Forecast Based on an Empirical Probability Function with an Adjusted Error Using Propagation of Error
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.
[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.
[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.
@article{"International Journal of Business, Human and Social Sciences:70213", author = "Oscar Javier Herrera and Manuel Ángel Camacho", title = "Forecast Based on an Empirical Probability Function with an Adjusted Error Using Propagation of Error", 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.", volume = "9", number = "6", pages = "1935-5", }