Importance of Forecasts in decision making in MSMEs

10.22579/23463910.17
Published 2018-01-01
content - research articles

Abstract

The study of forecasts as elements in decision making in companies have greater areas of opportunity within organizations, this is because the forecasts help decision makers to make more precise judgments about future events and In this part, mathematics turns out to have an important role, as mentioned (Wilson & Koerber, 1992) it has been shown that quantitative methods are useful to make better predictions about the future course of events, so it has been created the need for new software to be developed through the use of the computer these predictions are generated more quickly, but it is fundamental to understand well how the calculations are made manually to later use these computational tools.

For the purposes of knowledge that the administrator requires to know about the different methods of forecasts and thus select the one that complies with the internal objectives, the author Farrera Gutiérrez (2012) divides them into: Qualitative Methods among which are: Delphi Methods, Historical Analogy and Market Research. Within the Quantitative Methods are: Moving Average, Exponential Smoothing and Linear Regression. It is important to note that knowing the different forecasting methods allows the decision maker in front of a MSME, to get as accurate as possible about the behavior of the data on which their future estimates are generated.

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References

Charles, G. (2003). Métoos Cuantitativos Para La Toma de Decisiones en Administración.México: McGraw Hill.

Farrera Gutierrez, A. (2012). Manual de Pronósticos Para La Toma de Decisiones.México: Digital Tecnológico de Monterrey.

Fernanda, V. (Septiembre de 2016). Universidad Nacional del Sur Argentina.Recuperado el Mayo de 2018, de http://www.matematica.uns.edu.ar/uma2016/material/Introduccion_a_los_Modelos_de_Pronosticos.pdf

J, L. (2002). El Método Delphi. Una Técnica de Previsión del Futuro.España: Ariel.

Mendoza Ramírez, M. (2010). Pronósticos y Estadísticas Para la Administración.México: ITAM.

Nuño de León, P. d. (25 de abril de 2018). Bibliotecas Digitales.Obtenido de http://www.aliat.org.mx/BibliotecasDigitales/Hospitalidad/Administracion_de_pequenas_empresas.pdf

Wilson, J., & Koerber, D. (1992). Combining Subjective and Objective Forescasts Improves Results. Journal of Business Forecasting , 11(3), 12-16.

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