% to more than 85%. We believe that the proposed model may be applicable in other entities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
SIAG Consulting: Qué es exactamente el famoso cuadrante mágico de Gartner? Solo pienso en TIC - Consultoría tecnológica, 07 November 2016. http://www.solopiensoentic.com/cuadrante-magico-de-gartner/. Accessed 18 Mar 2018
Piatetsky, G.: Gainers and losers in gartner 2018 magic quadrant for data science and machine learning platforms, Feburary 2018. https://www.kdnuggets.com/2018/02/gartner-2018-mq-data-science-machine-learning-changes.html. Accessed 18 Mar 2018
Piatetsky-Shapiro, G.: Acerca de KDnuggets, analytics, big data, data mining y data science leader (2018). https://www.kdnuggets.com/about/index.html. Accessed 20 Mar 2018
Gregory Piatetsky, Kd.: Algoritmos principales y métodos utilizados por los científicos de datos, September (2016). https://www.kdnuggets.com/2016/09/poll-algorithms-used-data-scientists.html?lipi=urn%3Ali%3Apage%3Ad_flagship3_pulse_read%3Bhrjw5C3BQvOXA9P0xAjKdw%3D%3D. Accessed 20 Mar 2018
Gartner: Magic quadrant for data science and machine-learning platforms, Feburary 2018. https://www.gartner.com/doc/3860063/magic-quadrant-data-science-machinelearning. Accessed 18 Mar 2018
Wu, L., Yan, J.Y., Fan, Y.J.: Data mining algorithms and statistical analysis for sales data forecast. In: 2012 Fifth International Joint Conference on Computational Sciences and Optimization, pp. 577–581 (2012)
Wu, X., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)
Tanwar, H., Kakkar, M.: Performance comparison and future estimation of time series data using predictive data mining techniques. In: 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), pp. 9–12 (2017)
Juang, W.-C., Huang, S.-J., Huang, F.-D., Cheng, P.-W., Wann, S.-R.: Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan. BMJ Open 7(11), e018628 (2017)
Kaur, P., Singh, M., Josan, G.S.: Classification and prediction based data mining algorithms to predict slow learners in education sector. Procedia Comput. Sci. 57, 500–508 (2015)
Saranya, S., Ayyappan, R., Kumar, N.: Student progress analysis and educational institutional growth prognosis using data mining. J. Eng. Sci. Res. Technol. 3, 1982–1987 (2014)
Alvarado, J., Jiménez, A.: La predicción del rendimiento académico: regresión lineal versus regresión logística. Univ. Complut. Madr. 12, 5 (2010)
Francis, H., Kusiak, A.: Prediction of engine demand with a data-driven approach. Procedia Comput. Sci. 103, 28–35 (2017)
Alon, I., Qi, M., Sadowski, R.J.: Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods. J. Retail. Consum. Serv. 8(3), 147–156 (2001)
Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y., Sun, X.: The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis. Support Syst. 50(3), 559–569 (2011)
Choudhury, A., Jones, J.: Crop yield prediction using time series models. J. Econ. Econ. Educ. Res. 15, 53 (2014)
Contreras Juárez, A., Atziry Zuñiga, C., Martínez Flores, J.L., Sánchez Partida, D.: Análisis de series de tiempo en el pronóstico de la demanda de almacenamiento de productos perecederos. Estud. Gerenciales 32(141), 387–396 (2016)
Quintana, M.J.G., Jiménez, S.A.M.: Modelos de series de tiempo aplicados a los expedientes de la Comisión de Derechos Humanos del Distrito Federal. Econ. Inf. 398, 89–99 (2016)
López, J.A., López, M.A.A.: Modelo de predicción electoral: el caso de la elección municipal 2015 de León de los Aldama, Guanajuato1. Estud. Políticos 35, 87–101 (2015)
Alteryx: About Alteryx | Alteryx (2018). https://www.alteryx.com/about-us. Accessed 25 Jan 2018
Gartner: Tableau lidera cuadrante mágico Gartner de BI 2018. Microsystem, 28 February 2018. https://www.microsystem.cl/tableau-lidera-gartner-bi-2018/. Accessed 11 Mar 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mazón Quinde, I.K., Guun Yoo, S., Arroyo, R., Raura, G. (2020). Architecture for Demand Prediction for Production Optimization: A Case Study. In: Botto-Tobar, M., León-Acurio, J., Díaz Cadena, A., Montiel Díaz, P. (eds) Advances in Emerging Trends and Technologies. ICAETT 2019. Advances in Intelligent Systems and Computing, vol 1066. Springer, Cham. https://doi.org/10.1007/978-3-030-32022-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-32022-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32021-8
Online ISBN: 978-3-030-32022-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)