Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30569
Forecast Based on an Empirical Probability Function with an Adjusted Error Using Propagation of Error

Authors: Oscar Javier Herrera, Manuel Ángel Camacho

Abstract:

This paper addresses a cutting edge method of business demand forecasting, based on an empirical probability function when the historical behavior of the data is random. Additionally, it presents error determination based on the numerical method technique ‘propagation of errors.’ The methodology was conducted characterization and process diagnostics demand planning as part of the production management, then new ways to predict its value through techniques of probability and to calculate their mistake investigated, it was tools used numerical methods. All this based on the behavior of the data. This analysis was determined considering the specific business circumstances of a company in the sector of communications, located in the city of Bogota, Colombia. In conclusion, using this application it was possible to obtain the adequate stock of the products required by the company to provide its services, helping the company reduce its service time, increase the client satisfaction rate, reduce stock which has not been in rotation for a long time, code its inventory, and plan reorder points for the replenishment of stock.

Keywords: Demand forecasting, empirical distribution, propagation of error

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1107181

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1382

References:


[1] J. M. Fernández Güell. El diseño de escenarios en el ámbito empresarial (Book style). Ediciones Pirámide, España. 2004
[2] J. W. Taylor, R. Buizza. Using weather ensemble predictions in electricity demand forecasting, International Journal of Forecasting, Volume 19, Issue 1, 2003, pp 57-70.
[3] J. Swain, L. Taylor. Numerical construction of likelihood distributions and the. Nuclear Instruments and Methods in Physics Research, 1998, pp 153-158
[4] L. S. Ensenada. “Importancia Estrategica del pronóstico de Demanda”. Mexico, BC. 2010
[5] D. Sipper, R. L. Bulfin. “Planeación y Control de la Producción”. México D.F.: McGraw-Hill, 1998
[6] S. G. Makridakis. “Pronósticos, Estrategia y Planificación para el siglo XXI”. Madrid (España): Ediciones Días de Santos, S.A. 1993
[7] O. J. Herrera. “Optimización de Sistemas Logísticos”. Memorias, Diplomado Gerencia en Logística (pp. 89-99). Neiva, Huila: Unidad de desarrollo Empresarial UDE. 2011
[8] S. Makridakis, S. C. Wheelwright. “Forecasting Methods for Management”. New York: John Wiley & Sons, fifth Edition. 1990
[9] E. Yacuzzi, G. Paggi. Diseño e implementación de un sistema de pronóstico de ventas en Whirlpool Argentina, Serie Documentos de Trabajo, Universidad del CEMA: Área: negocios, No. 209. 2002
[10] S. C. Chapra, P. Raymond. “Métodos Numéricos para Ingenieros”. México: Mc Graw-Hill. 1995
[11] N. Hurtado. “Métodos numéricos aplicados a la ingeniería”. México: Continental. 1997
[12] S. Murray, J. Schiller, R. A. Srinivasan. “Probabilidad y Estadística”. Mc Graw-Hill. 2011
[13] D. Peña Sánchez de Rivera. Fundamentos de Estadística (1ª edición). Alianza Editorial. 2008, pp. 688
[14] R. Delgado de la Torre. “Probabilidad y Estadística para Ciencias e Ingenierías”. Galicia: Delta publicaciones. 1ra Ed. 2008
[15] D. C. Montgomery, G. C. Runger. Probabilidad y estadística aplicadas a la ingeniería, Segunda edición. Limusa Wiley. 2002
[16] S. Waner, S. R. Costenoble, Finite Mathematics and Applied Calculus, February 2000. (ref. of June 7, 2012). Available on Web: http://www.zweigmedia.com/RealWorld/tutindex.html.